The Rapid Advancement Of Blockchain Technology Has Enabled Secure And Transparent Data Storage Across Decentralized Networks. However, Effective Data Sharing Between Heterogeneous Blockchains Remains A Major Challenge Due To Differences In Governance, Security Models, And Access Control Policies. Attribute-Based Encryption (ABE) Has Emerged As A Promising Cryptographic Solution For Fine-grained Access Control, Yet Most Existing Schemes Rely On A Single Trusted Authority, Limiting Scalability, Flexibility, And Resistance To Authority Compromise. To Address These Limitations, This Study Proposes A Multi-Authority Attribute-Based Encryption (MA-ABE) Scheme With Access Delegation Tailored For Cross-blockchain Data Sharing. In The Proposed Framework, Multiple Independent Authorities Collaboratively Manage User Attributes, Thereby Eliminating Single Points Of Failure And Enhancing Trust Distribution. Furthermore, An Access Delegation Mechanism Is Integrated To Allow Secure Transfer Of Access Rights Among Authorized Entities Without Exposing Secret Keys, Thereby Enabling Flexible And Efficient Data Sharing Across Blockchain Platforms. The Scheme Ensures Confidentiality, Fine-grained Access Control, And Interoperability While Reducing Computational Overhead Through Optimized Key Generation And Decryption Processes. Security Analysis Demonstrates That The Proposed Scheme Resists Collusion Attacks, Unauthorized Access, And Authority Compromise, While Performance Evaluation Indicates Its Practicality In Real-world Cross-blockchain Applications Such As Supply Chain Management, Healthcare, And Financial Data Sharing.
The Rapid Proliferation Of Data-driven Services Across Healthcare, Finance, Government, And Enterprise Domains Has Intensified The Need For Secure And Privacy-preserving Data Sharing Mechanisms. However, Cross-domain Data Exchange Is Often Hindered By Regulatory Restrictions, Heterogeneous Access Policies, And Concerns Over Data Misuse. Existing Solutions Either Rely On Centralized Intermediaries, Which Introduce Single Points Of Failure, Or Expose Sensitive Information During Interoperability Processes. To Address These Challenges, This Study Introduces Privacy Passport, A Novel Framework For Privacy-preserving Cross-domain Data Sharing. The Framework Integrates Attribute-based Encryption (ABE), Zero-knowledge Proofs (ZKP), And Blockchain-based Auditability To Ensure Fine-grained Access Control, Verifiable Trust, And Compliance With Diverse Data Governance Policies. Instead Of Transferring Raw Data, Privacy Passport Enables The Issuance Of Cryptographically Secure Access Tokens—"passports"—that Validate User Attributes And Access Rights Across Domains Without Revealing Underlying Sensitive Information. Experimental Evaluation Demonstrates That The System Achieves Scalable Performance, Reduces Communication Overhead, And Resists Unauthorized Access While Maintaining Interoperability Between Heterogeneous Domains. By Combining Cryptographic Assurance With Policy Enforcement, Privacy Passport Establishes A Foundation For Secure, Auditable, And Regulation-compliant Data Sharing In Multi-domain Environments.
The Increasing Interconnection Of Cyber-physical Systems (CPS), Industrial Control Networks, And Internet Of Things (IoT) Infrastructures Has Exposed Them To Sophisticated Cyber-attacks Targeting Both Data Integrity And System Stability. Traditional Security Mechanisms Often Adopt Uniform Protection Strategies Without Considering The Varying Criticality Of Data, Leaving High-value Information Particularly Vulnerable To Targeted Attacks. To Address This Challenge, This Study Investigates A Data-importance-aware Attack Strategy, Where Adversaries Selectively Manipulate Or Disrupt Data Based On Its Role In Decision-making And Control Processes. By Modeling The Attack Impact With Respect To Data Sensitivity, We Reveal How Resource-constrained Adversaries Can Maximize System Disruption With Minimal Effort. Building On These Insights, We Propose A Secure Control Countermeasure That Dynamically Prioritizes Defense Resources According To Data Importance, Ensuring Robust System Performance Under Adversarial Conditions. The Framework Integrates Importance-weighted Anomaly Detection, Resilient State Estimation, And Adaptive Control Reconfiguration To Mitigate The Impact Of Strategic Attacks. Experimental Evaluations On Simulated CPS And IoT Scenarios Demonstrate That The Proposed Approach Not Only Reduces Vulnerability To Data-targeted Attacks But Also Optimizes Security Resource Allocation. This Work Provides A Novel Perspective On Both Offensive And Defensive Strategies In Cyber-physical Security, Contributing To The Design Of Resilient, Importance-aware Secure Control Mechanisms.
With The Exponential Growth Of Digital Information, Cloud Storage Has Become An Indispensable Solution For Managing Large Volumes Of Data. However, Ensuring Data Integrity, Secure Retrieval, And Efficient Verification Remains A Critical Challenge, Particularly When The Stored Data Contains A High Degree Of Similarity Across Files. Traditional Provable Data Possession (PDP) Schemes Provide Integrity Verification But Lack Support For Fine-grained Retrieval And Face Significant Overhead When Dealing With Massive And Redundant Datasets. To Address These Limitations, This Study Proposes A Blockchain-Empowered Keyword Searchable Provable Data Possession (KS-PDP) Framework Designed Specifically For Large-scale Similar Data. The System Integrates Blockchain’s Immutable And Transparent Ledger To Ensure Tamper-proof Auditing, While Enabling Keyword-based Searchable Access To Outsourced Data Without Revealing Sensitive Information. A Similarity-aware Indexing Mechanism Is Incorporated To Minimize Redundant Verification Costs And Enhance Storage Efficiency. The Proposed Scheme Achieves Threefold Objectives: (i) Provable Data Integrity Through Decentralized Blockchain-based Auditing, (ii) Privacy-preserving Keyword Search For Flexible And Secure Data Retrieval, And (iii) Optimized Performance For Large, Highly Similar Datasets By Reducing Computational And Storage Overhead. Security And Performance Analysis Demonstrate That The Framework Resists Malicious Behaviors, Ensures Verifiable Search Results, And Achieves Significant Efficiency Gains Compared To Conventional PDP Methods. This Work Lays The Foundation For Trustworthy, Scalable, And Efficient Data Management In Cloud And Distributed Storage Environments.
Federated Learning (FL) Has Emerged As A Promising Paradigm For Collaborative Model Training Without Directly Sharing Raw Data, Thereby Preserving Privacy Across Distributed Parties. In Horizontal Federated Learning (HFL), Participants Hold Datasets With The Same Feature Space But Different Samples, Making Feature Selection Critical For Improving Learning Efficiency, Reducing Communication Costs, And Enhancing Model Generalization. However, Existing Feature Selection Approaches In FL Often Rely On Computationally Intensive Methods Or Expose Sensitive Intermediate Information During Aggregation, Creating Scalability And Security Challenges. To Address These Issues, This Study Proposes A Secure And Lightweight Feature Selection Framework Tailored For HFL. The Framework Integrates Privacy-preserving Techniques, Such As Homomorphic Encryption And Secure Aggregation, With Low-complexity Feature Evaluation Metrics To Enable Efficient Selection Of The Most Informative Features Without Compromising Data Confidentiality. The Proposed Approach Significantly Reduces Communication Overhead, Improves Training Convergence, And Ensures Robust Privacy Guarantees Against Inference Attacks. Experimental Evaluations On Benchmark Datasets Demonstrate That Our Method Achieves Comparable Or Superior Accuracy To State-of-the-art FL-based Feature Selection Schemes While Maintaining Lower Computational And Communication Costs. This Work Contributes To Building Practical, Privacy-preserving, And Resource-efficient HFL Systems For Real-world Applications In Healthcare, Finance, And IoT Ecosystems.
With The Rapid Growth Of Cloud-based Storage And Data Outsourcing, Ensuring Both Fine-grained Access Control And Efficient Search Functionality Over Encrypted Data Has Become A Critical Challenge. Traditional Searchable Encryption Schemes Support Keyword Queries But Often Fail To Provide Flexible Search Capabilities, Such As Pattern Or Wildcard Matching, Which Are Essential For Practical Applications Involving Incomplete Or Uncertain Query Terms. Moreover, Many Existing Solutions Either Neglect User Authentication Or Rely On Symmetric-key Settings, Which Hinder Scalability In Multi-user Environments. To Address These Issues, This Study Proposes PAEWS (Public-Key Authenticated Encryption With Wildcard Search), A Novel Cryptographic Framework That Enables Secure And Efficient Search Over Outsourced Encrypted Data While Ensuring User Authentication Through Public-key Mechanisms. PAEWS Allows Authorized Users To Perform Flexible Wildcard-based Searches Without Revealing Query Contents Or Sensitive Data To The Cloud Server, Thereby Preserving Confidentiality. The Scheme Is Constructed Under A Rigorous Security Model, Providing Guarantees Of Data Privacy, Query Unlinkability, And Resistance To Unauthorized Access. Performance Analysis And Experimental Evaluations Demonstrate That PAEWS Achieves Strong Security With Practical Efficiency, Making It Well-suited For Real-world Applications Such As Healthcare, Finance, And Secure Information Retrieval In Cloud Environments.
The Rapid Advancement Of Blockchain Technology Has Enabled Secure And Transparent Data Storage Across Decentralized Networks. However, Effective Data Sharing Between Heterogeneous Blockchains Remains A Major Challenge Due To Differences In Governance, Security Models, And Access Control Policies. Attribute-Based Encryption (ABE) Has Emerged As A Promising Cryptographic Solution For Fine-grained Access Control, Yet Most Existing Schemes Rely On A Single Trusted Authority, Limiting Scalability, Flexibility, And Resistance To Authority Compromise. To Address These Limitations, This Study Proposes A Multi-Authority Attribute-Based Encryption (MA-ABE) Scheme With Access Delegation Tailored For Cross-blockchain Data Sharing. In The Proposed Framework, Multiple Independent Authorities Collaboratively Manage User Attributes, Thereby Eliminating Single Points Of Failure And Enhancing Trust Distribution. Furthermore, An Access Delegation Mechanism Is Integrated To Allow Secure Transfer Of Access Rights Among Authorized Entities Without Exposing Secret Keys, Thereby Enabling Flexible And Efficient Data Sharing Across Blockchain Platforms. The Scheme Ensures Confidentiality, Fine-grained Access Control, And Interoperability While Reducing Computational Overhead Through Optimized Key Generation And Decryption Processes. Security Analysis Demonstrates That The Proposed Scheme Resists Collusion Attacks, Unauthorized Access, And Authority Compromise, While Performance Evaluation Indicates Its Practicality In Real-world Cross-blockchain Applications Such As Supply Chain Management, Healthcare, And Financial Data Sharing.
The Exponential Growth Of Internet-connected Devices And Online Services Has Amplified The Frequency And Sophistication Of Distributed Denial-of-Service (DDoS) Attacks, Posing Severe Threats To Network Availability And Reliability. Traditional Rule-based And Signature-driven Detection Systems Often Fail Against Evolving Attack Patterns, While Conventional Machine Learning Models Struggle To Generalize In Highly Dynamic Traffic Environments. Deep Learning Techniques Have Recently Shown Promise In Extracting Complex Traffic Patterns For Accurate Anomaly Detection; However, Their Performance Is Often Hindered By Issues Such As High False-positive Rates And Vulnerability To Adversarial Manipulation. To Address These Challenges, This Study Proposes A Deep Learning-based DDoS Detection Framework Enhanced With Adversarial Optimization, Which Simultaneously Improves Detection Robustness And Adaptability. The Proposed Method Leverages Deep Neural Architectures For Feature Representation And Employs Adversarial Optimization Strategies To Refine Model Parameters, Enhance Resilience Against Evasion Attempts, And Minimize Classification Errors. Experimental Evaluation On Benchmark Network Traffic Datasets Demonstrates That The Framework Achieves Superior Detection Accuracy, Robustness To Adversarially Crafted Traffic, And Reduced False Alarms Compared To Baseline Models. This Work Highlights The Potential Of Integrating Deep Learning With Adversarial Optimization As A Practical And Scalable Solution For Next-generation DDoS Attack Detection In Complex Network Environments.
Email Remains A Primary Vector For Unsolicited And Malicious Content, Motivating Robust Spam Detection At Scale. This Comprehensive Review Synthesizes Progress In Machine-learning–based Spam Classification Across Feature Engineering, Model Design, Evaluation, And Deployment. We Contrast Classical Pipelines—tokenization, N-grams, TF-IDF, And Metadata Cues—with Representation Learning Via Word/character Embeddings And Contextual Language Models. Algorithms Surveyed Span Naïve Bayes, Logistic Regression, SVMs, K-NN, Decision Trees And Random Forests, Gradient Boosting, And Deep Architectures (CNN/RNN Hybrids, Attention Mechanisms, And Transformer-based Models), Including Ensemble And Hybrid Systems That Balance Precision And Recall Under Class Imbalance. We Examine Benchmark Datasets And Evaluation Protocols, Highlighting Metric Selection (precision/recall, F1, ROC-AUC, PR-AUC), Cost-sensitive Learning, And The Impact Of Concept Drift And Adversarial Obfuscation. Practical Considerations Include Multilingual Handling, Image/URL Attachments, Header/network Features, Interpretability, Resource Constraints For On-device Filtering, And Privacy-preserving Training. The Review Identifies Open Challenges—rapidly Evolving Spam Tactics, Domain Shift, Limited Labeled Data, And Explainability—and Outlines Promising Directions: Semi/self-supervised And Active Learning, Transfer From Large Language Models, Adaptive Online Learning For Drift, Graph And URL Reputation Features, And Privacy-aware Federated Approaches. We Conclude With A Taxonomy Of Methods And Deployment Patterns, Offering Guidance On Algorithm Selection And Research Gaps For Resilient, Scalable Email Spam Filtering.
The Increasing Prevalence Of Portable Document Format (PDF) Files In Everyday Digital Communication Has Also Made Them A Prime Target For Cyberattacks, As Adversaries Exploit Embedded Scripts, Obfuscated Content, And Structural Vulnerabilities To Deliver Malware. Traditional Signature-based Detection Methods Often Fail Against Novel Or Obfuscated Malicious PDFs, Necessitating More Intelligent And Adaptive Approaches. Machine Learning (ML) Offers A Promising Solution By Learning Discriminative Patterns From Benign And Malicious Files, Enabling Detection Beyond Known Signatures. This Work Proposes A Malicious PDF Detection Framework Based On Machine Learning With An Enhanced Feature Set That Integrates Structural Properties, Content-based Attributes, Metadata Analysis, And Behavioral Indicators. By Combining Static And Dynamic Feature Categories, The Framework Improves Robustness Against Evasion Techniques While Maintaining Scalability. Extensive Evaluation On Benchmark Datasets Demonstrates That The Enhanced Feature Representation Significantly Boosts Classification Accuracy, Reduces False Positives, And Provides Better Generalization Compared To Conventional Feature-limited Models. The Proposed Approach Highlights The Effectiveness Of Enriched Feature Engineering In Strengthening Machine-learning–based Defenses Against Evolving Malicious PDF Threats.
Insider Threats Remain A Critical And Growing Risk To Organizations As Attackers Exploit Valid Credentials, Careless Behaviour, And Increasingly, Generative AI To Scale And Obfuscate Malicious Activity. Recent Surveys Report That A Large Majority Of Organizations Experienced Insider Incidents Within The Last Year, And Financial Impacts Continue To Rise. Contemporary Detection Approaches Exhibit Three Converging Trends. First, Behavior-centric Analytics—notably User And Entity Behavior Analytics (UEBA)—are Gaining Traction As Essential Complements To Traditional Controls (IAM, DLP, EDR) Because They Detect Subtle Contextual Deviations Rather Than Solely Rule-triggered Events. Second, Machine Learning And Deep-learning Techniques (including CNNs, Transformer Architectures And Hybrid Models) Are Increasingly Used To Model Complex Temporal And Contextual Patterns In User Activity, Improving Detection Sensitivity But Raising Challenges In Explainability, Dataset Bias, And Privacy. Third, There Is Growing Emphasis On Trustworthy, Privacy-preserving And Explainable Detection Pipelines—federated Learning, Model Interpretability Tools, And Human-in-the-loop Workflows—to Balance Detection Performance With Legal/ethical Constraints And Operational Acceptability. We Conclude That Effective Insider Risk Programs Will Be Pragmatic And Multidisciplinary: Combining Advanced Analytics, Clear GenAI Governance, Data-minimizing Architectures, Workforce Education, And Incident-response Maturity. Future Research Should Prioritize Realistic, Diverse Datasets, Interpretable Models, And Measurable ROI To Aid Adoption In Resource-constrained Environments.
Domain Name System (DNS) Tunneling Has Emerged As A Covert Channel For Data Exfiltration And Command-and-control Communication, Allowing Attackers To Bypass Traditional Security Controls By Encapsulating Malicious Payloads Within Seemingly Benign DNS Traffic. Campus Networks, Characterized By Large-scale User Populations, Diverse Devices, And High Query Volumes, Are Particularly Vulnerable To Such Stealthy Attacks. Traditional Rule-based And Signature-driven Detection Approaches Often Fail Against Sophisticated Or Adaptive Tunneling Techniques That Mimic Legitimate DNS Behavior. This Work Proposes A Machine-learning–based DNS Tunnel Detection Scheme Tailored For Campus Network Environments. The Framework Extracts Discriminative Statistical And Lexical Features From DNS Queries And Responses—such As Query Length, Entropy, Frequency Distribution, And Domain Name Patterns—to Build Robust Classification Models. Supervised Learning Algorithms Are Evaluated For Their Ability To Distinguish Tunneling Traffic From Normal Queries With High Accuracy While Minimizing False Alarms. Experimental Results On Real-world Campus Network Datasets Demonstrate That The Proposed Approach Effectively Detects Various Tunneling Tools And Techniques, Achieving Improved Detection Performance Over Conventional Methods. This Study Highlights The Potential Of Machine Learning In Strengthening DNS Security Within Educational Networks And Provides A Foundation For Scalable, Adaptive Intrusion Detection Mechanisms.
The Rapid Growth Of Multimedia Communication Has Intensified Interest In Video Steganography As A Means Of Secure Data Concealment And Transmission. Unlike Traditional Steganographic Methods That Focus Primarily On Imperceptibility And Payload Capacity, Modern Threats Demand Stronger Resilience, Adaptability, And Layered Security. This Work Explores Advancements In Video Steganography Enhanced By Multifactor Authentication (MFA) Mechanisms And Powered By Convolutional Neural Networks (CNNs). CNNs Are Employed To Learn Robust Feature Representations For Both Embedding And Extraction, Improving Imperceptibility While Resisting Steganalysis Attacks. By Integrating MFA—including Biometric, Cryptographic, And Behavioral Verification—the Framework Ensures That Concealed Data Remains Inaccessible To Unauthorized Parties Even If Extraction Is Attempted. Experimental Evaluation Demonstrates Improved Payload Fidelity, Higher Peak Signal-to-noise Ratio (PSNR), And Reduced Detection Rates Against Conventional Steganalysis, While Authentication Layers Significantly Strengthen Data Confidentiality. The Proposed Approach Highlights A Secure, Intelligent, And Resilient Paradigm For Video Steganography, Suitable For Applications In Defense, Healthcare, And Digital Forensics Where Both Covert Communication And Stringent Access Control Are Essential.
Phishing Attacks Continue To Be One Of The Most Prevalent And Damaging Forms Of Cybercrime, Exploiting Human Trust To Steal Sensitive Information Such As Login Credentials, Financial Data, And Personal Identities. Traditional Blacklist-based Approaches Often Fail To Detect Newly Generated Or Obfuscated Phishing Websites, Necessitating More Adaptive And Intelligent Detection Mechanisms. This Work Proposes A Machine Learning–driven Phishing Detection System Integrated With The Flask Web Framework To Provide A Lightweight, Real-time, And User-friendly Application. Features Such As URL Characteristics, HTML And JavaScript Properties, And Domain-related Attributes Are Extracted And Used To Train Classification Models Capable Of Distinguishing Between Legitimate And Phishing Websites. The Trained Model Is Then Deployed Through Flask, Enabling Users To Input URLs And Receive Instant Detection Results Via A Web Interface. Experimental Results Demonstrate The Effectiveness Of The Proposed System In Achieving High Detection Accuracy And Generalizing Well To Unseen Phishing Attempts. The Integration Of Machine Learning With Flask Enhances Both Usability And Scalability, Making The Solution Practical For Deployment In Real-world Scenarios To Strengthen Defenses Against Phishing Threats.
Credit Card Fraud Has Emerged As One Of The Most Significant Challenges In The Digital Economy, With Fraudulent Transactions Causing Massive Financial Losses And Undermining Customer Trust In Financial Institutions. Traditional Rule-based Fraud Detection Systems Often Fail To Adapt To The Evolving Strategies Of Fraudsters, Resulting In Delayed Or Inaccurate Detection. To Address This Limitation, Machine Learning As A Data Mining Technique Provides A Promising Approach By Uncovering Hidden Patterns, Anomalies, And Correlations Within Large-scale Transaction Datasets. This Work Explores The Application Of Supervised And Unsupervised Learning Models—such As Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, And Clustering Methods—for Detecting Fraudulent Credit Card Transactions. Emphasis Is Placed On Handling Challenges Like Class Imbalance, Real-time Detection Requirements, And Model Interpretability. By Integrating Advanced Feature Engineering, Resampling Techniques, And Ensemble Learning, The Proposed System Aims To Improve Accuracy, Precision, And Recall In Fraud Detection While Minimizing False Positives. This Research Highlights The Potential Of Machine Learning To Create Adaptive, Efficient, And Scalable Solutions, Ultimately Strengthening Security In Electronic Payment Systems.
With The Exponential Growth Of Digital Communication, Ensuring The Confidentiality And Integrity Of Sensitive Information Has Become A Critical Challenge. Steganography, The Art Of Concealing Data Within Digital Media, Provides An Effective Means Of Covert Communication. Among Various Methods, Least Significant Bit (LSB) Substitution Is Widely Adopted Due To Its Simplicity And High Payload Capacity. However, Traditional LSB Techniques Are Vulnerable To Statistical Attacks And Unauthorized Extraction, Thereby Limiting Their Robustness. To Address These Shortcomings, This Work Proposes An Improved LSB-based Image Steganography Approach Integrated With The Advanced Encryption Standard (AES) Algorithm. Prior To Embedding, Secret Data Is Encrypted Using AES To Provide Strong Cryptographic Protection, Ensuring That Even If The Hidden Data Is Extracted, It Remains Unintelligible Without The Decryption Key. The Encrypted Message Is Then Embedded Into The LSBs Of Cover Images, Thereby Combining The Advantages Of High Imperceptibility And Enhanced Security. Experimental Results Demonstrate That The Proposed Method Achieves High Peak Signal-to-Noise Ratio (PSNR) And Structural Similarity Index (SSIM), Indicating Minimal Visual Distortion Of The Stego Image, While Significantly Strengthening Resistance Against Steganalysis And Brute-force Attacks. This Dual-layered Security Model Enhances The Confidentiality, Integrity, And Reliability Of Hidden Communication, Making It Suitable For Secure Multimedia Transmission In Real-world Applications.
The Exponential Growth Of Digital Communication Has Increased The Demand For Secure And Reliable Methods Of Information Concealment. Steganography, The Science Of Hiding Information Within Digital Media, Offers An Effective Solution For Covert Communication By Embedding Secret Data Into Images And Videos Without Arousing Suspicion. This Work Proposes A Hybrid Approach That Integrates Cryptography With Steganography To Enhance Both Confidentiality And Robustness. In The Proposed System, Sensitive Text Messages Are First Encrypted Using The RC6 Block Cipher, A Symmetric Key Encryption Algorithm Known For Its Efficiency And Strong Security Against Cryptanalytic Attacks. The Encrypted Data Is Then Embedded Into Video Frames Using The Least Significant Bit (LSB) Substitution Technique, Ensuring Minimal Distortion And Imperceptibility To The Human Eye. To Further Strengthen Access Control, A Password-based Authentication Mechanism Is Incorporated To Restrict Unauthorized Extraction. Experimental Evaluation Demonstrates That The Proposed Model Achieves High Payload Capacity, Strong Resistance Against Steganalysis, And Effective Protection Of Hidden Information, Making It A Viable Solution For Secure Multimedia Communication In Modern Digital Environments.
Automated Teller Machines (ATMs) Play A Vital Role In Modern Banking, Providing Customers With Quick And Convenient Access To Financial Services. However, ATM Transactions Remain Vulnerable To Security Threats Such As Skimming, Card Cloning, And PIN Theft, Which Compromise User Privacy And Trust. To Address These Challenges, This Work Proposes A Secure Cash Withdrawal Mechanism Using QR Code Technology. In The Proposed System, The User Initiates A Withdrawal Request Through A Mobile Banking Application, Which Generates A Unique, Time-sensitive QR Code Containing Encrypted Transaction Details. The ATM Scans The QR Code To Authenticate The User And Complete The Transaction Without Requiring A Physical Card Or Manual PIN Entry. This Approach Enhances Transaction Security By Eliminating Card-related Fraud, Reducing Dependence On Traditional Authentication Methods, And Ensuring End-to-end Encryption Of Sensitive Data. The Integration Of QR Code Technology With ATM Services Not Only Strengthens Security But Also Improves User Convenience, Paving The Way For Safer And More Efficient Digital Banking Solutions.
The Rapid Digital Transformation In Healthcare Has Led To A Surge In Electronic Health Records (EHRs) And Patient-centric Health Applications. However, The Lack Of Interoperability And Standardized Data Exchange Mechanisms Continues To Hinder Seamless Communication Between Hospitals And Patients. This Work Proposes An E-Health Framework That Enables Interoperable And Discrete Data Exchange, Ensuring That Patient Health Information Can Be Securely Accessed, Shared, And Utilized Across Different Healthcare Systems. The Proposed Model Integrates Standardized Protocols Such As HL7 FHIR (Fast Healthcare Interoperability Resources) And Employs Secure APIs To Facilitate Real-time, Bidirectional Communication. By Enabling Patients To Access Their Health Records In A Structured And Discrete Format, The System Enhances Transparency, Empowers Patients In Decision-making, And Supports Continuity Of Care. Furthermore, Strong Encryption And Authentication Mechanisms Are Incorporated To Maintain Data Privacy And Compliance With Healthcare Regulations Such As HIPAA. The Implementation Of Such An Interoperable Exchange Framework Not Only Reduces Administrative Overhead And Medical Errors But Also Fosters A Patient-centered Healthcare Ecosystem, Where Accurate And Timely Health Information Drives Improved Clinical Outcomes.
The Rapid Digitalization Of Healthcare Services Has Increased The Need For Secure And Efficient Hospital Management Systems. Traditional Systems Often Face Challenges Related To Unauthorized Access, Data Breaches, And Inefficient Patient Record Handling, Which Compromise Both Privacy And Trust. To Address These Issues, This Work Proposes A Hospital Management System Integrated With A QR Code–based Security Mechanism. The System Manages Core Hospital Operations Such As Patient Registration, Appointment Scheduling, Medical Records, Billing, And Staff Management While Embedding A Unique, Encrypted QR Code For Each Patient And Staff Member. The QR Code Serves As A Secure Identifier, Enabling Controlled Access To Sensitive Health Data And Reducing Risks Of Identity Theft Or Unauthorized Entry. By Leveraging QR Code Technology, The Proposed System Ensures Data Integrity, Confidentiality, And Easy Retrieval Of Records, While Also Streamlining Hospital Workflows. This Framework Not Only Strengthens Data Security But Also Enhances Operational Efficiency, Offering A Reliable, Scalable, And User-friendly Solution For Modern Healthcare Institutions.
ABSTRACT Traditional Alphanumeric Password Schemes Are Widely Used In Banking Systems But Remain Highly Vulnerable To Attacks Such As Shoulder Surfing, Where Attackers Directly Observe Or Record User Credentials During The Login Process. This Compromises Security And Erodes User Trust. To Address These Challenges, This Work Proposes A Shoulder Surfing Resistant Graphical Password Authentication System Tailored For Banking Applications. Unlike Conventional Text-based Methods, The Proposed System Employs Graphical Images, Patterns, And Randomized Challenges To Enhance Both Usability And Security. Users Authenticate By Selecting Or Interacting With Images Through Dynamic Interfaces That Change During Each Login Attempt, Significantly Reducing The Risk Of Observation-based Attacks. The System Integrates Encryption Techniques To Secure Password Data And Ensures Compatibility With Existing Banking Infrastructures. By Combining Resistance To Shoulder Surfing With Improved Memorability And User Experience, The Proposed Approach Provides A Robust Authentication Mechanism, Thereby Strengthening Security In Modern Banking Systems.
In Modern Communication Systems, Secure Key Generation And Management Play A Crucial Role In Protecting Data Against Unauthorized Access And Cyberattacks. Many-to-one Networks, Where Multiple Users Or Devices Communicate With A Single Central Node, Face Unique Challenges In Ensuring Confidentiality, Integrity, And Authentication Of Transmitted Data. This Work Proposes A Secure Secret-key Generation And Verification Framework Designed For Many-to-one Network Environments. The System Enables Each User To Generate A Unique Secret Key That Is Securely Shared With The Central Node. These Keys Are Then Used For Encrypting Messages At The Sender’s Side And Decrypting Them At The Receiver’s Side, Ensuring End-to-end Confidentiality. A Verification Mechanism Is Integrated To Authenticate The Key Before Decryption, Preventing Replay Attacks, Man-in-the-middle Intrusions, And Unauthorized Access. By Combining Symmetric Encryption With A Robust Verification Process, The Proposed Model Enhances Both The Efficiency And Security Of Communication In Many-to-one Networks, Making It Suitable For Applications Such As Cloud Computing, IoT Systems, And Secure Data Aggregation.
With The Rapid Growth Of Digital Communication And Increasing Sophistication Of Cyberattacks, Intrusion Detection Systems (IDS) Have Become Essential For Safeguarding Computer Networks. Traditional IDS Often Face Challenges Such As High Dimensionality Of Data, Redundant Features, And Reduced Detection Accuracy When Dealing With Large-scale Traffic. To Address These Issues, This Work Proposes An Intrusion Detection Framework That Integrates Principal Component Analysis (PCA) With A Random Forest Classifier. PCA Is Employed As A Dimensionality Reduction Technique To Eliminate Noise And Redundancy From Network Traffic Features, Thereby Improving Computational Efficiency. The Refined Feature Set Is Then Classified Using The Random Forest Algorithm, Known For Its Robustness, Scalability, And Ability To Handle Complex, Non-linear Relationships In Data. Experimental Evaluation Demonstrates That The Proposed PCA–Random Forest Approach Enhances Detection Accuracy, Reduces False Positive Rates, And Achieves Faster Processing Compared To Conventional Methods. This Hybrid Model Provides An Effective And Reliable Solution For Real-time Intrusion Detection In Modern Network Environments.
With The Rapid Growth Of Digital Media And Online Platforms, The Spread Of Fake News Has Become A Critical Challenge, Influencing Public Opinion, Creating Social Unrest, And Threatening The Credibility Of Information Sources. Traditional Manual Fact-checking Methods Are Time-consuming And Insufficient To Handle The Massive Volume Of Online Content. To Address This Issue, This Work Proposes A Machine Learning–based Framework For Automatic Fake News Detection. The System Leverages Natural Language Processing (NLP) Techniques To Extract Meaningful Features Such As Linguistic Patterns, Semantic Relationships, And Contextual Cues From News Articles. These Features Are Then Classified Using Supervised Learning Algorithms, Including Logistic Regression, Support Vector Machines, And Random Forest, To Distinguish Between Fake And Legitimate News. The Proposed Framework Is Evaluated On Benchmark Datasets, Demonstrating Improved Accuracy And Robustness Compared To Baseline Models. This Study Highlights The Potential Of Machine Learning In Combating Misinformation And Provides A Scalable Solution For Enhancing The Reliability Of Digital News Consumption.
With The Rapid Growth Of Social Networking Platforms, Facebook Has Become A Prime Target For Malicious Applications That Exploit User Trust, Harvest Personal Data, And Spread Harmful Content. These Applications Often Disguise Themselves As Legitimate Services, Making It Difficult For Users To Identify And Avoid Them. Traditional Security Mechanisms Such As User Reports And Manual Reviews Are Insufficient To Handle The Scale And Sophistication Of These Threats. To Address This Challenge, This Work Proposes A Detection Framework For Identifying Malicious Facebook Applications Using Machine Learning And Behavioral Analysis Techniques. The System Leverages Features Such As Permission Requests, Application Interaction Patterns, And User Activity Logs To Distinguish Between Benign And Malicious Applications. By Applying Classification Algorithms And Anomaly Detection Models, The Framework Can Proactively Flag Suspicious Applications Before They Cause Significant Harm. The Proposed Solution Enhances User Safety, Preserves Data Privacy, And Strengthens Trust In Social Networking Environments By Providing An Automated, Scalable, And Efficient Detection Mechanism.
With The Growing Reliance On Digital Platforms, Ensuring Data Security And User Authentication Has Become A Critical Challenge. Traditional Text-based Passwords And Authentication Mechanisms Are Increasingly Vulnerable To Attacks Such As Phishing, Brute Force, And Shoulder Surfing. To Address These Limitations, This Work Proposes A Desktop Application That Leverages QR Code Generation And Recognition For Enhanced Data Security And Authentication. The System Allows Users To Securely Encode Sensitive Information Into QR Codes Using Encryption Algorithms, Which Can Only Be Accessed And Decoded By Authorized Users. In Addition, The Recognition Module Ensures Quick And Reliable Scanning Of QR Codes For Authentication, Reducing Risks Of Credential Theft And Unauthorized Access. The Proposed Approach Combines The Advantages Of QR Code Technology—such As Portability, Ease Of Use, And Error Correction—with Modern Security Techniques To Create A Robust, User-friendly, And Efficient Solution. This Framework Can Be Applied In Domains Such As Secure Login Systems, Document Protection, And Confidential Data Sharing, Providing A Scalable And Reliable Method For Safeguarding Digital Assets.
The Widespread Adoption Of QR Codes Has Revolutionized Various Industries, Streamlined Transactions And Improved Inventory Management. However, This Increased Reliance On QR Code Technology Also Exposes It To Potential Security Risks That Malicious Actors Can Exploit. QR Code Phishing, Or “Quishing”, Is A Type Of Phishing Attack That Leverages QR Codes To Deceive Individuals Into Visiting Malicious Websites Or Downloading Harmful Software. These Attacks Can Be Particularly Effective Due To The Growing Popularity And Trust In QR Codes. This Paper Examines The Importance Of Enhancing The Security Of QR Codes Through The Utilization Of Artificial Intelligence (AI). The Abstract Investigates The Integration Of AI Methods For Identifying And Mitigating Security Threats Associated With QR Code Usage. By Assessing The Current State Of QR Code Security And Evaluating The Effectiveness Of AI-driven Solutions, This Research Aims To Propose Comprehensive Strategies For Strengthening QR Code Technology’s Resilience. The Study Contributes To Discussions On Secure Data Encoding And Retrieval, Providing Valuable Insights Into The Evolving Synergy Between QR Codes And AI For The Advancement Of Secure Digital Communication.
Phishing Is An Internet Scam In Which An Attacker Sends Out Fake Messages That Look To Come From A Trusted Source. A URL Or File Will Be Included In The Mail, Which When Clicked Will Steal Personal Information Or Infect A Computer With A Virus. Traditionally, Phishing Attempts Were Carried Out Through Wide-scale Spam Campaigns That Targeted Broad Groups Of People Indiscriminately. The Goal Was To Get As Many People To Click On A Link Or Open An Infected File As Possible. There Are Various Approaches To Detect This Type Of Attack. One Of The Approaches Is Machine Learning. The URL’s Received By The User Will Be Given Input To The Machine Learning Model Then The Algorithm Will Process The Input And Display The Output Whether It Is Phishing Or Legitimate. There Are Various ML Algorithms Like SVM, Neural Networks, Random Forest, Decision Tree, XG Boost Etc. That Can Be Used To Classify These URLs. The Proposed Approach Deals With The Random Forest, Decision Tree Classifiers. The Proposed Approach Effectively Classified The Phishing And Legitimate URLs With An Accuracy Of 87.0% And 82.4% For Random Forest And Decision Tree Classifiers Respectively.
As Seen In The Past Few Decades, It Is Very Common To Observe The Patient’s Paper Work At The Hospital. Even Though The Same Personal Information Is Used, An Unusual Way To Actually Decrement The Amount Of These Paper Works Does Not Exist. The Development Of Mobile Web Provides Development Direction For Medical Industry And A New Service Mode. In This Paper, We Introduce QR Code Based E-health Authentication System To Obtain Patient’s Health Record Easily And Securely In The Local Hospital And Also To Reduce The Redundant Paper Work. One Of The Aims Of This Project Is To Use The Dataset And Machine Learning Techniques To Predict The Type Of Disease Based On The Symptoms. A QR Code Which Includes Predicted Disease And Personal Information Of Patient Is Sent To The Doctor Automatically Via Email. Further The Doctor Sends A QR Code Generated Prescription To The Patient Which Is Scanned By The Pharmacist .Here, We Describe An Integrated System, Developed For Use By The Healthcare Personnel Within Healthcare Facilities, Adapted To All Handheld Devices .With Our Proposed Scheme, We Believe That It Will Improve Efficiency In Terms Of The Cost And Time For The Patient, Hospital And The Doctor And Protect Patient’s Personal Information.
Text-based Password Authentication Is A Common Method Used To Verify The Identity Of Users Who Are Trying To Access A Secure System Or Service. In Order To Use This Authentication Method, The User Must Input A Password Or Other Secret Phrase That Is Then Compared To A Server-side Copy Of The Same Password. Access Is Given If The Password Typed Matches The One Saved. Graphical Password Authentication Is A Type Of User Authentication That Involves Using Images Or Visual Elements Instead Of Alphanumeric Characters To Verify The Identity Of The User. Unlike Traditional Text-based Passwords, Graphical Passwords Offer An Intuitive And User-friendly Way Of Authentication, As They Rely On The User's Ability To Remember Pictures, Shapes, And Patterns. This Technology Has Been Developed To Address The Limitations Of Traditional Text-based Passwords, Such As The Difficulty Of Creating And Remembering Complex Passwords, And The Vulnerability To Brute-force Attacks. Compared To Conventional Text-based Passwords, Graphical Password Authentication Has A Number Of Benefits, Including Better Usability And Higher Security.
Films Are A Significant Form Of Entertainment Form Of Entertainment In Modern Society, With Filmmakers Investing Substantial Resources Into Their Production. However, This Effort Is Undermined By Piracy, Where Individuals Copy And Distribute Film Content Illegally, Often By Recording Movies With Portable Often By Recording Movies With Portable Cameras And Uploading Them To Online Platforms. Camcorder Theft In Particular Has A Significant Impact On The Film Industry. Despite Efforts To Track Pirates, Watermarking In Pirated Movies Is Often Undetectable, Making It Difficult To Deter Piracy Effectively.To Address This Issue, This Paper Proposes Two Innovative Solutions. Firstly, It Suggests Embedding A Secret Key Using Steganography Via MATLAB To Secure Movies Files. Steganography Allows For The Concealment Of Information Within Digital Media, Providing A Covert Means Of Protection. Secondly, This Recommendation Involves Constructing A Screen Fitted With An Infrared Transmitter Which Would Prevent People From Filming Illegally In Cinemas. The Idea Behind This System Is That It Emits Infrared Signals At The Same Time As Films Are Being Shown Thereby Making Recording Impossible. Also GSM Technology Can Be Used To Send Quick SMS Alerts To Authorized Personal Whenever There Is An Attempt At Piracy For Immediate Response. On The Whole, These Measures Are Designed To Combat Theater Piracy By Making Movies More Secure Against Illegal Duplication While At The Same Time Preventing People From Recording Them Without Permission.
It Is Estimated That Globally 425 Million Subjects Have Moderate To Severe Obstructive Sleep Apnea (OSA). The Accurate Prediction Of Sleep Apnea Events Can Offer Insight Into The Development Of Treatment Therapies. However, Research Related To This Prediction Is Currently Limited. We Developed A Covert Framework For The Prediction Of Sleep Apnea Events Based On Low-frequency Breathing-induced Vibrations Obtained From Piezoelectric Sensors. A CNN-transformer Network Was Utilized To Efficiently Extract Local And Global Features From Respiratory Vibration Signals For Accurate Prediction. Our Study Involved Overnight Recordings Of 105 Subjects. In Five-fold Cross-validation, We Achieved An Accuracy Of 85.9% And An F1 Score Of 85.8%, Which Are 3.5% And 5.3% Higher Than The Best-performed Classical Model, Respectively. Additionally, In Leave-one-out Cross-validation, 2.3% And 3.8% Improvements Are Observed, Respectively. Our Proposed CNN-transformer Model Is Effective In The Prediction Of Sleep Apnea Events. Our Framework Can Thus Provide A New Perspective For Improving OSA Treatment Modes And Clinical Management.
As Quantum Computing Evolves From Theoretical Promise To Emerging Reality, The Urgency To Develop Quantum-resilient Data Protection Mechanisms Becomes Increasingly Paramount-particularly Within Critical Infrastructure Systems Dependent On Multi-cloud Architectures. This Study Explores The Design And Deployment Of Quantum-resilient Encryption Protocols Tailored To Secure Sensitive Data Flows Across Heterogeneous And Decentralized Cloud Environments Supporting Energy, Transportation, Defense, And Healthcare Infrastructures. Beginning From A Broader Analysis Of Cryptographic Vulnerabilities Posed By Quantum Adversaries-especially Those Exploiting Shor's And Grover's Algorithms-the Paper Highlights The Limitations Of Current Asymmetric Key Systems And Symmetric Encryption Practices In Multi-cloud Data Orchestration. Building On This Foundation, The Research Narrows In On Post-quantum Cryptographic (PQC) Frameworks, Including Lattice-based, Code-based, And Multivariate Polynomial Schemes, Evaluating Their Performance And Adaptability For Dynamic Cloud-native Systems. A Key Focus Is Placed On Designing Lightweight, Interoperable Encryption Protocols That Can Seamlessly Integrate With Federated Identity Management, Zero-trust Security Models, And Real-time Data Streams Without Introducing Prohibitive Latency Or Computational Burden. The Study Also Presents An Architectural Model That Allows For Real-time Key Negotiation, Distributed Trust Management, And Algorithm Agility, Ensuring Compliance With Both Current And Forward-looking Regulatory Standards (e.g., NIST PQC Guidelines). Simulation And Benchmarking Conducted Across Hybrid Cloud Environments Demonstrate That Carefully Optimized Quantum-resilient Protocols Can Be Implemented Without Compromising System Availability Or Scalability. The Results Validate The Feasibility Of Transitioning From Conventional Cryptography To Quantum-safe Models In Mission-critical Multi-cloud Operations. The Paper Concludes By Offering A Strategic Roadmap For Organizations Seeking To Future-proof Their Cloud Infrastructures Against Quantum-era Threats.
Nowadays, Distributed Data Processing In Cloud Computing Has Gained Increasing Attention From Many Researchers. The Intense Transfer Of Data Has Made The Network An Attractive And Vulnerable Target For Attackers To Exploit And Experiment With Different Types Of Attacks. Therefore, Many Intrusion Detection Techniques Have Been Evolving To Protect Cloud Distributed Services By Detecting The Different Attack Types On The Network. Machine Learning Techniques Have Been Heavily Applied In Intrusion Detection Systems With Different Algorithms. This Paper Surveys Recent Research Advances Linked To Machine Learning Techniques. We Review Some Representative Algorithms And Discuss Their Proprieties In Detail. We Compare Them In Terms Of Intrusion Accuracy And Detection Rate Using Different Data Sets.
Because Of The Fast Expansion Of Internet Users, Phishing Attacks Have Become A Significant Menace Where The Attacker Poses As A Trusted Entity In Order To Steal Sensitive Data, Causing Reputational Damage, Loss Of Money, Ransomware, Or Other Malware Infections. Intelligent Techniques Mainly Machine Learning (ML) And Deep Learning (D L) Are Increasingly Applied In The Field Of Cyber Security Due To Their Ability To Learn From Available Data In Order To Extract Useful Insight And Predict Future Events. The Effectiveness Of Applying Such Intelligent Approaches In Detecting Phishing Web Sites Is Investigated In This Paper. We Used Two Separate Datasets And Selected The Highest Correlated Features Which Comprised Of A Combination Of Content-based, URL Lexical-based, And Domain-based Features. A Set Of ML Models Were Then Applied, And A Comparative Performance Evaluation Was Conducted. Results Proved The Importance Of Features Selection In Improving The Models' Performance. Furthermore, The Results Also Aimed To Identify The Best Features That Influence The Model In Identifying Phishing Websites. For Classification Performance, Random Forest (RF) Algorithm Achieved The Highest Accuracy For Both Datasets.
Credit Card Fraud Detection Is Presently The Most Frequently Occurring Problem In The Present World. This Is Due To The Rise In Both Online Transactions And E-commerce Platforms. Credit Card Fraud Generally Happens When The Card Was Stolen For Any Of The Unauthorized Purposes Or Even When The Fraudster Uses The Credit Card Information For His Use. In The Present World, We Are Facing A Lot Of Credit Card Problems. To Detect The Fraudulent Activities The Credit Card Fraud Detection System Was Introduced. This Project Aims To Focus Mainly On Machine Learning Algorithms. The Algorithms Used Are Random Forest Algorithm And The ExtraTreesClassifier Algorithm. The Results Of The Two Algorithms Are Based On Accuracy, Precision, Recall, And F1-score. The ROC Curve Is Plotted Based On The Confusion Matrix. The Random Forest And The Extra-Trees Classifier Algorithms Are Compared And The Algorithm That Has The Greatest Accuracy, Extra-Trees Classifier Is Considered As The Best Algorithm That Is Used To Detect The Fraud.
The Security Of Any Public Key Cryptosystem Depends On The Private Key Thus, It Is Important That Only An Authorized Person Can Have Access To The Private Key. The Paper Presents A New Algorithm That Protects The Private Key Using The Transposition Cipher Technique. The Performance Of The Proposed Technique Is Evaluated By Applying It In The Random Forest Algorithm’s Generated Private Keys Using 512-bit, 1024-bit, And 2048-bit, Respectively. The Result Shows That The Technique Is Practical And Efficient In Securing Private Keys While In Storage As It Produced High Avalanche Effect. Key Generator Is Part Of The Stream Cipher System That Is Responsible For Generating A Long Random Sequence Of Binary Bits Key That Used In Ciphering And Deciphering Processes In Everyday Life, Image Security Is Important These Days As Data Is Increasing A Lot. These Data Can Be Images, Videos, Text, Audio, Etc. So To Protect These Images From Attackers Who Can Destroy The Image Quality Or Modify The Images, Some Technologies Like AES, DES, RSA, Etc. Have Been Invented. With The Generation, Data Security Has Also Become An Essential Issue. Considering These Issues, The Proposed Technique Ensures Confidentiality, Integrity, And Authentication. Using These Techniques, The Host Can Encrypt And Decrypt The Image ,text ,video ,audio. The Digital Technology Was Completely Different From Today And The Scale Of Challenges Was Smaller, So With Recent Advanced Technology And The Emergence Of New Applications Such As Big Data Applications, In Addition To Applications Running With 64-bit And Many Other Applications Have Become Necessary To Design A New Current Algorithm For Current Requirements. Advanced Encryption Algorithm (AES) Is A Symmetric Algorithm, Which We Will Further And In Addition To New Recommendations For Future Work, A List Of Shortcomings And Vulnerabilities Of The Internal Structure Of The AES Algorithm Will Be Diagnosed.
For Secure Data Transmission Over Internet, It Is Important To Transfer Data In High Security And High Confidentiality, Information Security Is The Most Important Issue Of Data Communication In Networks And Internet. Either Image Or Video To Secure Transferred Information From Intruders, It Is Important To Convert The Information Into Cryptic Format The Image And Video Work On The Same Process. Different Methods Used To Ensure Data Security And Confidentiality During Transmission Like Steganography And Cryptography. This Paper We Convert Plaintext To Cipher Text For Doing So We Have Used RC6 Encryption Algorithm The Proposed Algorithm Ensure The Encryption And Decryption Using RC6 Stream Cipher And RGB Pixel Shuffling With Steganography By Using Hash-least Significant Bit (HLSB) That Make Use Of Hash Function To Developed Significant Way To Insert Data Bits In LSB Bits Of RGB Pixels Of Cover Image. The Security Evaluations For The Steganography Part We Will Be Using Modified LSB Algorithm Where We Overwrite The LSB Bits Of The Selected Frame (given By The User) From The Cover Video, With The Bit Of Text Message Character With Help Of Secret Key And Using KSA And PRGA.
Student Attendance System Is Used To Measure Student Participation In A Classroom. Before Pandemic Attendance Was Taken Manually Like In Sheets Or Registers. But When The Pandemic Hit, Everything Was Online, So Even The Classes. The Attendance Count Is A Very Important Problem That The Administrator Needs To Be More Careful About Taking During The Online Classes As There Are Many Chances Of A Proxy Happening. So, We Came Up With This Proposed System “Student Attendance Using QR Code” This Paper Proposes An Attendance System That Is Based On The QR Code-based Attendance System. The Students Need To Scan The QR In The Class According To The Professor Instruction. The Paper Explains The High Level Implementation Details Of The Proposed System. It Also Discusses How The System Verifies Student Identity To Eliminate False Registrations.
Nowadays, Dependency On Banking In The Virtual World Has Been Increased To The Peak Position. To Make It Consistent Advanced Technologies Should Be Used. As OTP Is Currently Used Worldwide For Security Purposes, It Can Be Overruled By QR Code. Main Advantage Of QR Code Over OTP Data Storage. OTP Can Only Confirm That The User Is Authorised User And Not Some Third Party Is Involved In This Transaction While QR Code Not Only Confirms The Authorised User But QR Code Itself Can Store Information Such As Transaction Id, Transaction Date, Time And Also Amount Of Transaction. So, There Is No Need Of Explicitly Keeping Track Of Transaction Every Transaction. Aim Of This Paper To Enhance The Functionality Of ATM Machine Using Android Application. Proposed System Is Combining The ATM And Mobile Banking And Minimizes The Time Of Withdrawing Cash From ATM. This Will Increase The Speed Of Transaction Almost Three Times Fast; Could Have Excellent Impact On Customer's Satisfaction. With The Help Of QR Code Information Get Encrypted So It Also Increases Security. As The Population Increasing ATM Queues Will Be Longer Day By Day. By Implementing Proposed System Current System Will Not Hampered, By Doing Some Minor Changes In Existing System It Will Be Possible To Get Cash Within Seconds. According To Analyst Report, Cost Of Transaction Using Mobile Application Is Almost Ten Times Less Than ATM And About Fifty Times Less, If Physical Bank Branch Used.
In Order To Prevent Health Risks And Provide A Better Service To The Patients That Have Visited The Hospital, There Is A Need For Monitoring The Patients After Being Released And Providing The Data Submitted By The Patient E-Health Enablers To The Medical Personnel. This Article Proposes Architecture For Providing The Secure Exchange Of Data Between The Patient And The Hospital Infrastructure. The Implemented Solution Is Validated On A Laboratory Tested. When It Comes To Exchanging Health Data Between Departments Or Across Institutions, There Are So Many Variables At Play That Additional Rules And Descriptions Are Absolutely Necessary. There Can’t Be Any Ambiguity When Transferring And Interpreting Information About The Patient's Allergies Or The Procedures, Materials, And Medications Required.
Nowadays, Quick Response (QR) Codes Seem To Be Present Everywhere. They Can Be Found On Advertisements In Magazines, Websites, Product Packaging, And Other Places. Since Mobile Phones Have Become A Basic Necessity For Everyone, Using QR Codes Is One Of The Most Fascinating Ways To Link Patients To The Internet Digitally. QR Codes Consist Of Black Squares Arranged In A Grid (matrix) On A White Background And Are Read By Specialized Software That Is Able To Extract Data From The Patterns That Are Present In The Matrix. Now Days It Is Used Widely In Many Organizations. In This Project, We Proposed QR Code-based For Hospital Management System. The Emergence Of QR Has Opened A Vast Variety Of Possibilities In The Technology Sector Which Made Accessing, Retrieving And Viewing Information And Data From Anywhere With Great Speed And Low Fault. It Is A Captivating Way Of Accessing Anything From A Website. Nowadays Due To The Ample Use Of Mobile Devices, Using QR Code Technology We Can Easily Establish Connections And Communicate With People And Share Information. It Is Also A Secure Way To Share Or Secure The Data Because Without The Correct Tool Retrieving Of Data For Someone Else Who Is Not Intended To View Is Impossible. Introducing QR Code Will Increase This Security One More Level Further. In This Paper, This Is A Patient Management App That Uses Both Quick Response (QR) Code Technology Hospital And Accesses Those Data In A Secure And Fast Manner. It Also Can Be Used By Patients To Recollect The Doctor Consultation Data And Retrieving Their Medical Records And Doctor-prescribed Medicines.
Graphical Password Is One Of Technic For Authentication Of Computer Security. The Most Crucial Aspect Of Computer Science Nowadays Is Digital/computer Security, Which Protects User Or Customer Data. And One Of The Hazards Is Shoulder-surfing, In Which A Criminal Can Acquire A Password By Watching Directly Or By Recording The Authentication Session. There Are A Number Of Methods For This Authentication, But The Most Popular And Straightforward Is The Graphic Password Method. A Bank Is Essential To People's Daily Lives. The Bank's Top Priority Is The Security Of Its Customers. To Safeguard User Accounts, The Authentication Process Must Be Secure. Textual Passwords Are A Frequently Used Method. The System Uses The Graphical Password To Demonstrate The Banking Website's Security In Order To Offer A Possible Substitute For The Traditional Alphanumeric Password Techniques To Prevent Shoulder Surfing Techniques.
The Security Of Any Public Key Cryptosystem Depends On The Private Key Thus, It Is Important That Only An Authorized Person Can Have Access To The Private Key. The Paper Presents A New Algorithm That Protects The Private Key Using The Transposition Cipher Technique. The Performance Of The Proposed Technique Is Evaluated By Applying It In The Random Forest Algorithm’s Generated Private Keys Using 512-bit, 1024-bit, And 2048-bit, Respectively. The Result Shows That The Technique Is Practical And Efficient In Securing Private Keys While In Storage As It Produced High Avalanche Effect.
With The Evolution In Wireless Communication, There Are Many Security Threats Over The Internet. The Intrusion Detection System (IDS) Helps To Find The Attacks On The System And The Intruders Are Detected. Previously Various Machine Learning (ML) Techniques Are Applied On The IDS And Tried To Improve The Results On The Detection Of Intruders And To Increase The Accuracy Of The IDS. This Paper Has Proposed An Approach To Develop Efficient IDS By Using The Principal Component Analysis (PCA) And The Random Forest Classification Algorithm. Where The PCA Will Help To Organise The Dataset By Reducing The Dimensionality Of The Dataset And The Random Forest Will Help In Classification. Results Obtained States That The Proposed Approach Works More Efficiently In Terms Of Accuracy As Compared To Other Techniques Like SVM, Naive Bayes, And Decision Tree. The Results Obtained By Proposed Method Are Having The Values For Performance Time (min) Is 3.24 Minutes, Accuracy Rate (%) Is 96.78 %, And The Error Rate (%) Is 0.21 %.
The Phenomenon Of Fake News Is Experiencing A Rapid And Growing Progress With The Evolution Of The Means Of Communication And Social Media. Fake News Detection Is An Emerging Research Area Which Is Gaining Big Interest. It Faces However Some Challenges Due To The Limited Resources Such As Datasets And Processing And Analyzing Techniques. In This Work, We Propose A System For Fake News Detection That Uses Machine Learning Techniques. We Used Term Frequency Inverse Document Frequency Of Bag Of Words And N-grams As Feature Extraction Technique, And Naïve Bayes As A Classifier. We Propose Also A Dataset Of Fake And True News To Train The Proposed System. Obtained Results Show The Efficiency Of The System
With Daily Installs, Third-party Apps Can Be A Important Cause For The Popularity And Attractiveness Of Facebook Or Any Online Social Media. Sadly, Cyber Criminals Get Came To The Realization That The Capability Of Using Apps For Spreading Spam And Malware. We Realize That At The Least 13% Of Facebook Apps In The Dataset Are Usually Malevolent. However With Their Findings , Several Issues Like Faux Profiles, Malicious Application Have Conjointly Full-grown. There Aren't Any Possible Method Exist To Regulate These Issues. During This Project, We Tend To Came Up With A Framework With That Automatic Detection Of Malicious Applications Is Feasible And Is Efficient. Suppose There's Facebook Application, Will The Facebook User Verify That The App Is Malicious Or Not. First We Identify A Set Of Features That Help Us To Analyze Malicious From Benign Ones. Second, Leveraging These Distinguishing Features ,where We Show That Post Of Application As Malicious With 95.9% Accuracy. Finally, We Explore The Ecosystems Of Malicious Facebook Apps And Identify Mechanisms That These Apps Use To Spread.
In Order To Prevent Health Risks And Provide A Better Service To The Patients That Have Visited The Hospital, There Is A Need For Monitoring The Patients After Being Released And Providing The Data Submitted By The Patient EHealth Enablers To The Medical Personnel. This Article Proposes Architecture For Providing The Secure Exchange Of Data Between The Patient Mobile Application And The Hospital Infrastructure. The Implemented Solution Is Validated On A Laboratory Testbed.
Real-time Communication (RTC) Is A New Standard And Industry-wide Effort That Expand The Web Browsing Model, Allowing Access To Information In Areas Like Social Media, Chat, Video Conferencing, And Television Over The Internet, And Unified Communication. These Systems Users Can View, Record, Remark, Or Edit Video And Audio Content Flows Using Time-critical Cloud Infrastructures That Enforce The Quality Of Services. However, There Are Many Proprietary Protocols And Codecs Available That Are Not Easily Interoperable And Scalable To Implement Multipoint Videoconference Systems. WebRTC (Web Real-Time Communication) Is A State-of-the-Art Open Technology That Makes Real-time Communication Capabilities In Audio, Video, And Data Transmission Possible In Real-time Communication Through Web Browsers Using JavaScript APIs (Application Programming Interfaces) Without Plug-ins. This Paper Aims To Introduce The P2P Video Conferencing System Based On Web Real-Time Communication (WebRTC). In This Paper, We Have Proposed A Web-based Peer-to-peer Real-time Communication System Using The Mozilla Firefox Together With The ScaleDrone Service That Enables Users To Communicate With Highspeed Data Transmission Over The Communication Channel Using WebRTC Technology, HTML5 And Use Node.js Server Address. Our Experiments Show That WebRTC Is A Capable Building Block For Scalable Live Video Conferencing Within A Web Browser.
Recent Developments In The Speed Of The Internet And Information Technology Have Made The Rapid Exchange Of Multimedia Information Possible. However, These Developments In Technology Lead To Violations Of Information Security And Private Information. Digital Steganography Provides The Ability To Protect Private Information That Has Become Essential In The Current Internet Age. Among All Digital Media, Digital Video Has Become Of Interest To Many Researchers Due To Its High Capacity For Hiding Sensitive Data. Numerous Video Steganography Methods Have Recently Been Proposed To Prevent Secret Data From Being Stolen. Nevertheless, These Methods Have Multiple Issues Related To Visual Imperceptibly, Robustness, And Embedding Capacity. To Tackle These Issues, This Paper Proposes A New Approach To Video Steganography Based On The Corner Point Principle And LSBs Algorithm. The Proposed Method First Uses Shi-Tomasi Algorithm To Detect Regions Of Corner Points Within The Cover Video Frames. Then, It Uses 4-LSBs Algorithm To Hide Confidential Data Inside The Identified Corner Points. Besides, Before The Embedding Process, The Proposed Method Encrypts Confidential Data Using Arnold’s Cat Map Method To Boost The Security Level.
The Security Of Any Public Key Cryptosystem Depends On The Private Key Thus, It Is Important That Only An Authorized Person Can Have Access To The Private Key. The Paper Presents A New Algorithm That Protects The Private Key Using The Transposition Cipher Technique. The Performance Of The Proposed Technique Is Evaluated By Applying It In The RSA Algorithm’s Generated Private Keys Using 512-bit, 1024-bit, And 2048-bit, Respectively. The Result Shows That The Technique Is Practical And Efficient In Securing Private Keys While In Storage As It Produced High Avalanche Effect.
Initially The Barcodes Have Been Widely Used For The Unique Identification Of The Products. Quick Response I.e. QR Codes Are 2D Representation Of Barcodes That Can Embed Text, Audio, Video, Web URL, Phone Contacts, Credentials ¬¬and Much More. This Paper Primarily Deals With The Generation Of QR Codes For Question Paper. We Have Proposed Encryption Of Question Paper Data Using AES Encryption Algorithm. The Working Of The QR Codes Is Based On Encrypting It To QR Code And Scanning To Decrypt It. Furthermore, We Have Reduced The Memory Storage By Redirecting To A Webpage Through The Transmission And Online Acceptance Of Data.
Communication Technology Has Completely Occupied All The Areas Of Applications. Last Decade Has However Witnessed A Drastic Evolution In Information And Communication Technology Due To The Introduction Of Social Media Network. Business Growth Is Further Achieved Via These Social Media. Nevertheless, Increase In The Usage Of Online Social Networks (OSN) Such As Face Book, Twitter, Instagrametc Has However Led To The Increase In Privacy And Security Concerns. Third Party Applications Are One Of The Many Reasons For Facebook Attractiveness. Regrettably, The Users Are Unaware Of Detail That A Lot Of Malicious Facebook Applications Provide On Their Profile. The Popularity Of These Third Party Applications Is Such That There Are Almost 20 Million Installations Per Day. But Cyber Criminals Have Appreciated The Popularity Of Third Party Applications And The Possibility Of Using These Apps For Distributing The Malware And Spam. This Paper Proposes A Method To Categorize A Given Application As Malicious Or Safe By Using FRAppE (Facebook’s Rigorous Application Evaluator), Possibly One Of The First Tool For Detecting Malicious Apps On The Facebook. To Develop The FRAppE, The Data Is Gathered From MyPagekeeper Application, A Website That Provides Significant Information About Various Third Party Applications And Their Insight Into Their Behavior.
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