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Papers tagged with "Intrusion Detection"
A Privacy-Preserving Federated Learning Framework With Dual Explainability For Hybrid CNN–LSTM Based Anomaly Detection In IoT Network Traffic
Intrusion Detection Systems (IDS) For The Internet Of Things Face A Three-way Tension: Deep Models Detect Attacks Accurately But Behave As Opaque Black Boxes, Centralised Training Pipelines Require Raw Traffic To Be Exported From Every Devi...
Federated Learning And Explainable Artificial Intelligence For Intrusion Detection In IoT Networks: A Review
The Rapid Enlargement Of The Internet Of Things Has Turned Billions Of Weakly Protected Devices Into An Attractive Attack Surface, And Machine-learning Intrusion Detection Has Become The Default Defensive Answer. Three Difficulties, However...
AI-POWERED INTELLIGENT FRAMEWORK FOR DETECTION AND PREVENTION OF CYBERSECURITY ATTACKS USING MACHINE LEARNING ALGORITHMS
This Paper Proposes An AI-powered Cybersecurity Framework Integrating Machine Learning, Deep Neural Networks, Real-time Network Monitoring, Automated Alerting, And Dashboard-driven Response For Enterprise Threat Detection And Prevention. Th...
AI-Powered Intelligent Framework For Detection And Prevention Of Cybersecurity Attacks Using Machine Learning Algorithms
The Rapid Growth Of Digital Technologies Has Substantially Increased Exposure To Cybersecurity Threats Including Malware, Phishing, Ransomware, And Unauthorized Network Intrusions. Traditional Signature-based Security Systems Are Inherently...
REAL –TIME AI INTRUSION DETECTION SYSTEM USING MACHINE LEARNING
The Relentless Escalation Of Cyber Threats In Contemporary Network Environments Has Necessitated A Fundamental Rethinking Of Intrusion Detection Methodologies. Traditional Signature-based Systems, While Once Sufficient, Have Proven Structur...
Intelligent Sql Injection Detection Using CSSGL
SQL Injection Remains One Of The Most Critical Security Threats To Web Applications, Enabling Attackers To Manipulate Database Queries And Gain Unauthorized Access To Sensitive Information. Traditional Detection Methods Often Fail To Identi...
HYBRID MACHINE LEARNING AND DEEP LEARNING-BASED VPN NETWORK TRAFFIC ANOMALY DETECTION SYSTEM
The Rapid Growth Of Internet Usage And The Widespread Adoption Of Virtual Private Networks (VPNs) Have Significantly Enhanced Secure Communication, But Have Also Introduced New Challenges In Detecting Cyber Threats Hidden Within Encrypted T...
Wifi Deauthendication Detector
This Paper Presents A Low-cost Wi-Fi Security Monitoring System Designed To Detect Deauthentication Attacks In Real Time Using The ESP8266 NodeMCU Microcontroller. The Increasing Reliance On Wireless Networks Has Made Network Security An Ur...
TRANSGUARD-RT: A REAL-TIME TRANSFORMER-DRIVEN INTRUSION DETECTION SYSTEM USING LIVE NETWORK TRAFFIC ANALYTICS
The Rapid Expansion Of Internet-based Communication And Interconnected Digital Infrastructures Has Significantly Increased Exposure To Advanced Cyber Threats, Including Malware Infiltration, Phishing Attempts, Denial-of-service Attacks, And...
Cyber Threat Intelligence System Using CNN-LSTM Deep Learning Model
Cyber Threat IntelligenceThe Rapid Evolution Of Cyber Threats, Including Malware, Phishing Attacks, Distributed Denial Of Service (DDoS), And Advanced Persistent Threats, Has Made Traditional Security Mechanisms Less Effective. Conventional...
Smart Cybersecurity Intrusion Detection & Prevention System Using AI (SecurityHub)
This Project Examines The Combination Of Network-level Security And Physical Biometric Verification Within A Unified, Hardware-accelerated Edge Environment. Conventional Security Setups Frequently Struggle With Operational Slowdowns, Primar...
Intelligent Network Traffic Prediction Using Graph Neural Networks
The Widespread Deployment Of Wireless Communication Technology Has Made The Wi-Fi Network More Susceptible To Sophisticated Cyber-attacks, Which Include De-authentication Attacks, Rogue Access Points, Traffic Flooding, And Protocol Misuse....
AI-DRIVEN SECURE NETWORK AUTHENTICATION WITH SPATIAL TRUST VALIDATION
This Paper Presents An AI-Driven Secure Network Authentication System With Spatial Trust Validation To Enhance Traditional Login Security Using Machine Learning And Generative AI Techniques. During Registration, The System Stores Baseline A...
Machine Learning Approaches For Intelligent Intrusion Detection Systems In Cloud Networks
Cloud Computing Has Become A Fundamental Platform For Storing Data And Running Applications Due To Its Scalability, Flexibility, And Cost Efficiency. However, The Rapid Growth Of Cloud Environments Has Also Increased Security Threats Such A...
Cyber Intrusion Detection Using Machine Learning Techniques
Cyber Intrusions Are Becoming Increasingly Complex And Sophisticated, Making Them Difficult To Detect Using Conventional Rule-based And Signature-driven Security Mechanisms. The Rapid Growth Of Network Traffic And Evolving Attack Patterns D...