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Intrusion Detection
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Intrusion Detection is the process of dynamically monitoring events occurring in a computer system or network, analyzing them for signs of possible incidents and often interdicting the unauthorized access. This is typically accomplished by automatically collecting information from a variety of systems and network sources, and then analyzing the information for possible security problems.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Generative Adversarial Evasion and Out-of-Distribution Detection for UAV Cyber-Attacks
Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysis
KnowML: Improving Generalization of ML-NIDS with Attack Knowledge Graphs
Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017
Dynamic Temporal Positional Encodings for Early Intrusion Detection in IoT
On the Performance of Cyber-Biomedical Features for Intrusion Detection in Healthcare 5.0
A Lightweight IDS for Early APT Detection Using a Novel Feature Selection Method
Assessing the Resilience of Automotive Intrusion Detection Systems to Adversarial Manipulation
Are Trees Really Green? A Detection Approach of IoT Malware Attacks
Fuse and Federate: Enhancing EV Charging Station Security with Multimodal Fusion and Federated Learning
Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning
A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges
A Systematic Review of Metaheuristics-Based and Machine Learning-Driven Intrusion Detection Systems in IoT
INSIGHT: A Survey of In-Network Systems for Intelligent, High-Efficiency AI and Topology Optimization
Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications
A Robust PPO-optimized Tabular Transformer Framework for Intrusion Detection in Industrial IoT Systems
A Scalable Hierarchical Intrusion Detection System for Internet of Vehicles
Neuromorphic Mimicry Attacks Exploiting Brain-Inspired Computing for Covert Cyber Intrusions
CSAGC-IDS: A Dual-Module Deep Learning Network Intrusion Detection Model for Complex and Imbalanced Data
Adaptive Pruning of Deep Neural Networks for Resource-Aware Embedded Intrusion Detection on the Edge
SecCAN: An Extended CAN Controller with Embedded Intrusion Detection
A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network
Adaptive Security Policy Management in Cloud Environments Using Reinforcement Learning
Simultaneous Intrusion Detection and Localization Using ISAC Network
Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems
Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced Datasets
Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems
Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability
Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking
Simplified and Secure MCP Gateways for Enterprise AI Integration
A Virtual Cybersecurity Department for Securing Digital Twins in Water Distribution Systems
Smart Water Security with AI and Blockchain-Enhanced Digital Twins
Zero-Day Botnet Attack Detection in IoV: A Modular Approach Using Isolation Forests and Particle Swarm Optimization
Breaking the Flow and the Bank: Stealthy Cyberattacks on Water Network Hydraulics
Blockchain Meets Adaptive Honeypots: A Trust-Aware Approach to Next-Gen IoT Security
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