SOTAVerified

Intrusion Detection

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.

Source: Machine Learning Techniques for Intrusion Detection

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–50 of 800 papers

TitleStatusHype
CyberRAG: An agentic RAG cyber attack classification and reporting tool—0
Detection of Cyber Attack in Network using Machine Learning Techniques.—0
Generative Adversarial Evasion and Out-of-Distribution Detection for UAV Cyber-Attacks—0
Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysis—0
KnowML: Improving Generalization of ML-NIDS with Attack Knowledge Graphs—0
Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017—0
Dynamic Temporal Positional Encodings for Early Intrusion Detection in IoT—0
On the Performance of Cyber-Biomedical Features for Intrusion Detection in Healthcare 5.0—0
A Lightweight IDS for Early APT Detection Using a Novel Feature Selection Method—0
Assessing the Resilience of Automotive Intrusion Detection Systems to Adversarial Manipulation—0
Are Trees Really Green? A Detection Approach of IoT Malware Attacks—0
Fuse and Federate: Enhancing EV Charging Station Security with Multimodal Fusion and Federated Learning—0
Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning—0
A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges—0
A Systematic Review of Metaheuristics-Based and Machine Learning-Driven Intrusion Detection Systems in IoT—0
INSIGHT: A Survey of In-Network Systems for Intelligent, High-Efficiency AI and Topology Optimization—0
Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications—0
A Robust PPO-optimized Tabular Transformer Framework for Intrusion Detection in Industrial IoT SystemsCode0
A Scalable Hierarchical Intrusion Detection System for Internet of Vehicles—0
Neuromorphic Mimicry Attacks Exploiting Brain-Inspired Computing for Covert Cyber Intrusions—0
SecCAN: An Extended CAN Controller with Embedded Intrusion DetectionCode0
CSAGC-IDS: A Dual-Module Deep Learning Network Intrusion Detection Model for Complex and Imbalanced Data—0
Adaptive Pruning of Deep Neural Networks for Resource-Aware Embedded Intrusion Detection on the EdgeCode0
A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network—0
Adaptive Security Policy Management in Cloud Environments Using Reinforcement Learning—0
Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection SystemsCode0
Simultaneous Intrusion Detection and Localization Using ISAC Network—0
Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced DatasetsCode0
Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems—0
Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability—0
Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking—0
Smart Water Security with AI and Blockchain-Enhanced Digital Twins—0
A Virtual Cybersecurity Department for Securing Digital Twins in Water Distribution Systems—0
Simplified and Secure MCP Gateways for Enterprise AI IntegrationCode1
Zero-Day Botnet Attack Detection in IoV: A Modular Approach Using Isolation Forests and Particle Swarm Optimization—0
Breaking the Flow and the Bank: Stealthy Cyberattacks on Water Network Hydraulics—0
Blockchain Meets Adaptive Honeypots: A Trust-Aware Approach to Next-Gen IoT Security—0
FLARE: Feature-based Lightweight Aggregation for Robust Evaluation of IoT Intrusion Detection—0
Sensor Scheduling in Intrusion Detection Games with Uncertain Payoffs—0
Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approach to Network Security—0
Deep Learning-based Intrusion Detection Systems: A Survey—0
Hybrid Temporal Differential Consistency Autoencoder for Efficient and Sustainable Anomaly Detection in Cyber-Physical Systems—0
WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systems—0
CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat DetectionCode0
Accelerating IoV Intrusion Detection: Benchmarking GPU-Accelerated vs CPU-Based ML Libraries—0
Integrated LLM-Based Intrusion Detection with Secure Slicing xApp for Securing O-RAN-Enabled Wireless Network Deployments—0
Are We There Yet? Unraveling the State-of-the-Art Graph Network Intrusion Detection Systems—0
Efficient IoT Intrusion Detection with an Improved Attention-Based CNN-BiLSTM Architecture—0
Payload-Aware Intrusion Detection with CMAE and Large Language Models—0
Robust Intrusion Detection System with Explainable Artificial Intelligence—0
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All datasetsCICIDS2017CIC-DDoSCIC-DoSUNSW-NB15

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13—Unverified
2K-Nearest NeighborsAccuracy (%)98.07—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-PCAAUC0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9—Unverified