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 101–125 of 800 papers

TitleStatusHype
Simultaneous Intrusion Detection and Localization Using ISAC Network—0
Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection SystemsCode0
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
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
Deep Learning-based Intrusion Detection Systems: A Survey—0
Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approach to Network Security—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
Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems—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