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

Showing 111120 of 800 papers

TitleStatusHype
Blockchain Meets Adaptive Honeypots: A Trust-Aware Approach to Next-Gen IoT Security0
FLARE: Feature-based Lightweight Aggregation for Robust Evaluation of IoT Intrusion Detection0
Sensor Scheduling in Intrusion Detection Games with Uncertain Payoffs0
Deep Learning-based Intrusion Detection Systems: A Survey0
Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approach to Network Security0
Hybrid Temporal Differential Consistency Autoencoder for Efficient and Sustainable Anomaly Detection in Cyber-Physical Systems0
WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systems0
CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat DetectionCode0
Accelerating IoV Intrusion Detection: Benchmarking GPU-Accelerated vs CPU-Based ML Libraries0
Integrated LLM-Based Intrusion Detection with Secure Slicing xApp for Securing O-RAN-Enabled Wireless Network Deployments0
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Benchmark Results

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