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

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Showing 1–10 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
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All datasetsCICIDS2017CIC-DDoSCIC-DoSUNSW-NB15

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95—Unverified