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 271–280 of 800 papers

TitleStatusHype
An Efficient Anomaly Detection Approach using Cube Sampling with Streaming Data—0
BARTPredict: Empowering IoT Security with LLM-Driven Cyber Threat Prediction—0
Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks—0
An Effective Networks Intrusion Detection Approach Based on Hybrid Harris Hawks and Multi-Layer Perceptron—0
Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems—0
A concise method for feature selection via normalized frequencies—0
Accelerating IoV Intrusion Detection: Benchmarking GPU-Accelerated vs CPU-Based ML Libraries—0
A Virtual Cybersecurity Department for Securing Digital Twins in Water Distribution Systems—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
An Autonomous Intrusion Detection System Using an Ensemble of Advanced Learners—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