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 171–180 of 800 papers

TitleStatusHype
AutoIDS: Auto-encoder Based Method for Intrusion Detection System—0
Automating Privilege Escalation with Deep Reinforcement Learning—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
A Virtual Cybersecurity Department for Securing Digital Twins in Water Distribution Systems—0
Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks—0
BARTPredict: Empowering IoT Security with LLM-Driven Cyber Threat Prediction—0
BayBFed: Bayesian Backdoor Defense for Federated Learning—0
Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection—0
Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection—0
Blockchain Large Language Models—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