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 211220 of 800 papers

TitleStatusHype
Enhancing Intrusion Detection in IoT Environments: An Advanced Ensemble Approach Using Kolmogorov-Arnold Networks0
Systematic Evaluation of Synthetic Data Augmentation for Multi-class NetFlow Traffic0
Beyond Detection: Leveraging Large Language Models for Cyber Attack Prediction in IoT Networks0
Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey0
Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine LearningCode0
Towards Explainable Network Intrusion Detection using Large Language Models0
AI-Driven Chatbot for Intrusion Detection in Edge Networks: Enhancing Cybersecurity with Ethical User Consent0
Preliminary study on artificial intelligence methods for cybersecurity threat detection in computer networks based on raw data packets0
A Life-long Learning Intrusion Detection System for 6G-Enabled IoV0
Explainable AI-based Intrusion Detection System for Industry 5.0: An Overview of the Literature, associated Challenges, the existing Solutions, and Potential Research Directions0
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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