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 521530 of 800 papers

TitleStatusHype
Preliminary study on artificial intelligence methods for cybersecurity threat detection in computer networks based on raw data packets0
Prepare for Trouble and Make it Double. Supervised and Unsupervised Stacking for AnomalyBased Intrusion Detection0
Privacy-Preserving Intrusion Detection using Convolutional Neural Networks0
Probabilistic Modeling for Novelty Detection with Applications to Fraud Identification0
Protection of an information system by artificial intelligence: a three-phase approach based on behaviour analysis to detect a hostile scenario0
PWG-IDS: An Intrusion Detection Model for Solving Class Imbalance in IIoT Networks Using Generative Adversarial Networks0
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection0
Quantised Neural Network Accelerators for Low-Power IDS in Automotive Networks0
Rallying Adversarial Techniques against Deep Learning for Network Security0
RANK: AI-assisted End-to-End Architecture for Detecting Persistent Attacks in Enterprise Networks0
Show:102550
← PrevPage 53 of 80Next →

Benchmark Results

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