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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 676–700 of 800 papers

TitleStatusHype
PWG-IDS: An Intrusion Detection Model for Solving Class Imbalance in IIoT Networks Using Generative Adversarial Networks—0
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection—0
Quantised Neural Network Accelerators for Low-Power IDS in Automotive Networks—0
Rallying Adversarial Techniques against Deep Learning for Network Security—0
RANK: AI-assisted End-to-End Architecture for Detecting Persistent Attacks in Enterprise Networks—0
Real-time Network Intrusion Detection via Decision Transformers—0
Real-time Regular Expression Matching—0
Real-Time Zero-Day Intrusion Detection System for Automotive Controller Area Network on FPGAs—0
Reconfigurable Edge Hardware for Intelligent IDS: Systematic Approach—0
Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection—0
REGARD: Rules of EngaGement for Automated cybeR Defense to aid in Intrusion Response—0
Reinforcement Learning for Feedback-Enabled Cyber Resilience—0
Relevant Feature Selection Model Using Data Mining for Intrusion Detection System—0
RePAD: Real-time Proactive Anomaly Detection for Time Series—0
ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series—0
Review: Deep Learning Methods for Cybersecurity and Intrusion Detection Systems—0
Review on the Feasibility of Adversarial Evasion Attacks and Defenses for Network Intrusion Detection Systems—0
RIDE: Real-time Intrusion Detection via Explainable Machine Learning Implemented in a Memristor Hardware Architecture—0
RNNIDS: Enhancing Network Intrusion Detection Systems through Deep Learning—0
Road Context-aware Intrusion Detection System for Autonomous Cars—0
A Comprehensive Guide to CAN IDS Data & Introduction of the ROAD Dataset—0
Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017—0
Robust Attack Detection Approach for IIoT Using Ensemble Classifier—0
Robust Intrusion Detection System with Explainable Artificial Intelligence—0
Robustness of ML-Enhanced IDS to Stealthy Adversaries—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