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 151–175 of 800 papers

TitleStatusHype
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data—0
PCAP-Backdoor: Backdoor Poisoning Generator for Network Traffic in CPS/IoT Environments—0
Enhanced Intrusion Detection in IIoT Networks: A Lightweight Approach with Autoencoder-Based Feature Learning—0
Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models—0
PolyLUT: Ultra-low Latency Polynomial Inference with Hardware-Aware Structured Pruning—0
A Comparative Analysis of DNN-based White-Box Explainable AI Methods in Network SecurityCode0
CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks—0
Cyber Shadows: Neutralizing Security Threats with AI and Targeted Policy Measures—0
BARTPredict: Empowering IoT Security with LLM-Driven Cyber Threat Prediction—0
LENS-XAI: Redefining Lightweight and Explainable Network Security through Knowledge Distillation and Variational Autoencoders for Scalable Intrusion Detection in Cybersecurity—0
Collaborative Approaches to Enhancing Smart Vehicle Cybersecurity by AI-Driven Threat Detection—0
Learning in Multiple Spaces: Few-Shot Network Attack Detection with Metric-Fused Prototypical Networks—0
An Anomaly Detection System Based on Generative Classifiers for Controller Area Network—0
PowerRadio: Manipulate Sensor Measurementvia Power GND Radiation—0
A Temporal Convolutional Network-based Approach for Network Intrusion Detection—0
Flow Exporter Impact on Intelligent Intrusion Detection Systems—0
Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks—0
Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies—0
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model SelectionCode0
Distributed Intrusion Detection System using Semantic-based Rules for SCADA in Smart Grid—0
Applications of Positive Unlabeled (PU) and Negative Unlabeled (NU) Learning in Cybersecurity—0
Machine Learning-based Android Intrusion Detection System—0
Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond—0
Optimized IoT Intrusion Detection using Machine Learning Technique—0
Graph-Powered Defense: Controller Area Network Intrusion Detection for Unmanned Aerial Vehicles—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