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 151–160 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
A Comparative Analysis of DNN-based White-Box Explainable AI Methods in Network SecurityCode0
PolyLUT: Ultra-low Latency Polynomial Inference with Hardware-Aware Structured Pruning—0
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
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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