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 76–100 of 800 papers

TitleStatusHype
Implementing Large Quantum Boltzmann Machines as Generative AI Models for Dataset Balancing—0
Secured Communication Schemes for UAVs in 5G: CRYSTALS-Kyber and IDSCode0
Analysis of Zero Day Attack Detection Using MLP and XAI—0
Investigating Application of Deep Neural Networks in Intrusion Detection System Design—0
PCAP-Backdoor: Backdoor Poisoning Generator for Network Traffic in CPS/IoT Environments—0
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data—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
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
Continual Learning with Strategic Selection and Forgetting for Network Intrusion DetectionCode1
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
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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