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 776–800 of 800 papers

TitleStatusHype
EagerNet: Early Predictions of Neural Networks for Computationally Efficient Intrusion DetectionCode0
CARACAS: vehiCular ArchitectuRe for detAiled Can Attacks SimulationCode0
A Generative Model Based Honeypot for Industrial OPC UA CommunicationCode0
Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification SystemsCode0
Benchmarking Unsupervised Online IDS for Masquerade Attacks in CANCode0
SSCL-IDS: Enhancing Generalization of Intrusion Detection with Self-Supervised Contrastive LearningCode0
Walling up Backdoors in Intrusion Detection SystemsCode0
Take Package as Language: Anomaly Detection Using TransformerCode0
Recomposition vs. Prediction: A Novel Anomaly Detection for Discrete Events Based On AutoencoderCode0
Intrusion Detection Using Mouse DynamicsCode0
Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion DetectionCode0
Towards Reliable Rare Category Analysis on Graphs via Individual CalibrationCode0
OptIForest: Optimal Isolation Forest for Anomaly DetectionCode0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic DataCode0
Self-Organizing Map assisted Deep Autoencoding Gaussian Mixture Model for Intrusion DetectionCode0
Diffusion-based Adversarial Purification for Intrusion DetectionCode0
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly DetectionCode0
Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection SystemsCode0
Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection SystemsCode0
X-CBA: Explainability Aided CatBoosted Anomal-E for Intrusion Detection SystemCode0
Deep Learning Applications for Intrusion Detection in Network TrafficCode0
Reliable Malware Analysis and Detection using Topology Data AnalysisCode0
Evaluating the Performance of Machine Learning-Based Classification Models for IoT Intrusion DetectionCode0
Separating Flows in Encrypted Tunnel TrafficCode0
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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