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 276–300 of 800 papers

TitleStatusHype
A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats—0
Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems—0
An Interpretable Generalization Mechanism for Accurately Detecting Anomaly and Identifying Networking Intrusion Techniques—0
MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network—0
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection—0
An Effective Networks Intrusion Detection Approach Based on Hybrid Harris Hawks and Multi-Layer Perceptron—0
IT Intrusion Detection Using Statistical Learning and Testbed Measurements—0
MLSTL-WSN: Machine Learning-based Intrusion Detection using SMOTETomek in WSNs—0
Utilizing Deep Learning for Enhancing Network Resilience in Finance—0
ROSpace: Intrusion Detection Dataset for a ROS2-Based Cyber-Physical SystemCode0
Multiclass Classification Procedure for Detecting Attacks on MQTT-IoT Protocol—0
Feature Selection using the concept of Peafowl Mating in IDS—0
X-CBA: Explainability Aided CatBoosted Anomal-E for Intrusion Detection SystemCode0
Effective Multi-Stage Training Model For Edge Computing Devices In Intrusion Detection—0
Past, Present, Future: A Comprehensive Exploration of AI Use Cases in the UMBRELLA IoT Testbed—0
Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction—0
Real-Time Zero-Day Intrusion Detection System for Automotive Controller Area Network on FPGAs—0
Deep Learning-based Embedded Intrusion Detection System for Automotive CAN—0
Quantised Neural Network Accelerators for Low-Power IDS in Automotive Networks—0
Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN—0
A Lightweight Multi-Attack CAN Intrusion Detection System on Hybrid FPGAs—0
A Lightweight FPGA-based IDS-ECU Architecture for Automotive CAN—0
Eclectic Rule Extraction for Explainability of Deep Neural Network based Intrusion Detection Systems—0
Deep Learning Applications for Intrusion Detection in Network TrafficCode0
Improving Intrusion Detection with Domain-Invariant Representation Learning in Latent Space—0
Show:102550
← PrevPage 12 of 32Next →
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