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

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Showing 251–300 of 800 papers

TitleStatusHype
KiNETGAN: Enabling Distributed Network Intrusion Detection through Knowledge-Infused Synthetic Data Generation—0
Strategic Deployment of Honeypots in Blockchain-based IoT Systems—0
Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities—0
Practical Performance of a Distributed Processing Framework for Machine-Learning-based NIDS—0
StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems—0
Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection—0
Large Language Models for Cyber Security: A Systematic Literature ReviewCode0
Systematic Review: Anomaly Detection in Connected and Autonomous Vehicles—0
Triadic-OCD: Asynchronous Online Change Detection with Provable Robustness, Optimality, and Convergence—0
Enhancing IoT Security: A Novel Feature Engineering Approach for ML-Based Intrusion Detection Systems—0
Multi-stage Attack Detection and Prediction Using Graph Neural Networks: An IoT Feasibility Study—0
Feature Distribution Shift Mitigation with Contrastive Pretraining for Intrusion Detection—0
Intrusion Detection at Scale with the Assistance of a Command-line Language Model—0
LEMDA: A Novel Feature Engineering Method for Intrusion Detection in IoT Systems—0
Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection—0
Privacy-Preserving Intrusion Detection using Convolutional Neural Networks—0
Reconfigurable Edge Hardware for Intelligent IDS: Systematic Approach—0
An incremental hybrid adaptive network-based IDS in Software Defined Networks to detect stealth attacks—0
Dealing with Imbalanced Classes in Bot-IoT Dataset—0
A Transformer-Based Framework for Payload Malware Detection and Classification—0
Expectations Versus Reality: Evaluating Intrusion Detection Systems in Practice—0
EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning—0
Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems—0
usfAD Based Effective Unknown Attack Detection Focused IDS Framework—0
Hierarchical Classification for Intrusion Detection System: Effective Design and Empirical Analysis—0
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
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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