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 301–350 of 800 papers

TitleStatusHype
Efficient Intrusion Detection Using Evidence Theory—0
Efficient IoT Intrusion Detection with an Improved Attention-Based CNN-BiLSTM Architecture—0
Efficient Network Representation for GNN-based Intrusion Detection—0
Efficient Network Traffic Feature Sets for IoT Intrusion Detection—0
EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning—0
A Review of Machine Learning based Anomaly Detection Techniques—0
EMO\&LY (EMOtion and AnomaLY) : A new corpus for anomaly detection in an audiovisual stream with emotional context.—0
A Lightweight Multi-Attack CAN Intrusion Detection System on Hybrid FPGAs—0
End-to-End Adversarial Learning for Intrusion Detection in Computer Networks—0
End-To-End Anomaly Detection for Identifying Malicious Cyber Behavior through NLP-Based Log Embeddings—0
Exploring Global and Local Information for Anomaly Detection with Normal Samples—0
Energy-based Models for Video Anomaly Detection—0
Enhanced Few-shot Learning for Intrusion Detection in Railway Video Surveillance—0
Enhanced Intrusion Detection in IIoT Networks: A Lightweight Approach with Autoencoder-Based Feature Learning—0
Enhanced Intrusion Detection System for Multiclass Classification in UAV Networks—0
Enhanced network anomaly detection based on deep neural networks—0
Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN Autoencoder Approach for Spectral Intrusion Analysis—0
Enhancing Cohesion and Coherence of Fake Text to Improve Believability for Deceiving Cyber Attackers—0
Utilizing Deep Learning for Enhancing Network Resilience in Finance—0
Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks—0
Enhancing Intrusion Detection In Internet Of Vehicles Through Federated Learning—0
Enhancing Intrusion Detection in IoT Environments: An Advanced Ensemble Approach Using Kolmogorov-Arnold Networks—0
Enhancing IoT Security: A Novel Feature Engineering Approach for ML-Based Intrusion Detection Systems—0
Enhancing IoT Security with CNN and LSTM-Based Intrusion Detection Systems—0
DI-NIDS: Domain Invariant Network Intrusion Detection System—0
Enhancing sensor attack detection in supervisory control systems modeled by probabilistic automata—0
Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification—0
Ensemble Classifier Design Tuned to Dataset Characteristics for Network Intrusion Detection—0
Ensemble learning techniques for intrusion detection system in the context of cybersecurity—0
EPASAD: Ellipsoid decision boundary based Process-Aware Stealthy Attack Detector—0
A review of Federated Learning in Intrusion Detection Systems for IoT—0
Are Trees Really Green? A Detection Approach of IoT Malware Attacks—0
Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges—0
Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking—0
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data—0
An Anomaly Detection System Based on Generative Classifiers for Controller Area Network—0
A Transformer-Based Framework for Payload Malware Detection and Classification—0
Evaluating the Robustness of Time Series Anomaly and Intrusion Detection Methods against Adversarial Attacks—0
Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs—0
Evaluation of Machine Learning Algorithms for Intrusion Detection System—0
Expectations Versus Reality: Evaluating Intrusion Detection Systems in Practice—0
Experimental Review of Neural-based approaches for Network Intrusion Management—0
Detect & Reject for Transferability of Black-box Adversarial Attacks Against Network Intrusion Detection Systems—0
Explainable AI-based Intrusion Detection System for Industry 5.0: An Overview of the Literature, associated Challenges, the existing Solutions, and Potential Research Directions—0
Automating Privilege Escalation with Deep Reinforcement Learning—0
Explainable and Optimally Configured Artificial Neural Networks for Attack Detection in Smart Homes—0
Explainable Intrusion Detection Systems Using Competitive Learning Techniques—0
Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities—0
Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems—0
A Lightweight IDS for Early APT Detection Using a Novel Feature Selection Method—0
Show:102550
← PrevPage 7 of 16Next →
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