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
Analysis of Zero Day Attack Detection Using MLP and XAI—0
A SVM and K-means clustering based fast and efficient intrusion detection system—0
A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes—0
Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System—0
A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems—0
A Compendium on Network and Host based Intrusion Detection Systems—0
Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer—0
1D CNN Based Network Intrusion Detection with Normalization on Imbalanced Data—0
A survey on deep packet inspection for intrusion detection systems—0
Enhanced network anomaly detection based on deep neural networks—0
Enhanced Intrusion Detection System for Multiclass Classification in UAV Networks—0
A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network—0
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection—0
Enhanced Intrusion Detection in IIoT Networks: A Lightweight Approach with Autoencoder-Based Feature Learning—0
Enhanced Few-shot Learning for Intrusion Detection in Railway Video Surveillance—0
Energy-based Models for Video Anomaly Detection—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
A Survey for Deep Reinforcement Learning Based Network Intrusion Detection—0
Enhancing sensor attack detection in supervisory control systems modeled by probabilistic automata—0
An Adversarial Approach for Explainable AI in Intrusion Detection Systems—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
End-To-End Anomaly Detection for Identifying Malicious Cyber Behavior through NLP-Based Log Embeddings—0
End-to-End Adversarial Learning for Intrusion Detection in Computer Networks—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
Assessment of the Relative Importance of different hyper-parameters of LSTM for an IDS—0
EMO\&LY (EMOtion and AnomaLY) : A new corpus for anomaly detection in an audiovisual stream with emotional context.—0
Assessing Cyclostationary Malware Detection via Feature Selection 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
An Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attacks—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
EG-ConMix: An Intrusion Detection Method based on Graph Contrastive 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
ASNM Datasets: A Collection of Network Traffic Features for Testing of Adversarial Classifiers and Network Intrusion Detectors—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