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 751–775 of 800 papers

TitleStatusHype
Implementing Lightweight Intrusion Detection System on Resource Constrained DevicesCode0
Convolutional Neural Network-based Intrusion Detection System for AVTP Streams in Automotive Ethernet-based NetworksCode0
A Renewal Model of IntrusionCode0
Enhanced Convolution Neural Network with Optimized Pooling and Hyperparameter Tuning for Network Intrusion DetectionCode0
Fragments Expert A Graphical User Interface MATLAB Toolbox for Classification of File FragmentsCode0
Individual Packet Features are a Risk to Model Generalisation in ML-Based Intrusion DetectionCode0
Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based modelCode0
Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine LearningCode0
Let the Noise Speak: Harnessing Noise for a Unified Defense Against Adversarial and Backdoor AttacksCode0
A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection SystemsCode0
Synthesis of Adversarial DDOS Attacks Using Tabular Generative Adversarial NetworksCode0
SCGNet-Stacked Convolution with Gated Recurrent Unit Network for Cyber Network Intrusion Detection and Intrusion Type ClassificationCode0
XAI-based Feature Selection for Improved Network Intrusion Detection SystemsCode0
Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic ApproachCode0
Interpretable Sequence Classification via Discrete OptimizationCode0
SDOoop: Capturing Periodical Patterns and Out-of-phase Anomalies in Streaming Data AnalysisCode0
CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat DetectionCode0
SecCAN: An Extended CAN Controller with Embedded Intrusion DetectionCode0
CML-IDS: Enhancing Intrusion Detection in SDN through Collaborative Machine LearningCode0
eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry KeysCode0
Adaptive Pruning of Deep Neural Networks for Resource-Aware Embedded Intrusion Detection on the EdgeCode0
Intrusion Detection In Computer Networks Using Machine Learning AlgorithmsCode0
Secured Communication Schemes for UAVs in 5G: CRYSTALS-Kyber and IDSCode0
Sparse Bayesian approach for metric learning in latent spaceCode0
Are Existing Out-Of-Distribution Techniques Suitable for Network Intrusion Detection?Code0
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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