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 751–800 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
EagerNet: Early Predictions of Neural Networks for Computationally Efficient Intrusion DetectionCode0
CARACAS: vehiCular ArchitectuRe for detAiled Can Attacks SimulationCode0
A Generative Model Based Honeypot for Industrial OPC UA CommunicationCode0
Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification SystemsCode0
Benchmarking Unsupervised Online IDS for Masquerade Attacks in CANCode0
SSCL-IDS: Enhancing Generalization of Intrusion Detection with Self-Supervised Contrastive LearningCode0
Walling up Backdoors in Intrusion Detection SystemsCode0
Take Package as Language: Anomaly Detection Using TransformerCode0
Recomposition vs. Prediction: A Novel Anomaly Detection for Discrete Events Based On AutoencoderCode0
Intrusion Detection Using Mouse DynamicsCode0
Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion DetectionCode0
Towards Reliable Rare Category Analysis on Graphs via Individual CalibrationCode0
OptIForest: Optimal Isolation Forest for Anomaly DetectionCode0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic DataCode0
Self-Organizing Map assisted Deep Autoencoding Gaussian Mixture Model for Intrusion DetectionCode0
Diffusion-based Adversarial Purification for Intrusion DetectionCode0
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly DetectionCode0
Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection SystemsCode0
Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection SystemsCode0
X-CBA: Explainability Aided CatBoosted Anomal-E for Intrusion Detection SystemCode0
Deep Learning Applications for Intrusion Detection in Network TrafficCode0
Reliable Malware Analysis and Detection using Topology Data AnalysisCode0
Evaluating the Performance of Machine Learning-Based Classification Models for IoT Intrusion DetectionCode0
Separating Flows in Encrypted Tunnel TrafficCode0
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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