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 101–150 of 800 papers

TitleStatusHype
K-Metamodes: frequency- and ensemble-based distributed k-modes clustering for security analyticsCode0
Learning Neural Representations for Network Anomaly DetectionCode0
Reliable Malware Analysis and Detection using Topology Data AnalysisCode0
Intrusion Detection In Computer Networks Using Machine Learning AlgorithmsCode0
Intrusion Detection Using Mouse DynamicsCode0
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion DetectionCode0
Implementing Lightweight Intrusion Detection System on Resource Constrained DevicesCode0
Individual Packet Features are a Risk to Model Generalisation in ML-Based Intrusion DetectionCode0
A Comprehensive Comparative Study of Individual ML Models and Ensemble Strategies for Network Intrusion Detection SystemsCode0
Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved FeaturesCode0
Hybrid Isolation Forest - Application to Intrusion DetectionCode0
Self-Organizing Map assisted Deep Autoencoding Gaussian Mixture Model for Intrusion DetectionCode0
A Comparative Analysis of DNN-based White-Box Explainable AI Methods in Network SecurityCode0
Separating Flows in Encrypted Tunnel TrafficCode0
Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic ApproachCode0
Fragments Expert A Graphical User Interface MATLAB Toolbox for Classification of File FragmentsCode0
eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry KeysCode0
Gotham Dataset 2025: A Reproducible Large-Scale IoT Network Dataset for Intrusion Detection and Security ResearchCode0
Interpretable Sequence Classification via Discrete OptimizationCode0
Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber SecurityCode0
A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networksCode0
Evaluating the Performance of Machine Learning-Based Classification Models for IoT Intrusion DetectionCode0
Enhanced Convolution Neural Network with Optimized Pooling and Hyperparameter Tuning for Network Intrusion DetectionCode0
Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection SystemsCode0
Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based modelCode0
EagerNet: Early Predictions of Neural Networks for Computationally Efficient Intrusion DetectionCode0
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly DetectionCode0
Diffusion-based Adversarial Purification for Intrusion DetectionCode0
Arhuaco: Deep Learning and Isolation Based Security for Distributed High-Throughput ComputingCode0
Detecting message modification attacks on the CAN bus with Temporal Convolutional NetworksCode0
Deep Learning Applications for Intrusion Detection in Network TrafficCode0
Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced DatasetsCode0
Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion DetectionCode0
CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat DetectionCode0
CML-IDS: Enhancing Intrusion Detection in SDN through Collaborative Machine LearningCode0
A Renewal Model of IntrusionCode0
Are Existing Out-Of-Distribution Techniques Suitable for Network Intrusion Detection?Code0
Adaptive Pruning of Deep Neural Networks for Resource-Aware Embedded Intrusion Detection on the EdgeCode0
Convolutional Neural Network-based Intrusion Detection System for AVTP Streams in Automotive Ethernet-based NetworksCode0
Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification SystemsCode0
Data Distribution ValuationCode0
Behavioural Reports of Multi-Stage MalwareCode0
Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternativesCode0
LuNet: A Deep Neural Network for Network Intrusion DetectionCode0
A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection SystemsCode0
A Robust PPO-optimized Tabular Transformer Framework for Intrusion Detection in Industrial IoT SystemsCode0
Benchmarking Unsupervised Online IDS for Masquerade Attacks in CANCode0
CARACAS: vehiCular ArchitectuRe for detAiled Can Attacks SimulationCode0
Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine LearningCode0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
Show:102550
← PrevPage 3 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