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 226–250 of 800 papers

TitleStatusHype
A Critical Assessment of Interpretable and Explainable Machine Learning for Intrusion Detection—0
Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks—0
Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV—0
AntibotV: A Multilevel Behaviour-based Framework for Botnets Detection in Vehicular Networks—0
Diffusion-based Adversarial Purification for Intrusion DetectionCode0
Benchmarking Unsupervised Online IDS for Masquerade Attacks in CANCode0
PPT-GNN: A Practical Pre-Trained Spatio-Temporal Graph Neural Network for Network Security—0
A Cutting-Edge Deep Learning Method For Enhancing IoT Security—0
Feasibility of Non-Line-of-Sight Integrated Sensing and Communication at mmWave—0
Let the Noise Speak: Harnessing Noise for a Unified Defense Against Adversarial and Backdoor AttacksCode0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
Enhanced Intrusion Detection System for Multiclass Classification in UAV Networks—0
Detection-Rate-Emphasized Multi-objective Evolutionary Feature Selection for Network Intrusion Detection—0
Efficient Network Traffic Feature Sets for IoT Intrusion Detection—0
CARACAS: vehiCular ArchitectuRe for detAiled Can Attacks SimulationCode0
SSCL-IDS: Enhancing Generalization of Intrusion Detection with Self-Supervised Contrastive LearningCode0
Sequential Binary Classification for Intrusion Detection—0
Novel Approach to Intrusion Detection: Introducing GAN-MSCNN-BILSTM with LIME Predictions—0
Individual Packet Features are a Risk to Model Generalisation in ML-Based Intrusion DetectionCode0
Generative AI-in-the-loop: Integrating LLMs and GPTs into the Next Generation Networks—0
Strengthening Network Intrusion Detection in IoT Environments with Self-Supervised Learning and Few Shot Learning—0
A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AI—0
Optimizing cnn-Bigru performance: Mish activation and comparative analysis with Relu—0
Enhancing IoT Security with CNN and LSTM-Based Intrusion Detection Systems—0
Survey of Graph Neural Network for Internet of Things and NextG Networks—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