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 201–250 of 800 papers

TitleStatusHype
A Survey for Deep Reinforcement Learning Based Network Intrusion Detection—0
TEAM: Temporal Adversarial Examples Attack Model against Network Intrusion Detection System Applied to RNN—0
Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space—0
Fair Anomaly Detection For Imbalanced Groups—0
Federated Learning in Adversarial Environments: Testbed Design and Poisoning Resilience in Cybersecurity—0
Towards a graph-based foundation model for network traffic analysis—0
A Novel Perturb-ability Score to Mitigate Evasion Adversarial Attacks on Flow-Based ML-NIDS—0
SDOoop: Capturing Periodical Patterns and Out-of-phase Anomalies in Streaming Data AnalysisCode0
C-RADAR: A Centralized Deep Learning System for Intrusion Detection in Software Defined Networks—0
AI-Driven Intrusion Detection Systems (IDS) on the ROAD Dataset: A Comparative Analysis for Automotive Controller Area Network (CAN)—0
Systematic Evaluation of Synthetic Data Augmentation for Multi-class NetFlow Traffic—0
Enhancing Intrusion Detection in IoT Environments: An Advanced Ensemble Approach Using Kolmogorov-Arnold Networks—0
Beyond Detection: Leveraging Large Language Models for Cyber Attack Prediction in IoT Networks—0
Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey—0
Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine LearningCode0
Towards Explainable Network Intrusion Detection using Large Language Models—0
AI-Driven Chatbot for Intrusion Detection in Edge Networks: Enhancing Cybersecurity with Ethical User Consent—0
Preliminary study on artificial intelligence methods for cybersecurity threat detection in computer networks based on raw data packets—0
A Life-long Learning Intrusion Detection System for 6G-Enabled IoV—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
Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach—0
Integrating Artificial Intelligence into Operating Systems: A Comprehensive Survey on Techniques, Applications, and Future Directions—0
Impacts of Data Preprocessing and Hyperparameter Optimization on the Performance of Machine Learning Models Applied to Intrusion Detection Systems—0
A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes—0
Multi-agent Reinforcement Learning-based Network Intrusion Detection System—0
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
Show:102550
← PrevPage 5 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