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 126–150 of 800 papers

TitleStatusHype
PacketCLIP: Multi-Modal Embedding of Network Traffic and Language for Cybersecurity Reasoning—0
SoK: Knowledge is All You Need: Accelerating Last Mile Delivery for Automated Provenance-based Intrusion Detection with LLMs—0
Generative Active Adaptation for Drifting and Imbalanced Network Intrusion Detection—0
Network Anomaly Detection for IoT Using Hyperdimensional Computing on NSL-KDD—0
Intrusion Detection in IoT Networks Using Hyperdimensional Computing: A Case Study on the NSL-KDD Dataset—0
CRUPL: A Semi-Supervised Cyber Attack Detection with Consistency Regularization and Uncertainty-aware Pseudo-Labeling in Smart Grid—0
Unmasking Stealthy Attacks on Nonlinear DAE Models of Power Grids—0
Enhancing sensor attack detection in supervisory control systems modeled by probabilistic automata—0
A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems—0
Binary and Multi-Class Intrusion Detection in IoT Using Standalone and Hybrid Machine and Deep Learning Models—0
CND-IDS: Continual Novelty Detection for Intrusion Detection Systems—0
Hybrid Machine Learning Models for Intrusion Detection in IoT: Leveraging a Real-World IoT Dataset—0
Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection SystemsCode0
Machine Learning-Based Intrusion Detection and Prevention System for IIoT Smart Metering Networks: Challenges and Solutions—0
Mapping the Landscape of Generative AI in Network Monitoring and Management—0
SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection—0
Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT—0
A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare Attacks in IoT Networks—0
Detecting APT Malware Command and Control over HTTP(S) Using Contextual Summaries—0
Technical Report: Generating the WEB-IDS23 Dataset—0
Gotham Dataset 2025: A Reproducible Large-Scale IoT Network Dataset for Intrusion Detection and Security ResearchCode0
Implementing Large Quantum Boltzmann Machines as Generative AI Models for Dataset Balancing—0
Secured Communication Schemes for UAVs in 5G: CRYSTALS-Kyber and IDSCode0
Analysis of Zero Day Attack Detection Using MLP and XAI—0
Investigating Application of Deep Neural Networks in Intrusion Detection System Design—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