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 201–225 of 800 papers

TitleStatusHype
1D CNN Based Network Intrusion Detection with Normalization on Imbalanced Data—0
An Identification System Using Eye Detection Based On Wavelets And Neural Networks—0
Change Detection in Noisy Dynamic Networks: A Spectral Embedding Approach—0
Characterization of Neural Networks Automatically Mapped on Automotive-grade Microcontrollers—0
Clustering Algorithm to Detect Adversaries in Federated Learning—0
An Intelligent Mechanism for Monitoring and Detecting Intrusions in IoT Devices—0
CND-IDS: Continual Novelty Detection for Intrusion Detection Systems—0
CoAP-DoS: An IoT Network Intrusion Dataset—0
An Interpretable Generalization Mechanism for Accurately Detecting Anomaly and Identifying Networking Intrusion Techniques—0
Collaborative Approaches to Enhancing Smart Vehicle Cybersecurity by AI-Driven Threat Detection—0
Cyber Shadows: Neutralizing Security Threats with AI and Targeted Policy Measures—0
Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network—0
Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies—0
Conformalized density- and distance-based anomaly detection in time-series data—0
Data Mining with Big Data in Intrusion Detection Systems: A Systematic Literature Review—0
Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systems—0
A survey on deep packet inspection for intrusion detection systems—0
A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network—0
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection—0
Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation—0
Anomaly Detection Dataset for Industrial Control Systems—0
Convolutional Neural Network for Intrusion Detection System In Cyber Physical Systems—0
Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond—0
C-RADAR: A Centralized Deep Learning System for Intrusion Detection in Software Defined Networks—0
A Survey for Deep Reinforcement Learning Based Network Intrusion Detection—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