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 676–700 of 800 papers

TitleStatusHype
Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection—0
Adaptive Bi-Recommendation and Self-Improving Network for Heterogeneous Domain Adaptation-Assisted IoT Intrusion Detection—0
Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models—0
Adaptive Security Policy Management in Cloud Environments Using Reinforcement Learning—0
ADASYN-Random Forest Based Intrusion Detection Model—0
A DDoS-Aware IDS Model Based on Danger Theory and Mobile Agents—0
A Deep Belief Network Based Machine Learning System for Risky Host Detection—0
A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems—0
A Dependable Hybrid Machine Learning Model for Network Intrusion Detection—0
A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats—0
Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection—0
Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems—0
Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems—0
Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning—0
Adversarial Examples in Constrained Domains—0
Adversarial Machine Learning in Network Intrusion Detection Systems—0
Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review—0
Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems—0
Adversarial Training for Deep Learning-based Intrusion Detection Systems—0
A Dynamic Watermarking Algorithm for Finite Markov Decision Problems—0
A Grassmannian Approach to Zero-Shot Learning for Network Intrusion Detection—0
A Heterogeneous Graph Learning Model for Cyber-Attack Detection—0
A Hybrid Approach for an Interpretable and Explainable Intrusion Detection System—0
A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection—0
A Hypergraph-Based Machine Learning Ensemble Network Intrusion Detection System—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