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

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Showing 651–700 of 800 papers

TitleStatusHype
ZYELL-NCTU NetTraffic-1.0: A Large-Scale Dataset for Real-World Network Anomaly Detection—0
Generative Adversarial Networks for Distributed Intrusion Detection in the Internet of Things—0
The Adversarial Machine Learning Conundrum: Can The Insecurity of ML Become The Achilles' Heel of Cognitive Networks?—0
1D CNN Based Network Intrusion Detection with Normalization on Imbalanced Data—0
AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs—0
CRUPL: A Semi-Supervised Cyber Attack Detection with Consistency Regularization and Uncertainty-aware Pseudo-Labeling in Smart Grid—0
Assessing the Resilience of Automotive Intrusion Detection Systems to Adversarial Manipulation—0
Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer—0
Accelerating IoV Intrusion Detection: Benchmarking GPU-Accelerated vs CPU-Based ML Libraries—0
A cognitive based Intrusion detection system—0
A Combination of Temporal Sequence Learning and Data Description for Anomaly-based NIDS—0
A Comparative Analysis of Machine Learning Algorithms for Intrusion Detection in Edge-Enabled IoT Networks—0
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection—0
A comparative evaluation of novelty detection algorithms for discrete sequences—0
A Comparative Study of AI-based Intrusion Detection Techniques in Critical Infrastructures—0
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis—0
A Compendium on Network and Host based Intrusion Detection Systems—0
A concise method for feature selection via normalized frequencies—0
A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare Attacks in IoT Networks—0
A Content-Based Deep Intrusion Detection System—0
A Critical Assessment of Interpretable and Explainable Machine Learning for Intrusion Detection—0
Active Learning for Network Intrusion Detection—0
Active Learning for Wireless IoT Intrusion Detection—0
A Cutting-Edge Deep Learning Method For Enhancing IoT Security—0
A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection—0
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