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 701–750 of 800 papers

TitleStatusHype
Protection of an information system by artificial intelligence: a three-phase approach based on behaviour analysis to detect a hostile scenario—0
A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics—0
An Adversarial Approach for Explainable AI in Intrusion Detection Systems—0
OCLEP+: One-class Anomaly and Intrusion Detection Using Minimal Length of Emerging Patterns—0
Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternativesCode0
Machine Learning for Anomaly Detection and Categorization in Multi-cloud Environments—0
Intrusion Detection Using Mouse DynamicsCode0
Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber SecurityCode0
Flow-based Network Traffic Generation using Generative Adversarial Networks—0
Time is of the Essence: Machine Learning-based Intrusion Detection in Industrial Time Series Data—0
IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection—0
Sequence Covering for Efficient Host-Based Intrusion DetectionCode0
Enhanced network anomaly detection based on deep neural networks—0
Using Randomness to Improve Robustness of Machine-Learning Models Against Evasion Attacks—0
Active Learning for Wireless IoT Intrusion Detection—0
Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification SystemsCode0
Enhancing Cohesion and Coherence of Fake Text to Improve Believability for Deceiving Cyber Attackers—0
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks—0
V-CNN: When Convolutional Neural Network encounters Data Visualization—0
A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection SystemsCode0
AI-based Two-Stage Intrusion Detection for Software Defined IoT Networks—0
Intensive Preprocessing of KDD Cup 99 for Network Intrusion Classification Using Machine Learning Techniques—0
EMO\&LY (EMOtion and AnomaLY) : A new corpus for anomaly detection in an audiovisual stream with emotional context.—0
Towards an Efficient Anomaly-Based Intrusion Detection for Software-Defined Networks—0
Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks—0
Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection—0
Onion-Peeling Outlier Detection in 2-D data Sets—0
BEBP: An Poisoning Method Against Machine Learning Based IDSs—0
Online Feature Ranking for Intrusion Detection Systems—0
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion DetectionCode0
First-order bifurcation detection for dynamic complex networks—0
Generative Models for Spear Phishing Posts on Social Media—0
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly DetectionCode0
One-class Collective Anomaly Detection based on Long Short-Term Memory Recurrent Neural Networks—0
Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms—0
Secure Mobile Crowdsensing with Deep Learning—0
Arhuaco: Deep Learning and Isolation Based Security for Distributed High-Throughput ComputingCode0
Fusion of ANN and SVM Classifiers for Network Attack Detection—0
Evaluation of Machine Learning Algorithms for Intrusion Detection System—0
Learning automata based SVM for intrusion detection—0
A Deep Belief Network Based Machine Learning System for Risky Host Detection—0
An empirical evaluation for the intrusion detection features based on machine learning and feature selection methods—0
Manifold regularization based on Nyström type subsampling—0
Performance Comparison of Intrusion Detection Systems and Application of Machine Learning to Snort System—0
A Renewal Model of IntrusionCode0
A Grassmannian Approach to Zero-Shot Learning for Network Intrusion Detection—0
Practical Machine Learning for Cloud Intrusion Detection: Challenges and the Way Forward—0
A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic DataCode0
Security Evaluation of Pattern Classifiers under Attack—0
Machine Learning Approach for Detection of nonTor Traffic—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