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 776–800 of 800 papers

TitleStatusHype
Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System—0
Detecting Clusters of Anomalies on Low-Dimensional Feature Subsets with Application to Network Traffic Flow Data—0
Extreme bandits—0
Anomaly Detection Framework Using Rule Extraction for Efficient Intrusion Detection—0
Keystroke Patterns as Prosody in Digital Writings: A Case Study with Deceptive Reviews and Essays—0
Efficient classification using parallel and scalable compressed model and Its application on intrusion detection—0
A Network Intrusions Detection System based on a Quantum Bio Inspired Algorithm—0
A New Clustering Approach for Anomaly Intrusion Detection—0
Relevant Feature Selection Model Using Data Mining for Intrusion Detection System—0
Continuous Features Discretization for Anomaly Intrusion Detectors Generation—0
Security Evaluation of Support Vector Machines in Adversarial Environments—0
Toward Supervised Anomaly Detection—0
An Identification System Using Eye Detection Based On Wavelets And Neural Networks—0
Intrusion Detection using Continuous Time Bayesian Networks—0
A DDoS-Aware IDS Model Based on Danger Theory and Mobile Agents—0
Two Timescale Convergent Q-learning for Sleep--Scheduling in Wireless Sensor Networks—0
Machine Learning Techniques for Intrusion Detection—0
A Review of Machine Learning based Anomaly Detection Techniques—0
The Dendritic Cell Algorithm for Intrusion Detection—0
Immune System Approaches to Intrusion Detection - A Review (ICARIS)—0
A SVM and K-means clustering based fast and efficient intrusion detection system—0
Predicting Network Attacks Using Ontology-Driven Inference—0
Fast Feature Reduction in intrusion detection datasets—0
A survey on deep packet inspection for intrusion detection systems—0
Intrusion Detection Systems Using Adaptive Regression Splines—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