Journal ArticleDOI
Mutual information-based feature selection for intrusion detection systems
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TLDR
This work proposes two feature selection algorithms and investigates the performance of using these algorithms compared to a mutual information-based feature selection method, using both a linear and a non-linear measure-linear correlation coefficient and mutual information, for the feature selection.About:
This article is published in Journal of Network and Computer Applications.The article was published on 2011-07-01. It has received 379 citations till now. The article focuses on the topics: Anomaly-based intrusion detection system & Feature selection.read more
Citations
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Journal ArticleDOI
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Anna L. Buczak,Erhan Guven +1 more
TL;DR: The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/ DM for cyber security is presented, and some recommendations on when to use a given method are provided.
Journal ArticleDOI
Feature selection in machine learning: A new perspective
TL;DR: This study discusses several frequently-used evaluation measures for feature selection, and surveys supervised, unsupervised, and semi-supervised feature selection methods, which are widely applied in machine learning problems, such as classification and clustering.
Journal ArticleDOI
Network Anomaly Detection: Methods, Systems and Tools
TL;DR: This paper provides a structured and comprehensive overview of various facets of network anomaly detection so that a researcher can become quickly familiar with every aspect of network anomalies detection.
Journal ArticleDOI
Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm
TL;DR: The evaluation results show that the feature selection algorithm contributes more critical features for LSSVM-IDS to achieve better accuracy and lower computational cost compared with the state-of-the-art methods.
Journal ArticleDOI
HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection
TL;DR: This paper proposes a novel IDS called the hierarchical spatial-temporal features-based intrusion detection system (HAST-IDS), which first learns the low-level spatial features of network traffic using deep convolutional neural networks (CNNs) and then learns high-level temporal features using long short-term memory networks.
References
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Journal ArticleDOI
Least Squares Support Vector Machine Classifiers
TL;DR: A least squares version for support vector machine (SVM) classifiers that follows from solving a set of linear equations, instead of quadratic programming for classical SVM's.
Journal ArticleDOI
Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
TL;DR: In this article, the maximal statistical dependency criterion based on mutual information (mRMR) was proposed to select good features according to the maximal dependency condition. But the problem of feature selection is not solved by directly implementing mRMR.
Feature selection based on mutual information: criteria ofmax-dependency, max-relevance, and min-redundancy
TL;DR: This work derives an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection, and presents a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers).
Book ChapterDOI
Fast effective rule induction
TL;DR: This paper evaluates the recently-proposed rule learning algorithm IREP on a large and diverse collection of benchmark problems, and proposes a number of modifications resulting in an algorithm RIPPERk that is very competitive with C4.5 and C 4.5rules with respect to error rates, but much more efficient on large samples.
Journal ArticleDOI
Estimating mutual information.
TL;DR: Two classes of improved estimators for mutual information M(X,Y), from samples of random points distributed according to some joint probability density mu(x,y), based on entropy estimates from k -nearest neighbor distances are presented.