Journal ArticleDOI
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Anna L. Buczak,Erhan Guven +1 more
TLDR
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.Abstract:
This survey paper describes a focused literature survey of machine learning (ML) and data mining (DM) methods for cyber analytics in support of intrusion detection. Short tutorial descriptions of each ML/DM method are provided. Based on the number of citations or the relevance of an emerging method, papers representing each method were identified, read, and summarized. Because data are so important in ML/DM approaches, some well-known cyber data sets used in ML/DM are described. 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.read more
Citations
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Journal ArticleDOI
A Survey on Machine Learning Techniques for Cyber Security in the Last Decade
TL;DR: This paper aims to provide a comprehensive overview of the challenges that ML techniques face in protecting cyberspace against attacks, by presenting a literature on ML techniques for cyber security including intrusion detection, spam detection, and malware detection on computer networks and mobile networks in the last decade.
Journal ArticleDOI
SS7 Vulnerabilities—A Survey and Implementation of Machine Learning vs Rule Based Filtering for Detection of SS7 Network Attacks
Kaleem Ullah,Imran Rashid,Hammad Afzal,Mian Muhammad Waseem Iqbal,Yawar Abbas Bangash,Haider Abbas +5 more
TL;DR: This paper provides a comprehensive review of the SS7 attacks with detailed methods to execute attacks, methods to enter theSS7 core network, and recommends safeguards against the SS 7 attacks and provides a machine learning based framework to detect anomalies in the SS6 network which is compared with rule based filtering.
Journal ArticleDOI
IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model
TL;DR: This paper presents an Intrusion Detection Tree (“IntruDTree”) machine-learning-based security model that first takes into account the ranking of security features according to their importance and then builds a tree-based generalized intrusion detection model based on the selected important features.
Journal ArticleDOI
Distributed Abnormal Behavior Detection Approach Based on Deep Belief Network and Ensemble SVM Using Spark
TL;DR: A novel distributed approach for the detection of abnormal behavior in large-scale networks using a combination of a deep feature extraction and multi-layer ensemble support vector machines (SVMs) in a distributed way is proposed.
Journal ArticleDOI
From Intrusion Detection to Attacker Attribution: A Comprehensive Survey of Unsupervised Methods
TL;DR: This paper provides a comprehensive overview of unsupervised and hybrid methods for intrusion detection, discussing their potential in the domain and descant how IDS data could be used to reconstruct and correlate attacks to identify attackers, with the use of advanced data analytics techniques.
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Book
Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
Collective dynamics of small-world networks
TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
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