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Journal ArticleDOI

A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection

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.

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Posted Content

IoT Behavioral Monitoring via Network Traffic Analysis.

TL;DR: This thesis is the culmination of efforts to develop techniques to profile the network behavioral pattern of IoTs, automate IoT classification, deduce their operating context, and detect anomalous behavior indicative of cyber-attacks.
Book ChapterDOI

Machine Learning and Deep Learning in Cyber Security for IoT

TL;DR: In this article, a wide review of challenges and research opportunities that concerned in applying by ML/DL techniques is provided, and a variety of attack models for IoT framework and solutions based on deep learning and machine learning techniques are examined.
Journal ArticleDOI

An Empirical Study for Detecting Fake Facebook Profiles Using Supervised Mining Techniques

TL;DR: This paper proposes a smart system (FBChecker) that enables users to check if any Facebook profile is fake, and utilizes the data mining approach to analyze and classify a set of behavioral and informational attributes provided in the personal profiles.
Journal ArticleDOI

Study on Security and Privacy in 5G-Enabled Applications

TL;DR: In this paper, the security and privacy risks faced by 5G applications and related security standards and research work are analyzed and a hierarchical solution for stakeholders to build secure 5G application is presented.
Journal ArticleDOI

The Role of Machine Learning in Cybersecurity

TL;DR: This paper is the first attempt to provide a holistic understanding of the role of ML in the entire cybersecurity domain and highlights the advantages of ML with respect to human-driven detection methods, as well as the additional tasks that can be addressed by ML in cybersecurity.
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.
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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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