[1906.10019] Machine Learning Construction: implications to cybersecurity Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Statistics > Machine Learning arXiv:1906.10019 (stat) [Submitted on 24 Jun 2019 ( v1 ), last revised 17 Dec 2022 (this version, v4)] Title: Machine Learning Construction: implications to cybersecurity Authors: Waleed A. Yousef View a PDF of the paper titled Machine Learning Construction: implications to cybersecurity, by Waleed A. Yousef View PDF HTML (experimental) Abstract: Statistical learning is the process of estimating an unknown probabilistic input-output relationship of a system using a limited number of observations. A statistical learning machine (SLM) is the algorithm, function, model, or rule, that learns such a process; and machine learning (ML) is the conventional name of this field. ML and its applications are ubiquitous in the modern world. Systems such as Automatic target recognition (ATR) in military applications, computer aided diagnosis (CAD) in medical imaging, DNA microarrays in genomics, optical character recognition (OCR), speech recognition (SR), spam email filtering, stock market prediction, etc., are few examples and applications for ML; diverse fields but one theory. In particular, ML has gained a lot of attention in the field of cyberphysical security, especially in the last decade. It is of great importance to this field to design detection algorithms that have the capability of learning from security data to be able to hunt threats, achieve better monitoring, master the complexity of the threat intelligence feeds, and achieve timely remediation of security incidents. The field of ML can be decomposed into two basic subfields: \textit{construction} and \textit{assessment}. We mean by \textit{construction} designing or inventing an appropriate algorithm that learns from the input data and achieves a good performance according to some optimality criterion. We mean by \textit{assessment} attributing some performance measures to the constructed ML algorithm, along with their estimators, to objectively assess this algorithm. \textit{Construction} and \textit{assessment} of a ML algorithm require familiarity with different other fields: probability, statistics, matrix theory, optimization, algorithms, and programming, among others.f Subjects: Machine Learning (stat.ML) ; Machine Learning (cs.LG) Cite as: arXiv:1906.10019 [stat.ML] (or arXiv:1906.10019v4 [stat.ML] for this version) https://doi.org/10.48550/arXiv.1906.10019 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1007/978-3-031-16237-4_2 Focus to learn more DOI(s) linking to related resources Submission history From: Waleed Yousef [ view email ] [v1] Mon, 24 Jun 2019 15:15:21 UTC (145 KB) [v2] Tue, 25 Jun 2019 11:27:44 UTC (145 KB) [v3] Wed, 25 May 2022 21:29:05 UTC (5,788 KB) [v4] Sat, 17 Dec 2022 19:35:23 UTC (5,791 KB) Full-text links: Access Paper: View a PDF of the paper titled Machine Learning Construction: implications to cybersecurity, by Waleed A. 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