Machine Learning and Data Analytics in Preventing APT Attacks

Machine Learning and Data Analytics in Preventing APT Attacks Student Name Institutional Affiliation Date Machine Learning and Data Analytics in Preventing APT Attacks Section 1: Machine Learning and Data Analytics * Describe the concepts of machine learning and data analytics and how applying them to cybersecurity will evolve the field. Machine learning is a subset of artificial intelligence (AI) that focuses on making computers learn from data ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"bclTgmKw","properties":{"formattedCitation":"(Sarker et al., 2020)","plainCitation":"(Sarker et al., 2020)","noteIndex":0},"citationItems":[{"id":30,"uris":["http://zotero.org/users/local/DoC3vaY8/items/YWB45UWT"],"itemData":{"id":30,"type":"article-journal","abstract":"Abstract\n \n In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting\n security incident patterns\n or insights from cybersecurity data and building corresponding\n data-driven model\n , is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss on\n cybersecurity data science\n , where the data is being gathered from relevant cybersecurity sources, and the analytics complement the\n latest data-driven patterns\n for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated\n research issues and future directions\n . Furthermore, we provide a\n machine learning\n based\n multi-layered framework\n for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.","container-title":"Journal of Big Data","DOI":"10.1186/s40537-020-00318-5","ISSN":"2196-1115","issue":"1","journalAbbreviation":"J Big Data","language":"en","page":"41","source":"DOI.org (Crossref)","title":"Cybersecurity data science: an overview from machine learning perspective","title-short":"Cybersecurity data science","volume":"7","author":[{"family":"Sarker","given":"Iqbal H."},{"family":"Kayes","given":"A. S. M."},{"family":"Badsha","given":"Shahriar"},{"family":"Alqahtani","given":"Hamed"},{"family":"Watters","given":"Paul"},{"family":"Ng","given":"Alex"}],"issued":{"date-parts":[["2020",12]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Sarker et al., 2020). It is concerned with the development of statistical algorithms which can learn from data and generalize, hence allowing machines to undertake tasks without explicit instructions. Closely related to machine learning is the concept of data analytics which denotes the process of extracting meaningful insights from vast datasets ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"bKnMHQpq","properties":{"formattedCitation":"(Nassar & Kamal, 2021)","plainCitation":"(Nassar & Kamal, 2021)","noteIndex":0},"citationItems":[{"id":32,"uris":["http://zotero.org/users/local/DoC3vaY8/items/H3HRP6J8"],"itemData":{"id":32,"type":"article-journal","container-title":"Journal of Artificial Intelligence and Machine Learning in Management","issue":"1","journalAbbreviation":"Journal of Artificial Intelligence and Machine Learning in Management","page":"51-63","title":"Machine Learning and Big Data analytics for Cybersecurity Threat Detection: A Holistic review of techniques and case studies","volume":"5","author":[{"family":"Nassar","given":"Ahmed"},{"family":"Kamal","given":"Mostafa"}],"issued":{"date-parts":[["2021"]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Nassar & Kamal, 2021). Cybersecurity generates huge amounts of data which need to be analyzed to counter cyber threats and data analytics allows security experts to sift through the immense data. The application of machine learning and data analytics will evolve the field of cybersecurity by enhancing threat detection, improving response, automating security operations and improving predictive capabilities. The use of machine learning and data analytics is necessitated by the changing nature of cyber threats ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"r06j5AvV","properties":{"formattedCitation":"(Sarker et al., 2020)","plainCitation":"(Sarker et al., 2020)","noteIndex":0},"citationItems":[{"id":30,"uris":["http://zotero.org/users/local/DoC3vaY8/items/YWB45UWT"],"itemData":{"id":30,"type":"article-journal","abstract":"Abstract\n \n In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting\n security incident patterns\n or insights from cybersecurity data and building corresponding\n data-driven model\n , is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper,...