2023
DOI: 10.47672/ejt.1486
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Machine Learning in Cybersecurity: Techniques and Challenges

Abstract: In the computer world, data science is the force behind the recent dramatic changes in cybersecurity's operations and technologies. The secret to making a security system automated and intelligent is to extract patterns or insights related to security incidents from cybersecurity data and construct appropriate data-driven models. Data science, also known as diverse scientific approaches, machine learning techniques, processes, and systems, is the study of actual occurrences via the use of data. Due to its dist… Show more

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Cited by 45 publications
(7 citation statements)
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“…In IT, AI-driven advancements have led to transformative developments across various domains. Examples include the implementation of natural language processing (NLP) for improved human-computer interactions [29], the use of machine learning in cybersecurity for proactive threat detection [30], and the application of predictive analytics to anticipate and prevent system failures. Automated code generation and testing, personalized user experiences, and autonomous IT operations are other notable areas where AI enhances efficiency [31].…”
Section: Artificial Intelligence and Societymentioning
confidence: 99%
“…In IT, AI-driven advancements have led to transformative developments across various domains. Examples include the implementation of natural language processing (NLP) for improved human-computer interactions [29], the use of machine learning in cybersecurity for proactive threat detection [30], and the application of predictive analytics to anticipate and prevent system failures. Automated code generation and testing, personalized user experiences, and autonomous IT operations are other notable areas where AI enhances efficiency [31].…”
Section: Artificial Intelligence and Societymentioning
confidence: 99%
“…Practitioners and researchers alike need to be on the lookout for potential abuses of AI-enhanced threat detection systems. To reduce the possibility of unforeseen repercussions, industry standards, international cooperation, and the creation and application of ethical frameworks are crucial [27]. The long-term societal effects of widespread AI deployment in cyber security are also ethically relevant.…”
Section: Privacy Consequences and Ethical Considerationsmentioning
confidence: 99%
“…The Learning to Rank method has been applied with the XGBoost method [38], but not in the context of peak time forecasting. The transformation of loads to ranks could yield one big advantage over more sophisticated methods: it is likely to be easier for endusers to implement and comprehend, which, as various studies have shown, is essential for the adoption of novel technologies and smart grid applications [39][40][41][42].…”
Section: Related Workmentioning
confidence: 99%