{"product_id":"data-science-in-cybersecurity-and-cyberthreat-intelligence-practical","title":"Data Science in Cybersecurity and Cyberthreat Intelligence - Practical","description":"\u003cp\u003eIn this review of Data Science in Cybersecurity and Cyberthreat Intelligence, the authors present a focused, research-driven collection aimed at security practitioners and researchers who need concrete methods for applying machine learning and semantic techniques to real cyber problems. The book's single biggest reason to buy is its emphasis on practical, automated approaches - from detecting network attacks and malicious URLs to predicting exploitation likelihood - that map research ideas to implementable analytics. Readers looking for applied methods and data fusion strategies will find the text especially useful.\u003c\/p\u003e\n\u003ch2\u003eKey Features\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eMachine learning for attack detection:\u003c\/strong\u003e Describes classifiers and models that help identify and predict cyberattacks across enterprise and IoT networks, enabling more proactive defenses.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMalware and URL identification:\u003c\/strong\u003e Shows methods for automating malicious code detection and for spotting malicious URLs and DGA-generated domains, improving incident triage.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSemantic reasoning and knowledge bases:\u003c\/strong\u003e Explains how formal knowledge bases and rule sets support richer threat inference and contextualized analysis.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eData aggregation and fusion:\u003c\/strong\u003e Details techniques to combine diverse data sources to automate data-driven cyberthreat intelligence workflows and analytics.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eVulnerability exploitation analysis:\u003c\/strong\u003e Outlines how machine learning classifiers can assess the likelihood of exploitation as an alternative or complement to traditional penetration testing.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eWho It's For\u003c\/h2\u003e\n\u003cp\u003eSecurity analysts, threat intelligence teams, and academic researchers who work at the intersection of data science and cybersecurity will get the most from this volume; it provides practical research approaches that can be adapted into detection pipelines and threat-hunting workflows. The chapters are suitable for readers already comfortable with basic machine learning and interested in applying those techniques to security use cases.\u003c\/p\u003e\n\u003cp\u003eThose seeking a beginner's primer on machine learning or a step-by-step coding tutorial may want a different, more hands-on textbook. Likewise, readers expecting a vendor or tool-specific buyer's guide should look elsewhere, because the book focuses on methods and reasoning rather than product reviews.\u003c\/p\u003e\n\u003ch2\u003ePros \u0026amp; Cons\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003ePros\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eCovers a wide range of applied topics from IoT attack detection to mHealth security, providing transferable approaches for real environments.\u003c\/li\u003e\n\u003cli\u003eBalances statistical machine learning with semantic and rule-based techniques to improve contextual detection and reduce false positives.\u003c\/li\u003e\n\u003cli\u003eFocuses on data fusion and automation, which helps teams move from isolated signals to integrated threat intelligence.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eCons\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe book is research-focused and does not provide step-by-step code walkthroughs for every technique, so practitioners may need to adapt concepts to their toolchains.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eSpecifications\u003c\/h2\u003e\n\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003eTitle\u003c\/td\u003e\n\u003ctd\u003eData Science in Cybersecurity and Cyberthreat Intelligence\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSeries\u003c\/td\u003e\n\u003ctd\u003eIntelligent Systems Reference Library\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAuthors \/ Editors\u003c\/td\u003e\n\u003ctd\u003eLeslie F. Sikos, Kim-Kwang Raymond Choo\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePrimary focus\u003c\/td\u003e\n\u003ctd\u003eMachine learning, semantic reasoning, knowledge bases for cybersecurity\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eApplications covered\u003c\/td\u003e\n\u003ctd\u003eNetwork attack detection, IoT security, malware and URL identification, mHealth protection\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAnalytic emphasis\u003c\/td\u003e\n\u003ctd\u003eData aggregation, data fusion, automated analytics for cyberthreat intelligence\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch2\u003eOur Verdict\u003c\/h2\u003e\n\u003cp\u003eThis is a pragmatic, research-oriented collection well suited to security professionals and researchers who need methods to operationalize machine learning and semantic techniques in threat detection. It is good value for readers looking to translate academic approaches into automated analytics and integrated threat intelligence pipelines, though those who need hands-on code examples should supplement it with implementation guides.\u003c\/p\u003e\n\u003ch2\u003eFrequently Asked Questions\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003eDoes the book include practical case studies?\u003c\/strong\u003e\u003cbr\u003eThe book presents applied approaches and examples across domains like IoT, enterprise networks, and mHealth to illustrate methods rather than step-by-step case walkthroughs.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eIs prior machine learning knowledge required?\u003c\/strong\u003e\u003cbr\u003eBasic familiarity with machine learning concepts helps, as the text focuses on applying classifiers and semantic techniques to security problems rather than teaching fundamentals.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWill this help improve threat intelligence workflows?\u003c\/strong\u003e\u003cbr\u003eYes; the sections on data aggregation, data fusion, and automated analytics are specifically geared toward making threat intelligence more data-driven and operational.\u003c\/p\u003e","brand":"Leslie F. 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