{"product_id":"machine-learning-for-malware-detection-strategies-models-and-applications-9798281853163","title":"Machine Learning for Malware Detection: Strategies, Models, and Applications","description":"\u003cp\u003e • Author(s): Taylor Royce\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Machine Theory\u003c\/p\u003e\u003cp\u003e\u003cb\u003eMachine Learning for Malware Detection: Strategies, Models, and Applications\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eReactive defenses are no longer adequate as the cybersecurity environment gets more complicated and adversaries become more skilled. A state-of-the-art, professionally grounded investigation of how artificial intelligence, in particular machine learning, can be used to proactively identify, categorize, and react to malware threats in real-time is provided by \u003cb\u003e\u003ci\u003eMachine Learning for Malware Detection: Strategies, Models, and Applications.\u003c\/i\u003e\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e\u003ci\u003eData scientists, threat analysts, cybersecurity professionals, and technology executives who understand the critical need for intelligent, scalable defenses in today's digital infrastructure are the target audience for this book. It provides a thorough and useful road map for incorporating machine learning into contemporary malware detection processes while being mindful of the operational, moral, and legal issues that come with AI-powered systems.\u003c\/i\u003e \u003cp\u003e\u003c\/p\u003eThis book explores the entire lifecycle of intelligent malware detection, from data gathering and feature engineering to model evaluation, adversarial resilience, and ethical deployment, rather than concentrating only on algorithms or superficial trends. Every chapter is thoughtfully organized to provide practical insights derived from current research, real-world problems, and tried-and-true tactics. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eThe following topics will be thoroughly understood by readers: \u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eThe advantages and disadvantages of machine learning models in dynamic threat situations\u003c\/li\u003e\n\u003cli\u003eMethods for adversarial hardening and identifying malware that evades artificial intelligence; strategies for reducing false positives and preserving model reliability over time\u003c\/li\u003e\n\u003cli\u003eStrategic considerations for creating resilient, future-ready cyber defense ecosystems; the use of machine learning into larger threat intelligence and incident response frameworks\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003ci\u003e\u003cb\u003eThis book stands out for its dedication to professionalism, depth, and clarity. In addition to being technically solid, the content is contextualized within the larger goals of safeguarding user privacy, defending digital assets, and facilitating the appropriate use of AI in security operations.\u003c\/b\u003e\u003c\/i\u003e \u003cp\u003e\u003c\/p\u003eIn a time when machine learning may be used as a weapon and a shield, \u003ci\u003e\u003cb\u003e Machine Learning for Malware Detection: Strategies, Models, and Applications \u003c\/b\u003e\u003c\/i\u003eis more than just a technical handbook; it is a strategic manual for creating intelligent, robust, and moral cybersecurity systems.","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":45554828607639,"sku":"9798281853163","price":1241.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798281853163.webp?v=1768589015","url":"https:\/\/atlanticbooks.com\/products\/machine-learning-for-malware-detection-strategies-models-and-applications-9798281853163","provider":"Atlantic Books","version":"1.0","type":"link"}