As cybersecurity threats continue to evolve in sophistication, velocity, and impact, the conventional reactive security approaches cannot keep up with them. In these cases, cyber attackers have employed more sophisticated strategies such as polymorphic malware, fileless attacks, and living-off-the-land techniques that do not depend on traditional detection methods. Anticipating this, predictive risk looking (more notably using artificial intelligence (AI) and machine learning) has emerged as a key technology in today’s cybersecurity strategies. Predictive risk finding allows security teams to proactively detect hidden risks, spot anomalies, and anticipate adversary behaviours before it results in a breach or device compromise. AI/ML approaches leverage behavioural analytics, large-scale telemetry data, and real-time learning to uncover overlooked patterns often missed by human analysts or rule-based architectures. In this article, we provide an exclusive overview of today’s cutting-edge AI and ML applications in predictive risk looking. We focus on core technologies, device architectures, algorithmic models, and industry specific implementations.
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Kanthavel R is having more than 27 years of professional academic, research and administrative experience in Asia, Middle East, and Oceania universities. Currently he is working as Professor of Computer Engineering, School of Electrical and Communications Engineering, PNG University of Technology,Papua New Guinea.
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - As cybersecurity threats continue to evolve in sophistication, velocity, and impact, the conventional reactive security approaches cannot keep up with them. In these cases, cyber attackers have employed more sophisticated strategies such as polymorphic malware, fileless attacks, and living-off-the-land techniques that do not depend on traditional detection methods. Anticipating this, predictive risk looking (more notably using artificial intelligence (AI) and machine learning) has emerged as a key technology in today's cybersecurity strategies. Predictive risk finding allows security teams to proactively detect hidden risks, spot anomalies, and anticipate adversary behaviours before it results in a breach or device compromise. AI/ML approaches leverage behavioural analytics, large-scale telemetry data, and real-time learning to uncover overlooked patterns often missed by human analysts or rule-based architectures. In this article, we provide an exclusive overview of today's cutting-edge AI and ML applications in predictive risk looking. We focus on core technologies, device architectures, algorithmic models, and industry specific implementations. Bestandsnummer des Verkäufers 9786208445133
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -As cybersecurity threats continue to evolve in sophistication, velocity, and impact, the conventional reactive security approaches cannot keep up with them. In these cases, cyber attackers have employed more sophisticated strategies such as polymorphic malware, fileless attacks, and living-off-the-land techniques that do not depend on traditional detection methods. Anticipating this, predictive risk looking (more notably using artificial intelligence (AI) and machine learning) has emerged as a key technology in today's cybersecurity strategies. Predictive risk finding allows security teams to proactively detect hidden risks, spot anomalies, and anticipate adversary behaviours before it results in a breach or device compromise. AI/ML approaches leverage behavioural analytics, large-scale telemetry data, and real-time learning to uncover overlooked patterns often missed by human analysts or rule-based architectures. In this article, we provide an exclusive overview of today's cutting-edge AI and ML applications in predictive risk looking. We focus on core technologies, device architectures, algorithmic models, and industry specific implementations.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 52 pp. Englisch. Bestandsnummer des Verkäufers 9786208445133
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Taschenbuch. Zustand: Neu. LEVERAGING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING | FOR PREDICTIVE THREAT HUNTING IN CYBERSECURITY | Kanthavel R (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786208445133 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 133620109
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