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.
"synopsis" may belong to another edition of this title.
Seller: California Books, Miami, FL, U.S.A.
Condition: New. Seller Inventory # I-9786208444730
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # L2-9786208444730
Quantity: Over 20 available
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 60 pp. Englisch. Seller Inventory # 9786208444730
Seller: Majestic Books, Hounslow, United Kingdom
Condition: New. Print on Demand. Seller Inventory # 409777010
Quantity: 4 available
Seller: Books Puddle, New York, NY, U.S.A.
Condition: New. Print on Demand. Seller Inventory # 26404425901
Seller: Biblios, Frankfurt am main, HESSE, Germany
Condition: New. PRINT ON DEMAND. Seller Inventory # 18404425895
Seller: AHA-BUCH GmbH, Einbeck, Germany
Taschenbuch. Condition: 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. Seller Inventory # 9786208444730
Seller: preigu, Osnabrück, Germany
Taschenbuch. Condition: Neu. Leveraging Artificial Intelligence and Machine Learning | Kanthavel Radhakrishnan (u. a.) | Taschenbuch | Englisch | 2025 | KS Omniscriptum Publishing | EAN 9786208444730 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Seller Inventory # 133335912