Présentation du livre en françaisintelligence artificielle

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR CYBERSECURITY

Name: ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR CYBERSECURITY
Author: Faramarz Kowsari

Foundations, Intelligent Threat Detection, Adversarial Machine Learning, Privacy, Generative AI, and Secure Autonomous Systems

« ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR CYBERSECURITY » est un livre de Faramarz Kowsari dans le domaine intelligence artificielle. Cette page de découverte en français organise les métadonnées publiques du livre afin de rendre ses thèmes, son public, sa langue réelle de publication et ses accès officiels plus faciles à trouver.

Les métadonnées publiques rattachent ce titre à l'intelligence artificielle, aux outils d'IA modernes ou aux flux de travail fondés sur les prompts.

Langue de publication: anglais. Cette page est une orientation en français. Le livre lui-même est publié en anglais ; le titre, l'aperçu et les options d'achat sur Google Books correspondent à cette édition.

Voir sur Google Books Ouvrir l'aperçu gratuit Page originale du livre

De quoi parle ce livre ?

Les métadonnées publiques rattachent ce titre à l'intelligence artificielle, aux outils d'IA modernes ou aux flux de travail fondés sur les prompts.

Thèmes mis en avant

Artificial intelligence in cybersecurityMachine learning for threat detectionNetwork intrusion detectionMalware and ransomware analysisPhishing and DNS securityEndpoint, cloud and container telemetrySecurity data engineeringData leakage and time-based validationPrecision, recall, calibration and concept driftGraph neural networks and attack graphsAdversarial machine learning and data poisoningModel extraction and model supply-chain securityDifferential privacy and federated learningLarge language model (LLM) security

Description publique originale du livre

anglais

Artificial Intelligence and Machine Learning for Cybersecurity is an English-language, 473-page graduate-level textbook organized into 70 chapters and ten connected parts. It begins with security objectives, threat modeling, networks, cryptography, logs and incident response, then builds the necessary Python, probability, statistics, machine learning and deep learning foundations. Security telemetry, temporal feature engineering, class imbalance, data leakage, calibration, uncertainty and reproducibility form the methodological bridge to applied threat detection. The applied chapters connect models to recognizable cybersecurity evidence: network flows, encrypted-traffic metadata, anomalies, malware files and behavior, ransomware, phishing, malicious URLs and DNS, identity misuse, botnets, DDoS, cloud and container events, digital forensics and threat intelligence. Further sections examine graph-based attack analysis, concept drift, operational technology, connected vehicles, defensible autonomous response and causal reasoning. Throughout, the distinction between a model prediction and an authorized operational decision remains central. The advanced sequence studies adversarial examples, problem-space evasion, poisoning, model extraction, robust evaluation and AI supply-chain integrity, followed by membership inference, differential privacy, federated learning and privacy-preserving techniques. The final chapters address generative AI, retrieval-augmented security intelligence, prompt injection, LLM application security, red teaming, agent permissions, MCP, SOC copilots, secure MLOps, model incidents, human factors, edge deployment, research design, an end-to-end capstone and post-quantum security. Python examples, explanatory figures and illustrative scenarios are…

À qui s'adresse le livre ?

Le public visé est fondé sur la description publique du livre. Pour les lecteurs francophones, il faut noter que l'édition disponible est publiée en anglais.

For master's-level AI and cybersecurity students, instructors, researchers, software engineers, security analysts and independent learners who want a structured bridge between machine learning theory, cybersecurity telemetry, defensible evaluation and secure AI system deployment.

Objectifs d'apprentissage publiés