Isbn: 9798181908970 - artificial intelligence applied to information security: from threat detection to automated incident response (6 risultati)

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Taschenbuch. Condizione: Neu. Neuware - Every attack begins before you notice. A silent process that mimics legitimate behavior. A file that slips past 70 antivirus engines undetected. A compromised credential lying dormant on the network for months. Adversaries already use Artificial Intelligence to attack at a scale and speed no human analyst can keep up with alone.This book exists because defense needs to be intelligent, too.Across ten chapters, you will learn to see what traditional tools miss: how machine learning models identify intrusion patterns in network traffic before an attack consolidates, how neural networks classify malware by behavior rather than by signature, and how large language models are redefining what a SOC analyst can accomplish in an eight-hour shift.The book assumes no advanced mathematics background or programming experience. It assumes you want to truly understand, not just operate tools.What you will learn: - How Random Forest algorithms and autoencoders detect intrusions and anomalies in network traffic with over 99% precision- How deep neural networks and Transformers classify malware by behavior, not by signature- How to build intelligent SIEMs with real-time behavioral analytics (UEBA)- How LLMs are being used both by attackers to generate personalized phishing and by defenders to automate alert triage- How to orchestrate incident response (SOAR) with AI agents without human intervention- How to ethically evaluate AI systems applied to security, including regulatory risks and algorithmic bias Four hands-on labs in Google Colab: - Lab 1: Build a network traffic classifier with Random Forest and interpret predictions using SHAP- Lab 2: Analyze a real Emotet malware sample in a sandbox and map behaviors to MITRE ATT&CK- Lab 3: Automate IOC triage via the VirusTotal API and export a structured CSV report- Lab 4: Write a complete ethics assessment of the systems you built, applying the AI Act and GDPR criteria Everything in the browser. Everything with free tools. No installation on your computer. Who this book is for: SOC analysts, detection engineers, incident response consultants, security architects, and managers with two to eight years of experience who need to understand what AI can and cannot do in defending their environments. Undergraduate and graduate students in information security will also find here foundational concepts and applications directly relevant to their careers.By the end, you will be able to assess when to trust an ML model, when to question it, and how to explain its limitations to decision-makers without technical backgrounds. That skill is worth more than any certification.…

Condizione: New. Print on Demand.

Paperback. Condizione: new. Paperback. Every attack begins before you notice. A silent process that mimics legitimate behavior. A file that slips past 70 antivirus engines undetected. A compromised credential lying dormant on the network for months. Adversaries already use Artificial Intelligence to attack at a scale and speed no human analyst can keep up with alone.This book exists because defense needs to be intelligent, too.Across ten chapters, you will learn to see what traditional tools miss: how machine learning models identify intrusion patterns in network traffic before an attack consolidates, how neural networks classify malware by behavior rather than by signature, and how large language models are redefining what a SOC analyst can accomplish in an eight-hour shift.The book assumes no advanced mathematics background or programming experience. It assumes you want to truly understand, not just operate tools.What you will learn: How Random Forest algorithms and autoencoders detect intrusions and anomalies in network traffic with over 99% precisionHow deep neural networks and Transformers classify malware by behavior, not by signatureHow to build intelligent SIEMs with real-time behavioral analytics (UEBA)How LLMs are being used both by attackers to generate personalized phishing and by defenders to automate alert triageHow to orchestrate incident response (SOAR) with AI agents without human interventionHow to ethically evaluate AI systems applied to security, including regulatory risks and algorithmic bias Four hands-on labs in Google Colab: Lab 1: Build a network traffic classifier with Random Forest and interpret predictions using SHAPLab 2: Analyze a real Emotet malware sample in a sandbox and map behaviors to MITRE ATT&CKLab 3: Automate IOC triage via the VirusTotal API and export a structured CSV reportLab 4: Write a complete ethics assessment of the systems you built, applying the AI Act and GDPR criteria Everything in the browser. Everything with free tools. No installation on your computer. Who this book is for: SOC analysts, detection engineers, incident response consultants, security architects, and managers with two to eight years of experience who need to understand what AI can and cannot do in defending their environments. Undergraduate and graduate students in information security will also find here foundational concepts and applications directly relevant to their careers.By the end, you will be able to assess when to trust an ML model, when to question it, and how to explain its limitations to decision-makers without technical backgrounds. That skill is worth more than any certification. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. Every attack begins before you notice. A silent process that mimics legitimate behavior. A file that slips past 70 antivirus engines undetected. A compromised credential lying dormant on the network for months. Adversaries already use Artificial Intelligence to attack at a scale and speed no human analyst can keep up with alone.This book exists because defense needs to be intelligent, too.Across ten chapters, you will learn to see what traditional tools miss: how machine learning models identify intrusion patterns in network traffic before an attack consolidates, how neural networks classify malware by behavior rather than by signature, and how large language models are redefining what a SOC analyst can accomplish in an eight-hour shift.The book assumes no advanced mathematics background or programming experience. It assumes you want to truly understand, not just operate tools.What you will learn: How Random Forest algorithms and autoencoders detect intrusions and anomalies in network traffic with over 99% precisionHow deep neural networks and Transformers classify malware by behavior, not by signatureHow to build intelligent SIEMs with real-time behavioral analytics (UEBA)How LLMs are being used both by attackers to generate personalized phishing and by defenders to automate alert triageHow to orchestrate incident response (SOAR) with AI agents without human interventionHow to ethically evaluate AI systems applied to security, including regulatory risks and algorithmic bias Four hands-on labs in Google Colab: Lab 1: Build a network traffic classifier with Random Forest and interpret predictions using SHAPLab 2: Analyze a real Emotet malware sample in a sandbox and map behaviors to MITRE ATT&CKLab 3: Automate IOC triage via the VirusTotal API and export a structured CSV reportLab 4: Write a complete ethics assessment of the systems you built, applying the AI Act and GDPR criteria Everything in the browser. Everything with free tools. No installation on your computer. Who this book is for: SOC analysts, detection engineers, incident response consultants, security architects, and managers with two to eight years of experience who need to understand what AI can and cannot do in defending their environments. Undergraduate and graduate students in information security will also find here foundational concepts and applications directly relevant to their careers.By the end, you will be able to assess when to trust an ML model, when to question it, and how to explain its limitations to decision-makers without technical backgrounds. That skill is worth more than any certification. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…