Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection
- 6h 15m
- Mehak Khurana, Shilpa Mahajan, Vania Vieira Estrela
- John Wiley & Sons (US)
- 2024
APPLYING ARTIFICIAL INTELLIGENCE IN CYBERSECURITY ANALYTICS AND CYBER THREAT DETECTION
Comprehensive resource providing strategic defense mechanisms for malware, handling cybercrime, and identifying loopholes using artificial intelligence (AI) and machine learning (ML)
Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection is a comprehensive look at state-of-the-art theory and practical guidelines pertaining to the subject, showcasing recent innovations, emerging trends, and concerns as well as applied challenges encountered, and solutions adopted in the fields of cybersecurity using analytics and machine learning. The text clearly explains theoretical aspects, framework, system architecture, analysis and design, implementation, validation, and tools and techniques of data science and machine learning to detect and prevent cyber threats.
Using AI and ML approaches, the book offers strategic defense mechanisms for addressing malware, cybercrime, and system vulnerabilities. It also provides tools and techniques that can be applied by professional analysts to safely analyze, debug, and disassemble any malicious software they encounter.
With contributions from qualified authors with significant experience in the field, Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection explores topics such as:
- Cybersecurity tools originating from computational statistics literature and pure mathematics, such as nonparametric probability density estimation, graph-based manifold learning, and topological data analysis
- Applications of AI to penetration testing, malware, data privacy, intrusion detection system (IDS), and social engineering
- How AI automation addresses various security challenges in daily workflows and how to perform automated analyses to proactively mitigate threats
- Offensive technologies grouped together and analyzed at a higher level from both an offensive and defensive standpoint
Providing detailed coverage of a rapidly expanding field, Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection is an essential resource for a wide variety of researchers, scientists, and professionals involved in fields that intersect with cybersecurity, artificial intelligence, and machine learning.
About the Author
Shilpa Mahajan, PhD, is an Associate Professor in the School of Engineering and Technology at The NorthCap University, India.
Mehak Khurana, PhD, is an Associate Professor in the School of Engineering and Technology at The NorthCap University, India.
Vania Vieira Estrela, PhD, is a Professor with the Telecommunications Department of the Fluminense Federal University, Brazil.
In this Book
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Disclaimer
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Note for Readers
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Introduction
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Analysis of Malicious Executables and Detection Techniques
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Detection and Analysis of Botnet Attacks Using Machine Learning Techniques
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Artificial Intelligence Perspective on Digital Forensics
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Review on Machine Learning‐based Traffic Rules Contravention Detection System
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Enhancing Cybersecurity Ratings Using Artificial Intelligence and DevOps Technologies
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Malware Analysis Techniques in Android‐Based Smartphone Applications
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Cyber Threat Detection and Mitigation Using Artificial Intelligence – A Cyber‐physical Perspective
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Performance Analysis of Intrusion Detection System Using ML Techniques
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Spectral Pattern Learning Approach‐based Student Sentiment Analysis Using Dense‐net Multi Perception Neural Network in E‐learning Environment
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Big Data and Deep Learning‐based Tourism Industry Sentiment Analysis Using Deep Spectral Recurrent Neural Network
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Enhancing Security in Cloud Computing Using Artificial Intelligence (AI)
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Utilization of Deep Learning Models for Safe Human‐Friendly Computing in Cloud, Fog, and Mobile Edge Networks
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Artificial Intelligence for Threat Anomaly Detection Using Graph Databases – A Semantic Outlook
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Security in Blockchain‐Based Smart Cyber‐Physical Applications Relying on Wireless Sensor and Actuators Networks
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Leveraging Deep Learning Techniques for Securing the Internet of Things in the Age of Big Data
