Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection

✍ Scribed by Shilpa Mahajan (editor), Mehak Khurana (editor), Vania Vieira Estrela (editor)

• Publisher: Wiley
• Year: 2024
• Tongue: English
• Leaves: 753
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Table of Contents
Title Page
Copyright
Dedication
About the Editors
List of Contributors
Preface
Acknowledgment
Disclaimer
Note for Readers
Introduction
Part I: Artificial Intelligence (AI) in Cybersecurity Analytics: Fundamental and Challenges
1 Analysis of Malicious Executables and Detection Techniques
1.1 Introduction
1.2 Malicious Code Classification System
1.3 Literature Review
1.4 Malware Behavior Analysis
1.5 Conventional Detection Systems
1.6 Classifying Executables by Payload Function
1.7 Result and Discussion
1.8 Conclusion
References
2 Detection and Analysis of Botnet Attacks Using Machine Learning Techniques
2.1 Introduction
2.2 Literature Review
2.3 Botnet Architecture
2.4 Methodology Adopted
2.5 Experimental Setup
2.6 Results and Discussions
2.7 Conclusion and Future Work
References
3 Artificial Intelligence Perspective on Digital Forensics
3.1 Introduction
3.2 Literature Survey
3.3 Phases of Digital Forensics
3.4 Demystifying Artificial Intelligence in the Digital World
3.5 Application of Machine Learning in Digital Forensics Investigations
3.6 Implementation of Artificial Intelligence in Forensics
3.7 Pattern Recognition Using Artificial Intelligence
3.8 Applications of AI in Criminal Investigations
3.9 Conclusion
References
4 Review on Machine Learning‐based Traffic Rules Contravention Detection System
4.1 Introduction
4.2 Technologies Involved in Smart Traffic Monitoring
4.3 Literature Review
4.4 Comparison of Results
4.5 Conclusion and Future Scope
References
5 Enhancing Cybersecurity Ratings Using Artificial Intelligence and DevOps Technologies
5.1 Introduction
5.2 Literature Review
5.3 Proposed Methodology
5.4 Results
5.5 Conclusion and Future Scope of Work
References
Part II: Cyber Threat Detection and Analysis Using Artificial Intelligence and Big Data
6 Malware Analysis Techniques in Android‐Based Smartphone Applications
6.1 Introduction
6.2 Malware Analysis Techniques
6.3 Hybrid Analysis
6.4 Result
6.5 Conclusion
References
7 Cyber Threat Detection and Mitigation Using Artificial Intelligence – A Cyber‐physical Perspective
7.1 Introduction
7.2 Types of Cyber Threats
7.3 Cyber Threat Intelligence (CTI)
7.4 Materials and Methods
7.5 Cyber‐Physical Systems Relying on AI (CPS‐AI)
7.6 Experimental Analysis
7.7 Conclusion
References
8 Performance Analysis of Intrusion Detection System Using ML Techniques
8.1 Introduction
8.2 Literature Survey
8.3 ML Techniques
8.4 Overview of Dataset
8.5 Proposed Approach
8.6 Simulation Results
8.7 Conclusion and Future Work
References
9 Spectral Pattern Learning Approach‐based Student Sentiment Analysis Using Dense‐net Multi Perception Neural Network in E‐learning Environment
9.1 Introduction
9.2 Related Work
9.3 Proposed Implementation
9.4 Result and Discussion
9.5 Conclusion
References
10 Big Data and Deep Learning‐based Tourism Industry Sentiment Analysis Using Deep Spectral Recurrent Neural Network
10.1 Introduction
10.2 Related Work
10.3 Materials and Method
10.4 Result and Discussion
10.5 Conclusion
References
Part III: Applied Artificial Intelligence Approaches in Emerging Cybersecurity Domains
11 Enhancing Security in Cloud Computing Using Artificial Intelligence (AI)
11.1 Introduction
11.2 Background
11.3 Identification Function (IF)
11.4 Protection Function (PF)
11.5 Detection Function (DF)
11.6 Response Function (RF)
11.7 Recovery Function (RcF)
11.8 Analysis, Discussion and Research Gaps
11.9 Conclusion
References
12 Utilization of Deep Learning Models for Safe Human‐Friendly Computing in Cloud, Fog, and Mobile Edge Networks
12.1 Introduction
12.2 Human‐Centered Computing (HCC)
12.3 Improving Cybersecurity Through Deep Learning (DL) Models: AI‐HCC Systems
12.4 Case Studies
12.5 Discussion
12.6 Conclusion
References
13 Artificial Intelligence for Threat Anomaly Detection Using Graph Databases – A Semantic Outlook
13.1 Introduction
13.2 KGs in Cybersecurity
13.3 CSKG Construction Methodologies
13.4 Datasets
13.5 Application Scenarios
13.6 Discussion and Future Trends on CSKG
13.7 Conclusion
References
14 Security in Blockchain‐Based Smart Cyber‐Physical Applications Relying on Wireless Sensor and Actuators Networks
14.1 Introduction
14.2 Methodology
14.3 GIBCS: An Overview
14.4 Blockchain Layer
14.5 Trust Management
14.6 Blockchain for Secure Monitoring Back‐End
14.7 Blockchain‐Enabled Cybersecurity: Discussion and Future Directions
14.8 Conclusions
References
15 Leveraging Deep Learning Techniques for Securing the Internet of Things in the Age of Big Data
15.1 Introduction to the IoT Security
15.2 Role of Deep Learning in IoT Security
15.3 Deep Learning Architecture for IoT Security
15.4 Future Scope of Deep Learning in IoT Security
15.5 Conclusion
References
Index
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