Machine Learning for Cybersecurity: Innovative Deep Learning Solutions (SpringerBriefs in Computer Science)

✍ Scribed by Marwan Omar

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 54
• Category: Library

✦ Synopsis

This SpringerBrief presents the underlying principles of machine learning and how to deploy various deep learning tools and techniques to tackle and solve certain challenges facing the cybersecurity industry.
By implementing innovative deep learning solutions, cybersecurity researchers, students and practitioners can analyze patterns and learn how to prevent cyber-attacks and respond to changing malware behavior.
The knowledge and tools introduced in this brief can also assist cybersecurity teams to become more proactive in preventing threats and responding to active attacks in real time. It can reduce the amount of time spent on routine tasks and enable organizations to use their resources more strategically. In short, the knowledge and techniques provided in this brief can help make cybersecurity simpler, more proactive, less expensive and far more effective
Advanced-level students in computer science studying machine learning with a cybersecurity focus will find this SpringerBrief useful as a study guide. Researchers and cybersecurity professionals focusing on the application of machine learning tools and techniques to the cybersecurity domain will also want to purchase this SpringerBrief.

✦ Table of Contents

Contents
Chapter 1: Application of Machine Learning (ML) to Address Cybersecurity Threats
1.1 Introduction
1.2 Methodological Approach
1.2.1 Review of Literature
1.3 Conclusion
References
Chapter 2: New Approach to Malware Detection Using Optimized Convolutional Neural Network
2.1 Introduction
2.1.1 Need for the Study
2.1.2 Major Contributions of the Study
2.2 Related Work
2.3 System Architecture
2.4 Methodology and Dataset
2.5 Empirical Results
2.5.1 Improving the Baseline Model
2.5.2 Finalizing Our Model and Making Predictions
2.6 Results Comparison with Previous Work
2.7 Conclusion
References
Chapter 3: Malware Anomaly Detection Using Local Outlier Factor Technique
3.1 Introduction
3.1.1 Malware Detection
3.1.2 Intrusion Detection Systems
3.1.3 Network-Based Intrusion Detection System
3.1.4 Advantages of Network-Based Intrusion Detection System
3.2 Related Works
3.3 Proposed Methodology
3.3.1 Local Outlier Factor
3.3.1.1 A Brief Understanding of the Local Outlier Factor Approach
3.3.1.2 Basic Working of Local Outlier Factor
3.3.1.3 Local Reachability Distance
3.4 Results and Discussion
3.5 Conclusion
References