Cyber Security Meets Machine Learning
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Cyber Security Meets Machine Learning
Machine learning boosts the capabilities of security solutions in the modern cyber environment. However, there are also security concerns associated with machine learning models and approaches: the vulnerability of machine learning models to adversarial attacks is a fatal flaw in the artificial intelligence technologies, and the privacy of the data used in the training and testing periods is also causing increasing concern among users. This book reviews the latest research in the area, including effective applications of machine learning methods in cybersecurity solutions and the urgent security risks related to the machine learning models. The book is divided into three parts: Cyber Security Based on Machine Learning; Security in Machine Learning Methods and Systems; and Security and Privacy in Outsourced Machine Learning. Addressing hot topics in cybersecurity and written by leading researchers in the field, the book features self-contained chapters to allow readers to select topics that are relevant to their needs. It is a valuable resource for all those interested in cybersecurity and robust machine learning, including graduate students and academic and industrial researchers, wanting to gain insights into cutting-edge research topics, as well as related tools and inspiring innovations.
MACHINE LEARNING FOR CYBERSECURITY: THREAT DETECTION AND MITIGATION
Author: Dr. Araddhana Arvind Deshmukh
language: en
Publisher: Xoffencer international book publication house
Release Date: 2024-07-05
As a result of the increasingly complex structure of today's information systems, there is a growing agreement that Artificial Intelligence (AI) is required in order to keep up with the exponential expansion of big data. Techniques from the field of machine learning (ML), in particular deep learning, are already being used to address a broad range of issues that are encountered in the real world. There are a number of intriguing examples of machine learning's practical triumphs, including machine translation, recommendations for vacations and travel, item identification and monitoring, and even various applications in the healthcare industry. Furthermore, machine learning has shown a great deal of promise in the area of autonomous driving and communication systems, which is why it is rightly considered to be a technical enabler. On the other hand, the civilization of today is more reliant than ever before on information technology systems, even autonomous ones, which are itself abused by malicious actors. In actuality, cybercriminals are always inventing new threats, and, they will have the ability to do significant harm or even kill people due to their capabilities. In order for defensive mechanisms to be able to prevent such events and limit the multiplicity of hazards that might potentially harm both current and future information technology systems, they need to be able to quickly adapt to (i) settings that are continually changing and (ii) threat landscapes that are always developing. It is hard to ignore the use of machine learning in the field of cybersecurity since it is manifestly impossible to address such a dual demand using methodologies that are static and human-defined. It is not surprising that a number of surveys and technical studies have been conducted on the subject of machine learning integration in the field of cybersecurity. Even though there have been a lot of accomplishments in research settings, there has been only a little amount of progress made in creating and integrating machine learning in industrial systems. The vast majority of these solutions are still using 'unsupervised' techniques, mostly for 'anomaly detection,' according to a recent report. This is despite the fact that more than ninety percent of enterprises are presently incorporating AI and ML into their defensive systems.
AI, Machine Learning and Deep Learning
Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and electronic devices, they are also vulnerable to various attacks and threats. AI parameters may be distorted by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be misled by changing the classification boundary, among many other attacks and threats. Such attacks can make AI products dangerous to use. While this discussion focuses on security issues in AI/ML/DL-based systems (i.e., securing the intelligent systems themselves), AI/ML/DL models and algorithms can actually also be used for cyber security (i.e., the use of AI to achieve security). Since AI/ML/DL security is a newly emergent field, many researchers and industry professionals cannot yet obtain a detailed, comprehensive understanding of this area. This book aims to provide a complete picture of the challenges and solutions to related security issues in various applications. It explains how different attacks can occur in advanced AI tools and the challenges of overcoming those attacks. Then, the book describes many sets of promising solutions to achieve AI security and privacy. The features of this book have seven aspects: This is the first book to explain various practical attacks and countermeasures to AI systems Both quantitative math models and practical security implementations are provided It covers both "securing the AI system itself" and "using AI to achieve security" It covers all the advanced AI attacks and threats with detailed attack models It provides multiple solution spaces to the security and privacy issues in AI tools The differences among ML and DL security and privacy issues are explained Many practical security applications are covered