AI, Machine Learning and Deep Learning a Security Perspective

In recent times, the utilization of machine learning (Al-Anqoudi et al. 2021; Majumder, Gupta, and Paul 2022), artificial intelligence, and deep learning (Batool and Khan 2022) has profoundly disrupted various industries and technological domains. This pertains not only to industrial implementations (Hidayatno, Destyanto, and Hulu 2019) but also to the potential setback in knowledge acquisition and the diminished productivity of both individuals and groups. With the advent of this book, I find it truly remarkable as it introduces a fresh perspective regarding the immense power of data science when integrated into the realms of AI, deep learning, and machine learning (Batool and Khan 2022; Dubey et al. 2019). I am convinced that this book will prove immensely beneficial to the scholarly community in the fields of data science, artificial intelligence, and deep learning. It stands as a breakthrough and serves as a guiding resource for current and future endeavors, including research activities.

The Book stresses the importance of AI and ML/DL in current IT, especially robotics and autonomous vehicles. It also illustrates AI systems’ vulnerability to attacks and threats, which could endanger safety. A book on AI/ML/DL security is mentioned. It will address realistic attacks and countermeasures, mathematical models and real-world security implementations, securing AI systems and employing AI for cybersecurity, advanced AI attacks, numerous security and privacy solutions, ML and DL security challenges, and practical security applications. The book helps AI developers, researchers, and industry specialists worldwide understand and address AI security concerns.

This book has 19 chapters, divided into four sections: “Secure AI/ML Systems: Attack Models”, “Defenses”, “Using AI/ML Algorithms for Cyber Security”, and “Application”. Part I has seven chapters. Chapter 1 describes machine learning (ML) and smart city applications. It also discusses adversarial attacks and data poisoning, which threaten machine learning algorithms. The chapter strives to explain these issues. Chapter two discusses how high-performance wireless communication systems are becoming more important and how machine learning might improve wireless security. Deep learning (DL) and its models are reviewed in the third chapter, focusing on their uses and adversarial potential. Chapter 4 covers collaborative deep learning (CDL) and related security challenges like poisoning and GAN attacks. Chapter 5 analyzes deep reinforcement learning (DRL) and possible defenses. In Chapter 6, big data analytics, cybersecurity, AI, ML, and DRL intersect to emphasize the need for strong defenses. Chapter 7 addresses IoT integration and cybersecurity challenges and introduces a Bayesian network-based risk assessment approach. These chapters conclude by discussing AI, ML, and related technologies’ applications, security challenges, and potential solutions in several domains.

Chapter 8 introduces machine learning, a popular artificial intelligence subject that can self-improve with technology and user behavior data. Classifying machine learning as supervised, unsupervised, and reinforcement illustrates its basics. Complexity and data interaction affect machine learning system security, which seeks to impair learning capacity, induce errors, or steal crucial data. Defenses include data filtering, decision analysis, and model upgrades. The chapter 9 examines real-time detection and correction are being studied, and the following chapter applies machine learning and deep learning to adversarial attacks. Network architecture changes, training, and network additions are defenses. Protecting against white-box assaults and propagation methods are tricky. Chapter 10 discusses DRL security and privacy for hostile attack susceptibility. DRL, reinforcement learning, privacy, defense mechanisms, and research problems are presented. Next chapter covers machine learning algorithm training and testing for evasion, membership, and data poisoning. Cyber-physical systems must mitigate these risks. GDPR and CCPA data privacy are covered in Chapter 11. Introduces privacy-focused federated learning. The chapter covers federated learning’s categories, methods, possibilities, and difficulties and blockchain’s security. In conclusion, these Part II chapters discuss machine learning, deep learning, and data security, their importance, vulnerabilities, protection mechanisms, and regulatory effects on AI and modern technology.

Part III begins with chapter 13 and discusses cybersecurity’s growing importance due to the internet’s accessibility and data gathering. It highlights how artificial intelligence (AI), particularly ML and DL, prevents cyberattacks. ML can detect and halt assaults without human intervention using patterns and algorithms. The chapter examines ML and DL in intrusion detection systems, emphasizing DL’s feature extraction. However, it acknowledges storage and processing capacity issues. Chapter 14 discusses cybersecurity, which protects computing resources, networks, software, and data against threats like ransomware and denial-of-service attacks. Big data, the Internet of Things, and the digital world have made cybersecurity more important than ever (IoT). The chapter stresses the relevance of big data analytics and artificial intelligence in cybersecurity and intrusion and malware detection as key tools for defending against emerging threats. The Internet of Things (IoT) has transformed the digital landscape by linking many devices and promoting the Industrial Internet of Things (IIoT) in industry. Interconnectedness raises security risks and vulnerabilities. Machine learning (ML) and deep learning (DL) are helping IIoT researchers detect intrusions. Effective model training and real-world ML and DL applications are needed to create industrial network intrusion detection systems (IDSs). Future research will focus on improving IDS development with ML and ensemble learning.

The final section, chapter 16, examines IoTrapid’s expansion and suggests an IoT search engine (IoTSE) to manage its massive number of devices. Users, the IoT search engine, and data sources comprise IoTSE. Multiple layers solve web-crawler architecture issues. An alternate design, Named Data Network (NDN), and machine learning (ML) can improve IoTSE network traffic analysis. ML identifies interest flooding attacks (IFA) on NDN, a unique security challenge. Chapter 17 uses data mining and machine learning to detect electronic financial system fraud. Generational adversarial networks (GANs) are used to detect fraud and risk GAN-based attacks. Monitoring transaction data, altering legislation, and strengthening variable links help mitigate these attacks. In Chapter 18, Smart Healthcare Systems (SHS) based on the Internet of Medical Things and COVID-19 are developed (IoMT). An ensemble of autoencoders (AE) and one-class support vector machines (OCSVM) with metaheuristic optimization detects SHS anomalies. A formal attack analyzer reduces false alarms and simulates SHS attacks to assess susceptibility. Phishing attacks are growing tougher to detect as cyberattacks proliferate, according to Chapter 19. To detect phishing emails, it prioritizes ML and DL algorithms because they lack distinguishing features.

User-centric measures and behavioral models must be utilized together since users must trust and comprehend technical phishing detectors. This research provides an interpretative artifact for ML/DL phishing detectors using explainable AI (XAI) technologies like LIME and anchor explanations to extract cues from phishing emails as a second line of defense. This strategy promotes technical phishing detectors and user collaboration to prevent phishing. Technology’s impact on society and education, IoT security, and explainable AI’s phishing detection are discussed. First, we evaluated how technology has affected society and education. Technology has increased knowledge and learning but also privacy, screen time, and digital divide issues. Teachers must teach digital literacy and manage the changing reality. We examined IoT security challenges next (IoT). Cybercriminals have more attack surfaces with connected gadgets in homes, communities, and industries. IoT ecosystems and sensitive data were protected by encryption, authentication, and software updates. Phishing detection by explainable AI (XAI) was last. User-centric methods like XAI can help individuals recognize and respond to phishing assaults despite technical solutions. Today’s speeches show technology’s complicated effects on society and the need to consider its merits and cons. Education, IoT security, and cybersecurity require informed decision-making and user empowerment in the digital age.

Fajar Pitarsi Dharma
Department of Industrial and System Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
Teknik Pembuatan Benang, AK-Tekstil Solo, Solo, Indonesia
[email protected]
Moses Laksono Singgih
Department of Industrial and System Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
Hamdan S. Bintang
Teknik Pembuatan Benang, AK-Tekstil Solo, Solo, Indonesia

Acknowledgment

The authors would like to express gratitude to Lembaga Pengelola Dana Pendidikan (LPDP) for supporting the publication of this reviews.