Intelligent Mobile Malware Detection

The popularity of Android mobile phones has caused more cybercriminals to create malware applications that carry out various malicious activities. The attacks, which escalated after the COVID-19 pandemic, proved there is great importance in protecting Android mobile devices from malware attacks. Intelligent Mobile Malware Detection will teach users how to develop intelligent Android malware detection mechanisms by using various graph and stochastic models. The book begins with an introduction to the Android operating system accompanied by the limitations of the state-of-the-art static malware detection mechanisms as well as a detailed presentation of a hybrid malware detection mechanism. The text then presents four different system call-based dynamic Android malware detection mechanisms using graph centrality measures, graph signal processing and graph convolutional networks. Further, it shows how most of the Android malware can be detected by checking the presence of a unique subsequence of system calls in its system call sequence. All the malware detection mechanisms presented in the book are based on the authors' recent research. The experiments are conducted with the latest Android malware samples and the malware samples are collected from public repositories. The source codes are also provided for easy implementation of the mechanisms. This book will be highly useful to Android malware researchers, developers, students and cyber security professionals to explore and build defense mechanisms against the ever-evolving Android malware.

Android手機的普及使得更多的網絡犯罪分子創建了執行各種惡意活動的惡意軟件應用程序。這些攻擊在COVID-19大流行之後不斷升級，證明了保護Android手機免受惡意軟件攻擊的重要性。《智能移動惡意軟件檢測》將教用戶如何通過使用各種圖形和隨機模型來開發智能Android惡意軟件檢測機制。該書首先介紹了Android操作系統，並介紹了現有靜態惡意軟件檢測機制的局限性，以及混合惡意軟件檢測機制的詳細介紹。接下來，書中介紹了四種基於系統調用的動態Android惡意軟件檢測機制，包括圖形中心性度量、圖形信號處理和圖形卷積網絡。此外，書中還展示了如何通過檢查系統調用序列中的唯一子序列的存在來檢測大多數Android惡意軟件。書中介紹的所有惡意軟件檢測機制都基於作者最近的研究。實驗使用最新的Android惡意軟件樣本進行，並且這些惡意軟件樣本是從公共存儲庫中收集的。書中還提供了源代碼，以便輕鬆實施這些機制。這本書對於Android惡意軟件研究人員、開發人員、學生和網絡安全專業人員來說非常有用，可以探索和建立對抗不斷演變的Android惡意軟件的防禦機制。

Tony Thomas is an associate professor at the Indian Institute of Information Technology and Management, Kerala (IIITM-K), India. He earned his master's and Ph.D degrees from IIT Kanpur. After completing his PhD, he pursued postdoctoral research at the Korea Advanced Institute of Science and Technology, Daejeon, South Korea. He later worked as a member of the research staff at the General Motors Research Lab, Bangalore, India, and the School of Computer Engineering, Nanyang Technological University, Singapore. His current research interests include malware analysis, biometrics, cryptography, machine learning, cyber threat prediction and visualization, digital watermarking, multimedia security and digital forensics.

Roopak Surendran is currently pursuing his PhD in the area of Android malware analysis at the Indian Institute of Information Technology and Management-Kerala (IIITM-K). Before joining his PhD program, he completed his MPhil degree in computer science with a specialization in cyber security from IIITM-K. He has published several research papers related to Android malware analysis and phishing detection. His research interests include malware analysis and phishing detection.

Teenu S. John holds an MTech degree in computer science with specialization in data security from TocH Institute of Science and Technology, part of the Cochin University of Science and Technology, Kerala, India and a BTech degree in Information Technology from the College of Engineering Perumon, also part of the Cochin University of Science and Technology, Kerala, India. She is currently doing her PhD in detecting adversarial attacks in Android malware detection at the Indian Institute of Information Technology and Management, Kerala (IIITM-K). Her research interests include malware analysis, machine learning for cybersecurity, data analytics and cyber threat detection.

Mamoun Alazab is an associate professor at the College of Engineering, IT and Environment at Charles Darwin University, Australia. Dr. Alazab's research is multidisciplinary and focuses on cybersecurity, which includes current and emerging issues in cyber environments such as cyber-physical systems, specifically cybercrime detection and prevention. His research focuses on human behavior, computational analysis like AI, machine learning, including deep learning for access control and biometrics, and human information processing concerns in security and privacy. He has published more than 150 research papers in many international journals and conferences. His research over the years has contributed to the development of several successful secure commercial systems in the areas of secure network systems, security tools, AI security, secure mobile systems, as well as cryptographic, cyber-physical systems for security, and applications where the devices are often lightweight.

Tony Thomas是印度信息技術與管理學院（IIITM-K）的副教授。他在印度理工學院坎普爾分校獲得碩士和博士學位。在完成博士學位後，他在韓國科學技術高等研究院進行博士後研究。之後，他曾在印度班加羅爾的通用汽車研究實驗室和新加坡南洋理工大學計算機工程學院擔任研究人員。他目前的研究興趣包括惡意軟件分析、生物特徵識別、加密學、機器學習、網絡威脅預測和可視化、數字水印、多媒體安全和數字取證。

Roopak Surendran目前在印度信息技術與管理學院喀拉拉邦（IIITM-K）攻讀Android惡意軟件分析的博士學位。在攻讀博士學位之前，他在IIITM-K獲得了計算機科學專業的MPhil學位，專攻網絡安全。他發表了多篇與Android惡意軟件分析和釣魚攻擊檢測相關的研究論文。他的研究興趣包括惡意軟件分析和釣魚攻擊檢測。

Teenu S. John擁有喀拉拉邦科欽科技學院（TocH Institute of Science and Technology）的計算機科學專業數據安全碩士學位，以及喀拉拉邦科欽科技學院（College of Engineering Perumon）的信息技術學士學位。她目前在印度信息技術與管理學院喀拉拉邦（IIITM-K）攻讀Android惡意軟件檢測中對抗性攻擊的博士學位。她的研究興趣包括惡意軟件分析、應用於網絡安全的機器學習、數據分析和網絡威脅檢測。

Mamoun Alazab是澳大利亞查爾斯達爾文大學工程、信息技術和環境學院的副教授。Alazab博士的研究領域跨學科，專注於網絡安全，包括網絡物理系統中的當前和新興問題，特別是網絡犯罪的檢測和預防。他的研究重點在於人類行為、人工智能、機器學習（包括深度學習）在訪問控制和生物特徵識別方面的應用，以及安全和隱私中的人類信息處理問題。他在許多國際期刊和會議上發表了150多篇研究論文。多年來，他的研究為安全網絡系統、安全工具、人工智能安全、安全移動系統以及輕量級設備的加密、網絡物理系統安全和設備應用的開發做出了貢獻。