The Android Malware Handbook: Detection and Analysis by Human and Machine (Paperback)

Written by machine-learning researchers and members of the Android Security team, this all-star guide tackles the analysis and detection of malware that targets the Android operating system.

This groundbreaking guide to Android malware distills years of research by machine learning experts in academia and members of Meta and Google's Android Security teams into a comprehensive introduction to detecting common threats facing the Android eco-system today.

Explore the history of Android malware in the wild since the operating system first launched and then practice static and dynamic approaches to analyzing real malware specimens. Next, examine machine learning techniques that can be used to detect malicious apps, the types of classification models that defenders can implement to achieve these detections, and the various malware features that can be used as input to these models. Adapt these machine learning strategies to the identifica-tion of malware categories like banking trojans, ransomware, and SMS fraud.

You'll:

• Dive deep into the source code of real malware
• Explore the static, dynamic, and complex features you can extract from malware for analysis
• Master the machine learning algorithms useful for malware detection
• Survey the efficacy of machine learning techniques at detecting common Android malware categories

The Android Malware Handbook's team of expert authors will guide you through the Android threat landscape and prepare you for the next wave of malware to come.

由機器學習研究人員和Android安全團隊成員撰寫，《Android惡意軟體手冊》是一本全明星指南，探討針對Android作業系統的惡意軟體的分析和偵測。

這本開創性的Android惡意軟體手冊將學術界的機器學習專家和Meta與Google的Android安全團隊多年的研究成果，結合成一本全面介紹當今Android生態系統所面臨的常見威脅的指南。從Android作業系統首次推出以來，探索野外Android惡意軟體的歷史，並實踐靜態和動態分析真實惡意軟體樣本的方法。接下來，研究可以用於檢測惡意應用程式的機器學習技術，防禦者可以實施哪些分類模型來實現這些檢測，以及可以用作這些模型輸入的各種惡意軟體特徵。將這些機器學習策略適應於銀行木馬、勒索軟體和短信詐騙等惡意軟體類別的識別。您將會：

• 深入研究真實惡意軟體的原始碼


• 探索從惡意軟體中提取的靜態、動態和複雜特徵，以進行分析


• 掌握對惡意軟體檢測有用的機器學習演算法


• 調查機器學習技術在檢測常見Android惡意軟體類別方面的效果

Qian Han, Research Scientist at Meta since 2021, received his PhD in Computer Science from Dartmouth College and his Bachelor's in Electronic Engineering from Tsinghua University, Beijing, China.

Salvador Mandujano, Security Engineering Manager at Google, has led product security engineering, malware reverse engineering and payments security teams. Before Google, he held senior security research and architecture positions at Intel and Nvidia. He has a PhD in Artificial Intelligence from Tecnológico de Monterrey, an MSc in Computer Science from Purdue, an MBA from The University of Texas, and a BSc in Computer Engineering from Universidad Nacional Autónoma de México.

Sebastian Porst is manager of Google's Android Application Security Research team, which tries to predict or research novel attacks on Android devices and Android users by malware or through app vulnerabilities. He has an MSc Masters from Trier University of Applied Sciences, Germany in 2007.

V.S. Subrahmanian is the Walter P. Murphy Professor of Computer Science and Buffet Faculty Fellow in the Buffet Institute of Global Affairs at Northwestern University. Prof. Subrahmanian is one of the world's foremost experts at the intersection of AI and security issues. He has written eight books, edited ten, and published over 300 refereed articles.

Sai Deep Tetali, Principal Engineer and Tech Lead Manager at Meta, works on privacy solutions for augmented and virtual reality applications. He spent 5 years at Google developing machine learning techniques to detect Android malware and has a PhD from University of California Los Angeles.

Yanhai Xiong is currently an Assistant Professor in the Department of Computer Science and Engineering at the University of Louisville. She has a PhD from Nanyang Technological University focusing on applying AI techniques to improve the efficiency of electric vehicle infrastructure and a BS in Engineering from the University of Science and Technology of China.

Qian Han，自2021年起在Meta擔任研究科學家，他在達特茅斯學院獲得計算機科學博士學位，並在中國清華大學獲得電子工程學士學位。

Salvador Mandujano，在Google擔任安全工程經理，曾領導產品安全工程、惡意軟體逆向工程和支付安全團隊。在加入Google之前，他在英特爾和Nvidia擔任高級安全研究和架構職位。他擁有墨西哥蒙特雷科技大學的人工智慧博士學位，普渡大學的計算機科學碩士學位，德克薩斯大學的工商管理碩士學位，以及墨西哥國立自治大學的計算機工程學士學位。

Sebastian Porst是Google Android應用程式安全研究團隊的經理，該團隊致力於預測或研究針對Android設備和用戶的新型攻擊，包括惡意軟體或應用程式漏洞。他於2007年在德國特里爾應用科學大學獲得碩士學位。

V.S. Subrahmanian是西北大學計算機科學沃爾特·P·墨菲教授和Buffet全球事務研究所的Buffet教職研究員。Subrahmanian教授是人工智慧和安全問題交叉領域的世界頂尖專家之一。他撰寫了八本書，編輯了十本書，發表了300多篇經過同行評審的文章。

Sai Deep Tetali是Meta的首席工程師和技術領導經理，致力於增強現實和虛擬實境應用程式的隱私解決方案。他在Google度過了5年的時間，開發機器學習技術來檢測Android惡意軟體，並在加州洛杉磯大學獲得博士學位。

Yanhai Xiong目前是路易斯維爾大學計算機科學和工程系的助理教授。她在新加坡南洋理工大學獲得博士學位，專注於應用人工智慧技術改善電動車基礎設施的效率，並在中國科學技術大學獲得工程學學士學位。