Data Exfiltration Threats and Prevention Techniques: Machine Learning and Memory-Based Data Security

Comprehensive resource covering threat prevention techniques for data exfiltration and applying machine learning applications to aid in identification and prevention

Data Exfiltration Threats and Prevention Techniques provides readers the knowledge needed to prevent and protect from malware attacks by introducing existing and recently developed methods in malware protection using AI, memory forensic, and pattern matching, presenting various data exfiltration attack vectors and advanced memory-based data leakage detection, and discussing ways in which machine learning methods have a positive impact on malware detection.

Providing detailed descriptions of the recent advances in data exfiltration detection methods and technologies, the authors also discuss details of data breach countermeasures and attack scenarios to show how the reader may identify a potential cyber attack in the real world.

Composed of eight chapters, this book presents a better understanding of the core issues related to the cyber-attacks as well as the recent methods that have been developed in the field.

In Data Exfiltration Threats and Prevention Techniques, readers can expect to find detailed information on:

• Sensitive data classification, covering text pre-processing, supervised text classification, automated text clustering, and other sensitive text detection approaches
• Supervised machine learning technologies for intrusion detection systems, covering taxonomy and benchmarking of supervised machine learning techniques
• Behavior-based malware detection using API-call sequences, covering API-call extraction techniques and detecting data stealing behavior based on API-call sequences
• Memory-based sensitive data monitoring for real-time data exfiltration detection and advanced time delay data exfiltration attack and detection

Aimed at professionals and students alike, Data Exfiltration Threats and Prevention Techniques highlights a range of machine learning methods that can be used to detect potential data theft and identifies research gaps and the potential to make change in the future as technology continues to grow.

《資料外洩威脅與防護技術》是一本全面的資源，涵蓋了防止資料外洩的技術以及應用機器學習來協助識別和預防的方法。

本書介紹了現有和最近發展的人工智能、記憶體取證和模式匹配等恶意軟體保護方法，以防止和保護免受惡意軟體攻擊。書中還介紹了各種資料外洩攻擊向量和高級基於記憶體的資料洩漏檢測方法，並討論了機器學習方法對恶意軟體檢測的積極影響。

本書提供了最近資料外洩檢測方法和技術的詳細描述，還討論了資料洩漏對策和攻擊場景的細節，以展示讀者如何在現實世界中識別潛在的網絡攻擊。

本書共分為八章，旨在提供對網絡攻擊核心問題的更好理解，以及該領域最新發展的方法。

在《資料外洩威脅與防護技術》中，讀者可以找到以下詳細信息：

- 敏感資料分類，包括文本預處理、監督式文本分類、自動文本聚類和其他敏感文本檢測方法
- 監督式機器學習技術用於入侵檢測系統，包括監督式機器學習技術的分類和評估
- 基於行為的恶意軟體檢測，使用API調用序列，包括API調用提取技術和基於API調用序列檢測資料竊取行為
- 基於記憶體的敏感資料監控，用於實時資料外洩檢測和高級時間延遲資料外洩攻擊和檢測

《資料外洩威脅與防護技術》針對專業人士和學生，突出了一系列可用於檢測潛在資料竊取的機器學習方法，並確定了研究缺口和未來技術發展的潛力。

Zahir Tari is Professor at RMIT and Research Director of the RMIT Centre of Cyber Security Research and Innovation.

Nasrin Sohrabi received a PhD in Computer Science from RMIT University, Australia. She is a Postdoctoral Research Fellow in Cloud, Systems and Security discipline, School of Computing Technologies, RMIT University and a core member of the RMIT Centre of Cyber Security Research and Innovation (CCSRI). She has several publications in highly ranked conferences and journals, including ICDE, IEEE Transactions on Services Computings, ACM Computing surveys, IEEE Transactions on Transportation systems, IEEE Transactions on Smart Grids.

Yasaman Samadi is a PhD student in Computer Science at RMIT University, Australia and a researcher in Quantum Cybersecurity. Yasaman has a Master's in Computer Architecture and worked as a quantum engineer at QBee.

Jakapan Suaboot received his PhD in Cybersecurity from RMIT, Australia. He previously worked as a Lecturer for the Department of Computer Engineering from Prince of Songkla University, Phuket, Thailand.

Zahir Tari是RMIT的教授，也是RMIT Centre of Cyber Security Research and Innovation的研究主任。

Nasrin Sohrabi在澳大利亞RMIT大學獲得計算機科學博士學位。她是RMIT大學計算機技術學院的雲端、系統和安全學科的博士後研究員，也是RMIT Centre of Cyber Security Research and Innovation (CCSRI)的核心成員。她在高排名的會議和期刊上發表了多篇論文，包括ICDE、IEEE Transactions on Services Computings、ACM Computing surveys、IEEE Transactions on Transportation systems和IEEE Transactions on Smart Grids。

Yasaman Samadi是澳大利亞RMIT大學計算機科學的博士生，也是量子網絡安全的研究員。Yasaman擁有計算機架構的碩士學位，曾在QBee擔任量子工程師。

Jakapan Suaboot在澳大利亞RMIT大學獲得了網絡安全的博士學位。他曾在泰國普吉島的Prince of Songkla大學計算機工程系擔任講師。