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 Название: Data Exfiltration Threats and Prevention Techniques: Machine Learning and Memory-Based Data Security
Автор: Zahir Tari, Nasrin Sohrabi, Yasaman Samadi
Издательство: Wiley
Год: 2023
Страниц: 291
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
Data Exfiltration Threats and Prevention Techniques 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.
Data exfiltration is a type of cyberattacks that causes breaches of sensitive information. It is undoubtedly a critical issue for the modern world of data-centric services. In particular, the sectors of critical infrastructure (CI), information technology (IT), and mobile computing are the targets of advanced persistent threat (APT). Data breaches cause huge losses every year to a wide range of industries, including large enterprises such as Google, Facebook, Microsoft, to name a few. Furthermore, such breaches can have major impacts on national security if government departments or the military are targeted. Since the adversary constantly attacks the target using various system vulnerabilities (e.g. unknown or zero-day exploits), a prevention-based measure alone is not sufficient to thwart the adversary. To address such a problem, in this book, a holistic approach for data exfiltration detection based on three approaches for the detection of data breaches will be discussed in detail.
We begin the description of the technical content by providing basic background so to enable readers understand some of the fundamental and technical concepts/models covered in the remaining chapters of the book. The background covers, for example, basic knowledge of hidden Markov model (HMM), memory forensics, bag-of-words (BoW) model, and sparse distributed representations (SDRs). Cybersecurity threats are also covered, as these can cause a wide range of damage, including the physical destruction of an entire information systems facility. Recognizing different types of data security threats and the way that they steal sensitive information from individuals and organizations will give readers a clear understanding on how to protect their data. Hence, data security threats are explained, and various attacks are discussed, such as malware, denial of service (DoS), SQL injection, Emotet (malspam), social engineering and phishing, and man-in-the-middle (MITM) attacks. These attacks often access the high-value targets, such as nation states and major corporations, to steal the crucial data.
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.
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