SpringerBriefs on Cyber Security Systems and Networks – serie

This book brings together the essential methodologies required to understand the advancement of digital technologies into digital transformation, as well as to protect them against cyber threat vulnerabilities (in this context cybersecurity attack ontology is included, modeling different types of adversary knowledge).

This book presents the latest research on honeypots and their applications. After introducing readers to the basic concepts of honeypots and common types, it reviews various honeypot frameworks such as web-server-based, client-based, shadow and artificially intelligent honeypots. In addition, it offers extensive information on the contribution of honeypots in some of the most popular malware research area such as DDoS, Worm, APT, forensics and Bot attacks.The book subsequently tackles the issue of honeypot countermeasures, shows many of the tricks often used by hackers to discover honeypots, and proposes a counter-countermeasure to help conceal them. It then puts forward a new framework that integrates various novel concepts, and which can feasibly be used for the detection of potential ransomware and bitcoin. As such, the book provides non-experts with a concise guide to honeypots, and will also benefit practitioners working on security systems.

This book provides an in-depth understanding of big data challenges to digital forensic investigations, also known as big digital forensic data. It also develops the basis of using data mining in big forensic data analysis, including data reduction, knowledge management, intelligence, and data mining principles to achieve faster analysis in digital forensic investigations. By collecting and assembling a corpus of test data from a range of devices in the real world, it outlines a process of big data reduction, and evidence and intelligence extraction methods. Further, it includes the experimental results on vast volumes of real digital forensic data. The book is a valuable resource for digital forensic practitioners, researchers in big data, cyber threat hunting and intelligence, data mining and other related areas.

This book presents current research in the area of advanced monitoring in P2P botnets, and uses a dual-perspective approach to discuss aspects of botnet monitoring in-depth. First, from the perspective of a defender, e.g. researchers, it introduces advanced approaches to successfully monitor botnets, taking the presence of current botnet anti-monitoring mechanisms into consideration. Then, adopting a botmaster perspective to anticipate the advances in future botnets, it introduces advanced measures to detect and prevent monitoring activities. All the proposed methods were evaluated either using real-world data or in a simulation scenario. In addition to providing readers with an in-depth understanding of P2P botnets, the book also analyzes the implications of the various design choices of recent botnets for effectively monitoring them. It serves as an excellent introduction to new researchers and provides a useful review for specialists in the field.

This book provides an in-depth understanding of big data challenges to digital forensic investigations, also known as big digital forensic data. It also develops the basis of using data mining in big forensic data analysis, including data reduction, knowledge management, intelligence, and data mining principles to achieve faster analysis in digital forensic investigations. By collecting and assembling a corpus of test data from a range of devices in the real world, it outlines a process of big digital forensic data analysis for evidence and intelligence. It includes the results of experiments on vast volumes of real digital forensic data. The book is a valuable resource for digital forensic practitioners, researchers in big data, cyber threat hunting and intelligence, data mining and other related areas.

This book presents recent advances in intrusion detection systems (IDSs) using state-of-the-art deep learning methods.

This book provides a comprehensive study of the state of the art in location privacy for mobile applications. It presents an integrated five-part framework for location privacy research, which includes the analysis of location privacy definitions, attacks and adversaries, location privacy protection methods, location privacy metrics, and location-based mobile applications. In addition, it analyses the relationships between the various elements of location privacy, and elaborates on real-world attacks in a specific application. Furthermore, the book features case studies of three applications and shares valuable insights into future research directions. Shedding new light on key research issues in location privacy and promoting the advance and development of future location-based mobile applications, it will be of interest to a broad readership, from students to researchers and engineers in the field.

This book discusses the state-of-the-art in privacy-preserving deep learning (PPDL), especially as a tool for machine learning as a service (MLaaS), which serves as an enabling technology by combining classical privacy-preserving and cryptographic protocols with deep learning.

This book provides a thorough overview of the evolution of privacy-preserving machine learning schemes over the last ten years, after discussing the importance of privacy-preserving techniques.