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Data analytics and decision support for cybersecurity : trends, methodologies and applications
Carrascosa I., Kalutarage H., Huang Y., Springer International Publishing, New York, NY, 2017. 270 pp. Type: Book (978-3-319594-38-5)
Date Reviewed: Aug 13 2018

Data analytics and decision support have recently influenced many fields; cybersecurity is one of them. This book, published in Springer’s “Data Analytics” series, is on data analytics and decision support applications for cybersecurity. The book has two parts. The first part comprises seven regular chapters, whereas the second part includes two invited chapters.

The first part begins with chapter 1, which “focuses on anomaly-based network intrusion detection.” The authors use data mining techniques to overcome the difficulties in utilizing anomaly-based network intrusion detection.

Chapter 2 studies “the problem of detecting insider threats in organizations.” It looks at some of the key challenges confronted by insider threat detection systems, for example, the hardships faced in cutting down on false alarms. The chapter demonstrates the benefits of pairing visual analytics with machine learning to arrive at well-informed decisions.

Nowadays, malware affects more than just desktop computer systems; mobile devices are also significantly impacted. Chapter 3 concentrates on collusion attacks, specifically on Android-based mobile devices. By compounding authorizations from multiple apps, attackers can attempt more serious threats. To deal with these attacks, the authors present two analysis methods that evaluate the possible dangers of apps that may perhaps turn out to be responsible for a collusion attack. Apps evaluated as untrustworthy are later analyzed in more detail, “to confirm whether an actual collusion exists.” The chapter includes recent research in app collusion analysis.

Chapter 4 examines the dynamic analysis of runtime opcodes to overcome the ravaging consequences of malware. The authors claim that they can discover malicious software with substantial exactness in real-world applications. They employ n-gram analysis and assert better malware classification accuracy than prior comparable research.

Chapter 5 introduces a scalable lightweight intrusion detection system (IDS) framework that uses a statistical decision-making engine to discover mistrustful forms of activity in network systems. The authors exemplify how statistical analysis helps identify “the normality patterns in the network data being analyzed.”

Chapter 6 emphasizes the importance of cybersecurity in e-learning systems. The author presents two frameworks for defeating cheating in online examinations, and demonstrates their superiority over existing approaches. Biometric authentication is used to guarantee examinations devoid of cheating.

Chapter 7 scrutinizes several well-known classification approaches: “support vector machines, principal component analytics, and random forest ensembles.” These approaches are compared with respect to “noisy data and its impact on classification accuracy.” The author develops a noise removal algorithm and studies the effect of factors such as lopsidedness, sample ratios, and the classification technique on classification performance.

Part 2 starts with chapter 8. The authors underscore the key role of cybersecurity in smart power systems. The primary indicators for detecting cyberattacks in these circumstances include power consumption and forecasting information. The authors establish a “method based on Gaussian process regression and fuzzy logic inference, aimed at analyzing, reasoning, and learning from load demand information in smart grids.” Their system is able to arrive at assured decisions to discover if abuse by an attacker is indeed happening.

Chapter 9 provides a broad “overview of analytics approaches based on data provenance, effective security visualization techniques, cybersecurity standards, and decision support applications.” The authors indicate the possible gains of incorporating data provenance and visualization techniques into decision making in information technology (IT) security settings. They introduce a new security visualization standard and report on its main rules of thumb and law enforcement implications.

All of the book’s contributors are academics from universities, except for one author from industry. Thus, the contribution from industry is negligible and the book overwhelmingly offers an academic perspective. As commercial firms predominantly provide solutions to real-world cybersecurity problems, I wish there had been significant contributions from industry experts. The book includes many illustrations, some of which are in color; I wish some of these were more readable. Author biographies for the invited chapters alone are provided; short biographies of the regular chapters’ contributors would have also been helpful. Every chapter includes many useful references to the literature, but some are not current. One major shortcoming of the book is that it does not have author or subject indices. The book provides a panoramic view of some emerging data analytics and decision support applications for cybersecurity.

Reviewer:  S. V. Nagaraj Review #: CR146198 (1811-0557)
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