Sergey Butakov

Malware is a growing threat to all mobile platforms and hundreds of new malicious applications are being detected every day. At the same time, the development of automated software obfuscation techniques allows for the easy production of new malware variants even by attackers with entry-level programming skills. Such obfuscation techniques can evade the signature-based mechanism implemented in current antimalware technology. This paper presents the results of a study that examined how automated obfuscation techniques affect malicious and benign applications by two widely used malware detection approaches, namely static and dynamic analyses. The research explored 5000 samples of malware and benign programs and evaluated the impact of automated obfuscation on Android applications. The experimental results indicated that (1) up to 73% of the reviewed applications “survived” the automated obfuscation; (2) automated obfuscation reduced the detection ratio to 65–85% depending on the obfuscation method used. These findings call for a more active use of advanced malware detection methods in commonly used antivirus platforms.

Big data, like most technological innovations, brings noticeable benefits as well potential risks. Dataveillance using big data is becoming another dimension in the increasing privacy concerns of the workforce. Such concerns emanate from the tension between the correct use of employee personal data and information privacy in big data within and outside the work environment. It has evolved as employees are becoming increasingly cognizant of the ways in which employers can use technologies to monitor social media activities, internet interactions, emails and other online activities outside the work environment. The objective of this research paper is to recommend a set of guidelines which will be mapped to COBIT 5 framework to help medium and large organizations balance the tension between the increasing potential of big data and employee dataveillance privacy concerns in workplaces.

Information System Audit and Control Association (ISACA) proposed a preliminary idea on applying fault tree analysis to look at the root reasons for the IT risks outlined in COBIT 5 Risk Scenarios. So far, there was no prescriptive procedure/methodology, which could be used to build the fault tree. This research looked into various methodologies for building the fault tree and proposed a new methodology, which could be used for analysis of risks outlined in COBIT 5 Risk Scenarios document. The methodology has been developed specific to COBIT 5 processes to build the fault tree, which, in turn, can help to outline the common factors that lead to failure of the processes subsequently leading to a risk. Fault tree analysis, could help to improve processes and suggest potential mitigation strategy to improve management/governance of IT. The paper also includes a sample of using the proposed methodology on one of the risk scenarios in order to calculate minimal cut set of IT management practices that organization needs to focus on to address specific risks.

The security content automation includes configurations of large number of systems, installation of patches securely, verification of security-related configuration settings, compliance with security policies and regulatory requirements, and ability to respond quickly when new threats are discovered [1]. Although humans are important in information security management, humans sometimes introduce errors and inconsistencies in an organization due to manual nature of their tasks [2]. Security Content Automation Protocol was developed by the U.S. NIST to automate information security management tasks such as vulnerability and patch management, and to achieve continuous monitoring of security configurations in an organization. In this paper, SCAP is employed to develop an automated security configuration checklist for use in verifying Apple iOS device configuration against the defined security baseline to enforce policy compliance in an enterprise.

The chapter presents a case study of using data mining tools to solve the puzzle of inconsistency between students' in-class performance and the results of the final tests. Classical test theory cannot explain such inconsistency, while the classification tree generated by one of the well-known data mining algorithms has provided reasonable explanation, which was confirmed by course exit interviews. The experimental results could be used as a case study of implementing Artificial Intelligence-based methods to analyze course results. Such analyses equip educators with an additional tool that allows closing the loop between assessment results and course content and arrangements.