Application of rank correlation, clustering and classification in information security

This article is devoted to experimental investigation of a novel application of a clustering technique introduced by the authors recently in order to use robust and stable consensus functions in information security, where it is often necessary to process large data sets and monitor outcomes in real time, as it is required, for example, for intrusion detection. Here we concentrate on a particular case of application to profiling of phishing websites. First, we apply several independent clustering algorithms to a randomized sample of data to obtain independent initial clusterings. Silhouette index is used to determine the number of clusters. Second, rank correlation is used to select a subset of features for dimensionality reduction. We investigate the effectiveness of the Pearson Linear Correlation Coefficient, the Spearman Rank Correlation Coefficient and the Goodman--Kruskal Correlation Coefficient in this application. Third, we use a consensus function to combine independent initial clusterings into one consensus clustering. Fourth, we train fast supervised classification algorithms on the resulting consensus clustering in order to enable them to process the whole large data set as well as new data. The precision and recall of classifiers at the final stage of this scheme are critical for the effectiveness of the whole procedure. We investigated various combinations of several correlation coefficients, consensus functions, and a variety of supervised classification algorithms.

User preference of cyber security awareness delivery methods

Operating systems and programmes are more protected these days and attackers have shifted their attention to human elements to break into the organisation's information systems. As the number and frequency of cyber-attacks designed to take advantage of unsuspecting personnel are increasing, the significance of the human factor in information security management cannot be understated. In order to counter cyber-attacks designed to exploit human factors in information security chain, information security awareness with an objective to reduce information security risks that occur due to human related vulnerabilities is paramount. This paper discusses and evaluates the effects of various information security awareness delivery methods used in improving end-users’ information security awareness and behaviour. There are a wide range of information security awareness delivery methods such as web-based training materials, contextual training and embedded training. In spite of efforts to increase information security awareness, research is scant regarding effective information security awareness delivery methods. To this end, this study focuses on determining the security awareness delivery method that is most successful in providing information security awareness and which delivery method is preferred by users. We conducted information security awareness using text-based, game-based and video-based delivery methods with the aim of determining user preferences. Our study suggests that a combined delivery methods are better than individual security awareness delivery method.

Information security governance: the art of detecting hidden malware

Detecting malicious software or malware is one of the major concerns in information security governance as malware authors pose a major challenge to digital forensics by using a variety of highly sophisticated stealth techniques to hide malicious code in computing systems, including smartphones. The current detection techniques are futile, as forensic analysis of infected devices is unable to identify all the hidden malware, thereby resulting in zero day attacks. This chapter takes a key step forward to address this issue and lays foundation for deeper investigations in digital forensics. The goal of this chapter is, firstly, to unearth the recent obfuscation strategies employed to hide malware. Secondly, this chapter proposes innovative techniques that are implemented as a fully-automated tool, and experimentally tested to exhaustively detect hidden malware that leverage on system vulnerabilities. Based on these research investigations, the chapter also arrives at an information security governance plan that would aid in addressing the current and future cybercrime situations.

A competitive three-level pruning technique for information security

The reduction of size of ensemble classifiers is important for various security applications. The majority of known pruning algorithms belong to the following three categories: ranking based, clustering based, and optimization based methods. The present paper introduces and investigates a new pruning technique. It is called a Three-Level Pruning Technique, TLPT, because it simultaneously combines all three approaches in three levels of the process. This paper investigates the TLPT method combining the state-of-the-art ranking of the Ensemble Pruning via Individual Contribution ordering, EPIC, the clustering of the K-Means Pruning, KMP, and the optimisation method of Directed Hill Climbing Ensemble Pruning, DHCEP, for a phishing dataset. Our new experiments presented in this paper show that the TLPT is competitive in comparison to EPIC, KMP and DHCEP, and can achieve better outcomes. These experimental results demonstrate the effectiveness of the TLPT technique in this example of information security application.

Contractor requirements for the control and security of TSCA confidential business information : security manual.

Mode of access: Internet.

21st National Information Systems Security Conference : building the information security bridge to the 21st century : October 5-8, 1998, Hyatt Regency Crystal City, Arlington, Va.

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Information resources management plan of the federal government.

Mode of access: Internet.

Land management systems : status of BLM's actions to improve information technology management : report to the Subcommittee on Interior and Related Agencies, Committee on Appropriations, House of Representatives /

"B-284472"--P. 3.