Information security disclosure : a case study

New social networking systems such as Facebook are an ever evolving and developing means of social interaction, which is not only being used to disseminate information to family, friends and colleagues but as a way of meeting and interacting with "strangers" through the advent of a large number of social applications. This paper will focus upon the impact of Generation F - the Facebook Generation and their attitudes to security. The paper will be based around discussing the findings of a major UK case study and the implications that this has. The case study identifies 51 recommendations to improve the situation of data security within the military of the UK. These recommendations will be the data for the analysis and will form an overview of the case study’s point of view as regards the younger generation and data security. This paper will suggest another interpretation of the results supplied by Burton.

Management framework for corporate telecommunication and data centre information security

Network security, particularly Internet security, is at the forefront of business and government networks. This research has discovered weaknesses in current professional practice, particularly in mitigation strategies to reduce the impacts of security violations in corporate telecommunications and data centres. The importance of integrating security policies, processes and operational practice is demonstrated. Leadership models and innovation mechanisms best suited to improved security design are also identified.

Information security governance : when compliance becomes more important than security

Current security governance is often based on a centralized decision making model and still uses an ineffective 20th century risk management approach to security. This approach is relatively simple to manage since it needs almost no security governance below the top enterprise level where most decisions are made. However, while there is a role for more corporate governance, new regulations, and improved codes of best practice to address current weak organizational security practices, this may not be sufficient in the current dynamic security environment. Organizational information security must adapt to changing conditions by extending security governance to middle management as well as system/network administrators. Unfortunately the lack of clear business security objectives and strategies at the business unit level is likely to result in a compliance culture, where those responsible for implementing information security are more interested in complying with organizational standards and policies than improving security itself.

An application of novel clustering technique for information security

This article presents experimental results devoted to a new application of the novel clustering technique introduced by the authors recently. Our aim is to facilitate the application of 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 the 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, we use a consensus function to combine these independent clusterings into one consensus clustering . Feature ranking is used to select a subset of features for the consensus function. Third, 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 effectiveness of the whole procedure. We investigated various combinations of three consensus functions, Cluster-Based Graph Formulation (CBGF), Hybrid Bipartite Graph Formulation (HBGF), and Instance-Based Graph Formulation (IBGF) and a variety of supervised classification algorithms. The best precision and recall have been obtained by the combination of the HBGF consensus function and the SMO classifier with the polynomial kernel.

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.

Does traditional security risk assessment have a future in information security?

The current information security standards still advocate the use of risk assessment in the prioritisation of security investments. However, prior research on the use of risk assessment methodologies in organisational security has shown that the use of the traditional monolithic risk assessment process described in the current risk management standard is simply not practical at the organisational level. This paper first examines the problems in performing a systematic risk assessment and then discusses the limitations of a traditional risk assessment. To address these limitations, this paper proposes splitting up the current monolithic risk assessment process. The result is an information security assessment framework that puts greater emphasis on situational awareness and allows for better decision making on the prioritization of security investments.

Matrix-based pairwise key establishment in wireless mesh networks using deployment knowledge

Due to the nature of wireless transmission, communication in wireless mesh networks (WMNs) is vulnerable to many adversarial activities including eavesdropping. Pairwise key establishment is one of the fundamental issues in securing WMNs. This paper presents a new matrix-based pairwise key establishment scheme for mesh clients. Our design is motivated by the fact that in WMNs, mesh routers are more powerful than mesh clients, both in computation and communication. By exploiting this heterogeneity, expensive operations can be delegated to mesh routers, which help alleviate the overhead of mesh clients during key establishment. The new scheme possesses two desirable features: (1) Neighbor mesh clients can directly establish pairwise keys; and (2) Communication and storage costs at mesh clients are significantly reduced.

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.