Elsevier

Information Sciences

Volume 231, 10 May 2013, Pages 1-3
Information Sciences

Editorial
Guest editorial: Special issue on data mining for information security

https://doi.org/10.1016/j.ins.2013.01.027Get rights and content

Introduction

Computer and communication systems are subject to repeated security attacks. Given the variety of new vulnerabilities discovered every day, the introduction of new attack schemes, and the ever-expanding use of the Internet, it is not surprising that the field of computer and network security has grown and evolved significantly in recent years. Attacks are so pervasive nowadays that many firms, especially large financial institutions, spend over 10% of their total information and communication technology budget directly on computer and network security. Changes in the type of attacks, such as the use of botnets and the identification of new vulnerabilities, have resulted in a highly dynamic threat landscape that is unamenable to traditional security approaches.

Data mining techniques which incorporate induction algorithms that explore data in order to discover hidden patterns and develop predictive models, have proved to be effective in tackling the aforementioned information security challenges. In recent years classification, associations rules, and clustering mechanisms, have all been used to discover and generalize attack patterns in order to develop powerful solutions for coping with the latest threats such as: distributed denial of service (DDoS) attacks, host-based intrusions [15], [17], data leakage, SPAM and malicious code including Trojan, Worms and computer viruses [8], [9], [13], [14], [16], [20].

Section snippets

The special issue

The papers in this special issue are clustered into four groups. The first group focuses on employing data mining techniques for coping with intrusion detection. The second group deals with using classification techniques to identify malicious code. The third group mainly addresses privacy preserving data mining. Finally, the fourth group presents new techniques for the detection of the presence of embedded secret messages (Steganalysis) using machine learning techniques.

In the following

Acknowledgements

We would like to thank all the authors who submitted papers for consideration to the special issue. We have received 55 papers from which we could include in the special issue 9 papers. We would especially like to thank the reviewers for their time and detailed reviews that helped us to decide which papers to include in the special issue. Finally, we would like to thank the Editor-in-Chief, Prof. Witold Pedrycz, and Prof. Paul P. Wang, Special Issue Editor, for their valuable guidance and

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