Use of Data Visualisation for Zero-Day Malware Detection
Joint Authors
Alazab, Mamoun
Venkatraman, Sitalakshmi
Source
Security and Communication Networks
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-02
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Information Technology and Computer Science
Abstract EN
With the explosion of Internet of Things (IoT) worldwide, there is an increasing threat from malicious software (malware) attackers that calls for efficient monitoring of vulnerable systems.
Large amounts of data collected from computer networks, servers, and mobile devices need to be analysed for malware proliferation.
Effective analysis methods are needed to match with the scale and complexity of such a data-intensive environment.
In today’s Big Data contexts, visualisation techniques can support malware analysts going through the time-consuming process of analysing suspicious activities thoroughly.
This paper takes a step further in contributing to the evolving realm of visualisation techniques used in the information security field.
The aim of the paper is twofold: (1) to provide a comprehensive overview of the existing visualisation techniques for detecting suspicious behaviour of systems and (2) to design a novel visualisation using similarity matrix method for establishing malware classification accurately.
The prime motivation of our proposal is to identify obfuscated malware using visualisation of the extended x86 IA-32 (opcode) similarity patterns, which are hard to detect with the existing approaches.
Our approach uses hybrid models wherein static and dynamic malware analysis techniques are combined effectively along with visualisation of similarity matrices in order to detect and classify zero-day malware efficiently.
Overall, the high accuracy of classification achieved with our proposed method can be visually observed since different malware families exhibit significantly dissimilar behaviour patterns.
American Psychological Association (APA)
Venkatraman, Sitalakshmi& Alazab, Mamoun. 2018. Use of Data Visualisation for Zero-Day Malware Detection. Security and Communication Networks،Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1213908
Modern Language Association (MLA)
Venkatraman, Sitalakshmi& Alazab, Mamoun. Use of Data Visualisation for Zero-Day Malware Detection. Security and Communication Networks No. 2018 (2018), pp.1-13.
https://search.emarefa.net/detail/BIM-1213908
American Medical Association (AMA)
Venkatraman, Sitalakshmi& Alazab, Mamoun. Use of Data Visualisation for Zero-Day Malware Detection. Security and Communication Networks. 2018. Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1213908
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1213908