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Canali, D., Lanzi, A., Balzarotti, D., Kruegel, C., Christodorescu, M. and Kirda, E. (2012) A Quantitative Study of Accuracy in System Call-Based Malware Detection. Proceedings of the 2012 International Symposium on Software Testing and Analysis (ISSTA 2012), Minneapolis, 15-20 July 2012, 122-132. http://dx.doi.org/10.1145/2338965.2336768

has been cited by the following article:

• TITLE: Evaluation of Modified Vector Space Representation Using ADFA-LD and ADFA-WD Datasets

  AUTHORS: Bhavesh Borisaniya, Dhiren Patel

  KEYWORDS: System Call Trace, Vector Space Model, Modified Vector Space Representation, ADFA-LD, ADFA-WD

  JOURNAL NAME: Journal of Information Security, Vol.6 No.3, July 28, 2015

  ABSTRACT: Predicting anomalous behaviour of a running process using system call trace is a common practice among security community and it is still an active research area. It is a typical pattern recognition problem and can be dealt with machine learning algorithms. Standard system call datasets were employed to train these algorithms. However, advancements in operating systems made these datasets outdated and un-relevant. Australian Defence Force Academy Linux Dataset (ADFA-LD) and Australian Defence Force Academy Windows Dataset (ADFA-WD) are new generation system calls datasets that contain labelled system call traces for modern exploits and attacks on various applications. In this paper, we evaluate performance of Modified Vector Space Representation technique on ADFA-LD and ADFA-WD datasets using various classification algorithms. Our experimental results show that our method performs well and it helps accurately distinguishing process behaviour through system calls.