ORCID
- Ghita, Bogdan: 0000-0002-1788-547X
Abstract
Detection and mitigation of modern malware are critical for the normal operation of an organisation. Traditional defence mechanisms are becoming increasingly ineffective due to the techniques used by attackers such as code obfuscation, metamorphism, and polymorphism, which strengthen the resilience of malware. In this context, the development of adaptive, more effective malware detection methods has been identified as an urgent requirement for protecting the IT infrastructure against such threats, and for ensuring security. In this paper, we investigate an alternative method for malware detection that is based on N-grams and machine learning. We use a dynamic analysis technique to extract an Indicator of Compromise (IOC) for malicious files, which are represented using N-grams. The paper also proposes TF-IDF as a novel alternative used to identify the most significant N-grams features for training a machine learning algorithm. Finally, the paper evaluates the proposed technique using various supervised machine-learning algorithms. The results show that Logistic Regression, with a score of 98.4%, provides the best classification accuracy when compared to the other classifiers used.
DOI
10.3390/electronics9111777
Publication Date
2020-10-26
Publication Title
Electronics
Volume
9
Issue
11
Embargo Period
2021-06-08
Organisational Unit
School of Engineering, Computing and Mathematics
First Page
1777
Last Page
1777
Recommended Citation
Ali, M., Shiaeles, S., Bendiab, G., & Ghita, B. (2020) 'MALGRA: Machine Learning and N-Gram Malware Feature Extraction and Detection System', Electronics, 9(11), pp. 1777-1777. Available at: https://doi.org/10.3390/electronics9111777
