Malware detection through machine learning techniques

Amer, Ahmed and Abdul Aziz, Normaziah (2019) Malware detection through machine learning techniques. International Journal of Advanced Trends in Computer Science and Engineering, 8 (5). pp. 2408-2413. ISSN 2278-3091

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Abstract

Malware attack is a never-ending cyber security issue. Since traditional approaches are less efficient in detecting newly appeared malware, researchers are applying machine learning methods. In this research we started by an overview of the domain and went over available malware datasets. Then we discussed disadvantages of traditional Anti-Malware methods and reviewed possible Machine Learning techniques used in this domain. A study on EMBER dataset has been made with an objective of improving the baseline Gradient Boosted Decision Tree model by optimizing its hyper-parameter and eliminating noisy features from the dataset. EMBER dataset consists of 1.1M observations of static features extracted from executable files. Our optimized model has achieved 99.38% accuracy with 0.004 false positive rate in 7 minutes running time. We conclude that Machine Learning techniques are practical to be applied as anti-malware solutions including for Zero-day attacks.

Item Type:	Article (Journal)
Additional Information:	5505/76535
Uncontrolled Keywords:	Artificial Intelligence, Machine Learning, Cyber Security, Malware Analysis, Smart Anti-Malware, GBDT Algorithm, Anti-virus
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology
Depositing User:	Dr. Normaziah Abdul Aziz
Date Deposited:	23 Dec 2019 10:43
Last Modified:	26 Dec 2019 08:35
URI:	http://irep.iium.edu.my/id/eprint/76535

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