Abstract
In recent years the amount of malware spreading through the internet and infecting computers and other communication devices has tremendously increased. To date, countless techniques and methodologies have been proposed to detect and neutralize these malicious agents. However, as new and automated malware generation techniques emerge, a lot of malware continues to be produced, which can bypass some state-of-the-art malware detection methods. Therefore, there is a need for the classification and detection of these adversarial agents that can compromise the security of people, organizations, and countless other forms of digital assets. In this paper, we propose a spatial attention and convolutional neural network (SACNN) based on deep learning framework for image-based classification of 25 well-known malware families with and without class balancing. Performance was evaluated on the Malimg benchmark dataset using precision, recall, specificity, precision, and F1 score on which our proposed model with class balancing reached 97.42%, 97.95%, 97.33%, 97.11%, and 97.32%. We also conducted experiments on SACNN with class balancing on benign class, also produced above 97%. The results indicate that our proposed model can be used for image-based malware detection with high performance, despite being simpler as compared to other available solutions.
Highlights
Malware is malicious software developed intentionally to cause harm to computer systems
The remaining sections are as follows: Section 2 presents an extensive literature review; Section 3 explains in detail the malware benchmark dataset used along with data pre-processing, and highlights the technologies used and their inherent limitations due to which we pivot towards more reliable methods; Section 4 describes experimental details such as setup, parametrization, and discusses the results along with important visualizations; and Sections 5 and 6 present the discussion, limitations, and conclusions, where we describe the processes and highlight important details
The spatial attention-based deep learning model proposed in this paper has some advantages over other machine learning and deep learning models proposed by other authors for the malware recognition task
Summary
Malware ( known as malicious program) is malicious software developed intentionally to cause harm to computer systems. 360,000 new malware files were detected every day in 2020, and the number of files found daily has increased by 5.2%. This rapid growth in malware production and distribution became possible due to the use of intelligent and automatic malware generation software such as SpyEye of Zeus and denial of service [2,3]. Phishing attacks are on the rise in many cases, leading to tricking the recipient into clicking a malicious link that can lead to the installation of malicious software
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