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Machine Learning for CybersecurityNew Approach to Malware Detection Using Optimized Convolutional Neural Network

Machine Learning for Cybersecurity: New Approach to Malware Detection Using Optimized... [Cybercrimes have become a multibillion-dollar industry in the recent years. Most cybercrimes/cyberattacks involve deploying some type of malware. Malware that viciously targets every industry, every sector, every enterprise, and even individuals has shown its capabilities to take entire business organizations offline and cause significant financial damage in billions of dollars annually. Malware authors are constantly evolving in their attack strategies and sophistication and are developing malware that is difficult to detect and can lay dormant in the background for quite some time in order to evade security controls. Given the above argument, traditional approaches to malware detection are no longer effective. As a result, deep learning models have become an emerging trend to detect and classify malware. This paper proposes a new convolutional deep learning neural network to accurately and effectively detect malware with high precision. This paper is different than most other papers in the literature in that it uses an expert data science approach by developing a convolutional neural network from scratch to establish a baseline of the performance model first, explores and implements an improvement model from the baseline model, and finally evaluates the performance of the final model. The baseline model initially achieves 98% accurate rate, but after increasing the depth of the CNN model, its accuracy reaches 99.183 which outperforms most of the CNN models in the literature. Finally, to further solidify the effectiveness of this CNN model, we use the improved model to make predictions on new malware samples within our dataset.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Machine Learning for CybersecurityNew Approach to Malware Detection Using Optimized Convolutional Neural Network

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Publisher
Springer International Publishing
Copyright
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
ISBN
978-3-031-15892-6
Pages
13 –35
DOI
10.1007/978-3-031-15893-3_2
Publisher site
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Abstract

[Cybercrimes have become a multibillion-dollar industry in the recent years. Most cybercrimes/cyberattacks involve deploying some type of malware. Malware that viciously targets every industry, every sector, every enterprise, and even individuals has shown its capabilities to take entire business organizations offline and cause significant financial damage in billions of dollars annually. Malware authors are constantly evolving in their attack strategies and sophistication and are developing malware that is difficult to detect and can lay dormant in the background for quite some time in order to evade security controls. Given the above argument, traditional approaches to malware detection are no longer effective. As a result, deep learning models have become an emerging trend to detect and classify malware. This paper proposes a new convolutional deep learning neural network to accurately and effectively detect malware with high precision. This paper is different than most other papers in the literature in that it uses an expert data science approach by developing a convolutional neural network from scratch to establish a baseline of the performance model first, explores and implements an improvement model from the baseline model, and finally evaluates the performance of the final model. The baseline model initially achieves 98% accurate rate, but after increasing the depth of the CNN model, its accuracy reaches 99.183 which outperforms most of the CNN models in the literature. Finally, to further solidify the effectiveness of this CNN model, we use the improved model to make predictions on new malware samples within our dataset.]

Published: Sep 24, 2022

Keywords: Convolutional neural networks; Deep learning; Malware detection; Image features; Malware visualization; Malimg dataset; Malware classification

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