Visible to the public Biblio

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2020-10-29
Priyamvada Davuluru, Venkata Salini, Narayanan Narayanan, Barath, Balster, Eric J..  2019.  Convolutional Neural Networks as Classification Tools and Feature Extractors for Distinguishing Malware Programs. 2019 IEEE National Aerospace and Electronics Conference (NAECON). :273—278.

Classifying malware programs is a research area attracting great interest for Anti-Malware industry. In this research, we propose a system that visualizes malware programs as images and distinguishes those using Convolutional Neural Networks (CNNs). We study the performance of several well-established CNN based algorithms such as AlexNet, ResNet and VGG16 using transfer learning approaches. We also propose a computationally efficient CNN-based architecture for classification of malware programs. In addition, we study the performance of these CNNs as feature extractors by using Support Vector Machine (SVM) and K-nearest Neighbors (kNN) for classification purposes. We also propose fusion methods to boost the performance further. We make use of the publicly available database provided by Microsoft Malware Classification Challenge (BIG 2015) for this study. Our overall performance is 99.4% for a set of 2174 test samples comprising 9 different classes thereby setting a new benchmark.

2018-06-20
Kebede, T. M., Djaneye-Boundjou, O., Narayanan, B. N., Ralescu, A., Kapp, D..  2017.  Classification of Malware programs using autoencoders based deep learning architecture and its application to the microsoft malware Classification challenge (BIG 2015) dataset. 2017 IEEE National Aerospace and Electronics Conference (NAECON). :70–75.

Distinguishing and classifying different types of malware is important to better understanding how they can infect computers and devices, the threat level they pose and how to protect against them. In this paper, a system for classifying malware programs is presented. The paper describes the architecture of the system and assesses its performance on a publicly available database (provided by Microsoft for the Microsoft Malware Classification Challenge BIG2015) to serve as a benchmark for future research efforts. First, the malicious programs are preprocessed such that they are visualized as gray scale images. We then make use of an architecture comprised of multiple layers (multiple levels of encoding) to carry out the classification process of those images/programs. We compare the performance of this approach against traditional machine learning and pattern recognition algorithms. Our experimental results show that the deep learning architecture yields a boost in performance over those conventional/standard algorithms. A hold-out validation analysis using the superior architecture shows an accuracy in the order of 99.15%.