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%.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Classification of Malware programs using autoencoders based deep learning architecture and its application to the microsoft malware Classification challenge (BIG 2015) dataset




    Publication date :

    2017-06-01


    Size :

    364810 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Static Analysis through Topic Modeling and its Application to Malware Programs Classification

    Djaneye-Boundjou, Ouboti / Messay-Kebede, Temesguen / Kapp, David et al. | IEEE | 2019


    A Novel Malware Traffic Classification Method Based on Differentiable Architecture Search

    Shi, Yunxiao / Zhang, Xixi / He, Zhengran et al. | IEEE | 2022



    Convolutional Neural Networks as Classification Tools and Feature Extractors for Distinguishing Malware Programs

    Priyamvada Davuluru, Venkata Salini / Narayanan Narayanan, Barath / Balster, Eric J. | IEEE | 2019


    kNN Classification of Malware Data Dependency Graph Features

    Musgrave, John / Ralescu, Anca | IEEE | 2024