Author : Eric J. Barbin
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (143 download)
Book Synopsis Image-based Android Malware Detection and Classification with Convolutional Neural Networks by : Eric J. Barbin
Download or read book Image-based Android Malware Detection and Classification with Convolutional Neural Networks written by Eric J. Barbin and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of machine learning for detecting and classifying malware is becoming increasingly popular amongst cybersecurity researchers. Unlike traditional methods which depend on known malware signatures and features hand-crafted by cybersecurity domain experts, machine learning techniques can perform detection and classification on previously unseen samples. With deep learning (DL) methods specifically, the manual process of feature extraction is replaced with a deep neural network (DNN) capable of performing feature learning and classification. Current research shows that techniques borrowed from the field of computer vision are particularly effective, where malware binaries are represented as images and processed through a Convolutional Neural Network (CNN) to perform classification. While this area of research is gaining interest, there are few standard datasets available and until recently, most research has been conducted against small and private datasets making it difficult to compare existing research, reproduce results, and develop new methodologies. Additionally, much of the research in this domain predominantly focuses on Microsoft Windows malware, making it difficult to significantly advance malware detection and classification research as it relates to other platforms. However, as the use of mobile devices and services continues to grow, so does the interest in developing malware for mobile platforms. Therefore, this work aims to expand current research related to image-based malware detection and classification with CNNs to achieve state-of-the-art results against a dataset comprised of malware developed for the Android operating system (OS).