{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/designing-deep-convolutional-neural-networks-1","title":"Designing Deep Convolutional Neural Networks using a Genetic Algorithm for Image-based Malware Classification","arxiv_id":null,"date":"2022-07-18","proceeding":"IEEE Congress on Evolutionary Computation (CEC) 2022 7","authors":["Cornelius Paardekooper","Nasimul Noman","Raymond Chiong","Vijay Varadharajan"],"abstract":"In recent years, deep Convolutional Neural Networks (CNNs) have shown great potential in malware classification. CNNs, which are originally designed for image processing, identify malware binaries visualised as images. Despite offering promising performance, these human-designed networks are very large requiring more resources to train and deploy them. Evolutionary algorithms have been successfully used in designing deep neural networks automatically for different application domains. In this work, we use a Genetic Algorithm (GA) to optimise the CNN topology and hyperparameters for image-based malware classification. Computational experiments with two different malware datasets, Malimg and Microsoft Malware, show that the GA-evolved networks are very competitive to the networks designed by experts in classifying malware, yet they are also considerably smaller in size comparison.","url_abs":"https://ieeexplore.ieee.org/document/9870218","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9870218","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"evolutionary-algorithms","task_name":"Evolutionary Algorithms"},{"task_slug":"malware-classification","task_name":"Malware Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/malware-classification-on-malimg-dataset","task":"Malware Classification","dataset":"Malimg Dataset","model":"GA Designed Deep CNN","rank_in_archive_order":2,"of":5,"metrics":{"Accuracy":"0.985","Macro F1":"0.9391"},"uses_additional_data":false},{"leaderboard":"/sota/malware-classification-on-microsoft-malware","task":"Malware Classification","dataset":"Microsoft Malware Classification Challenge","model":"GA Designed Deep CNN","rank_in_archive_order":29,"of":29,"metrics":{"Accuracy":"0.9307"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}