{"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/sequential-embedding-based-attentive-sea","title":"Sequential Embedding-based Attentive (SEA) classifier for malware classification","arxiv_id":"2302.05728","date":"2023-02-11","proceeding":null,"authors":["Muhammad Ahmed","Anam Qureshi","Jawwad Ahmed Shamsi","Murk Marvi"],"abstract":"The tremendous growth in smart devices has uplifted several security threats. One of the most prominent threats is malicious software also known as malware. Malware has the capability of corrupting a device and collapsing an entire network. Therefore, its early detection and mitigation are extremely important to avoid catastrophic effects. In this work, we came up with a solution for malware detection using state-of-the-art natural language processing (NLP) techniques. Our main focus is to provide a lightweight yet effective classifier for malware detection which can be used for heterogeneous devices, be it a resource constraint device or a resourceful machine. Our proposed model is tested on the benchmark data set with an accuracy and log loss score of 99.13 percent and 0.04 respectively.","url_abs":"https://arxiv.org/abs/2302.05728v1","url_pdf":"https://arxiv.org/pdf/2302.05728v1.pdf","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":[{"paper_slug":"sequential-embedding-based-attentive-sea","repo_url":"https://github.com/Muhammad4hmed/SEA","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"malware-classification","task_name":"Malware Classification"},{"task_slug":"malware-detection","task_name":"Malware Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/malware-classification-on-microsoft-malware","task":"Malware Classification","dataset":"Microsoft Malware Classification Challenge","model":"SEA","rank_in_archive_order":7,"of":29,"metrics":{"Accuracy (10-fold)":"0.9912","LogLoss":"0.0431","Macro F1 (10-fold)":"0.9908"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}