{"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/adversarial-deep-learning-for-robust","title":"Adversarial Deep Learning for Robust Detection of Binary Encoded Malware","arxiv_id":"1801.02950","date":"2018-01-09","proceeding":null,"authors":["Abdullah Al-Dujaili","Alex Huang","Erik Hemberg","Una-May O'Reilly"],"abstract":"Malware is constantly adapting in order to avoid detection. Model based\nmalware detectors, such as SVM and neural networks, are vulnerable to so-called\nadversarial examples which are modest changes to detectable malware that allows\nthe resulting malware to evade detection. Continuous-valued methods that are\nrobust to adversarial examples of images have been developed using saddle-point\noptimization formulations. We are inspired by them to develop similar methods\nfor the discrete, e.g. binary, domain which characterizes the features of\nmalware. A specific extra challenge of malware is that the adversarial examples\nmust be generated in a way that preserves their malicious functionality. We\nintroduce methods capable of generating functionally preserved adversarial\nmalware examples in the binary domain. Using the saddle-point formulation, we\nincorporate the adversarial examples into the training of models that are\nrobust to them. We evaluate the effectiveness of the methods and others in the\nliterature on a set of Portable Execution~(PE) files. Comparison prompts our\nintroduction of an online measure computed during training to assess general\nexpectation of robustness.","url_abs":"http://arxiv.org/abs/1801.02950v3","url_pdf":"http://arxiv.org/pdf/1801.02950v3.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":"adversarial-deep-learning-for-robust","repo_url":"https://github.com/ALFA-group/robust-adv-malware-detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"adversarial-deep-learning-for-robust","repo_url":"https://github.com/ALFA-group/malware_challenge","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[{"method_slug":"svm","method_name":"SVM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1801.02950","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1801.02950"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ALFA-group/robust-adv-malware-detection","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ALFA-group/malware_challenge","reach":{"status":"unanswered"}}],"summary":{"ran_honours":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"31155c4739d2ae39","entry":"file_rank","repo":"ALFA-group/robust-adv-malware-detection","repo_kind":"official","path":"utils/script_functions.py","file_url":"https://github.com/ALFA-group/robust-adv-malware-detection/blob/HEAD/utils/script_functions.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"31155c4739d2ae39"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}