{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T17:23:21Z","timestamp":1763573001792,"version":"3.45.0"},"reference-count":71,"publisher":"Informa UK Limited","issue":"4","funder":[{"name":"National Science Foundation","award":["1921485"],"award-info":[{"award-number":["1921485"]}]},{"name":"National Science Foundation","award":["1936370"],"award-info":[{"award-number":["1936370"]}]}],"content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["Journal of Management Information Systems"],"published-print":{"date-parts":[[2025,10,2]]},"DOI":"10.1080\/07421222.2025.2561384","type":"journal-article","created":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T17:12:57Z","timestamp":1763572377000},"page":"1118-1148","update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":0,"title":["Defending Deep Learning-Based Raw Malware Detectors Against Adversarial Attacks: A Sequence Modeling Approach"],"prefix":"10.1080","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1367-3338","authenticated-orcid":false,"given":"Reza","family":"Ebrahimi","sequence":"first","affiliation":[{"name":"School of Information Systems, University of South Florida","place":["Tampa, USA"]}]},{"given":"James Lee","family":"Hu","sequence":"additional","affiliation":[{"name":"Eller College of Management, University of Arizona","place":["Tucson, USA"]}]},{"given":"Ning","family":"Zhang","sequence":"additional","affiliation":[{"name":"Acium Inc","place":["USA"]}]},{"suffix":"Jr.","given":"Jay F.","family":"Nunamaker","sequence":"additional","affiliation":[{"name":"Eller College of Management, University of Arizona","place":["Tucson, USA"]}]},{"given":"Hsinchun","family":"Chen","sequence":"additional","affiliation":[{"name":"Eller College of Management, University of Arizona","place":["Tucson, USA"]}]}],"member":"301","published-online":{"date-parts":[[2025,11,19]]},"reference":[{"key":"e_1_3_4_2_1","doi-asserted-by":"publisher","DOI":"10.2307\/25750686"},{"key":"e_1_3_4_3_1","article-title":"Machine Learning Static Evasion Competition","author":"Anderson H.S.","year":"2019","unstructured":"Anderson, H.S. Machine Learning Static Evasion Competition. GitHub, 2019. https:\/\/github.com\/endgameinc\/malware_evasion_competition (accessed on September 30, 2025).","journal-title":"GitHub"},{"key":"e_1_3_4_4_1","article-title":"Learning to evade static PE machine learning malware models via reinforcement learning","author":"Anderson H.S.","year":"2018","unstructured":"Anderson, H.S.; Kharkar, A.; Filar, B.; Evans, D.; and Roth, P. Learning to evade static PE machine learning malware models via reinforcement learning. arXiv preprint arXiv:1801.08917, (2018).","journal-title":"arXiv preprint arXiv:1801.08917"},{"key":"e_1_3_4_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330944"},{"key":"e_1_3_4_6_1","doi-asserted-by":"publisher","DOI":"10.25300\/MISQ\/2019\/13808"},{"key":"e_1_3_4_7_1","doi-asserted-by":"publisher","DOI":"10.1080\/07421222.2016.1205918"},{"key":"e_1_3_4_8_1","unstructured":"Bissell K.; LaSalle R.M. and Cin P.D. Ninth Annual Cost of Cybercrime Study: Unlocking the Value of Improved Cybersecurity Protection. Accenture and Ponemon 2019. https:\/\/www.accenture.com\/us-en\/insights\/security\/cost-cybercrime-study accessed on September 30 2025)."},{"key":"e_1_3_4_9_1","article-title":"Symantec Unveils Industry\u2019s First Neural Network to Protect Critical Infrastructure from Cyber Warfare","author":"Bloomberg","year":"2018","unstructured":"Bloomberg. Symantec Unveils Industry\u2019s First Neural Network to Protect Critical Infrastructure from Cyber Warfare. Bloomberg, 2018. https:\/\/www.bloomberg.com\/press-releases\/2018-12-05\/symantec-unveils-industry-s-first-neural-network-to-protect-critical-infrastructure-from-cyber-warfare (accessed on January 30, 2020).","journal-title":"Bloomberg"},{"key":"e_1_3_4_10_1","unstructured":"Braue D. Cybercrime to cost the world $12.2 trillion annually by 2031. Cybercrime Magazine 2025."},{"key":"e_1_3_4_11_1","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown T.","year":"2020","unstructured":"Brown, T.; Mann, B.; Ryder, N., Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; and Shyam, P. Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, (2020), 1877\u20131901.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_4_12_1","doi-asserted-by":"publisher","DOI":"10.1287\/isre.2019.0860"},{"key":"e_1_3_4_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOMAN.2019.8714698"},{"key":"e_1_3_4_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2913439"},{"key":"e_1_3_4_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jisa.2023.103508"},{"key":"e_1_3_4_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3082330"},{"key":"e_1_3_4_17_1","unstructured":"Devlin J.; Chang M.-W.; Lee K.; and Toutanova K. Bert: Pre-training of deep bidirectional transformers for language understanding. CoRR abs\/1810.04805 (2018)."},{"key":"e_1_3_4_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-7561-3_11"},{"key":"e_1_3_4_19_1","doi-asserted-by":"publisher","DOI":"10.25300\/MISQ\/2022\/16618"},{"key":"e_1_3_4_20_1","article-title":"\u2018RADAR: A framework for developing adversarially robust cyber defense AI agents with deep reinforcement learning","author":"Ebrahimi R.","year":"2025","unstructured":"Ebrahimi, R.; Chai, Y.; Li, W.; Pacheco, J., and Chen, H. \u2018RADAR: A framework for developing adversarially robust cyber defense AI agents with deep reinforcement learning. MIS Quarterly, 2025.","journal-title":"MIS Quarterly"},{"key":"e_1_3_4_21_1","doi-asserted-by":"publisher","DOI":"10.1080\/07421222.2020.1790186"},{"key":"e_1_3_4_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2024.3477272"},{"key":"e_1_3_4_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2908033"},{"key":"e_1_3_4_24_1","article-title":"Non-negative networks against adversarial attacks","author":"Fleshman W.","year":"2019","unstructured":"Fleshman, W.; Raff, E.; Sylvester, J.; Forsyth, S.; and McLean, M. Non-negative networks against adversarial attacks. arXiv preprint arXiv:1806.06108, (2019).","journal-title":"arXiv preprint arXiv:1806.06108"},{"key":"e_1_3_4_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3134599"},{"key":"e_1_3_4_26_1","doi-asserted-by":"publisher","DOI":"10.25300\/MISQ\/2013\/37.2.01"},{"key":"e_1_3_4_27_1","article-title":"Practical traffic-space adversarial attacks on learning-based Nidss","author":"Han D.","year":"2020","unstructured":"Han, D.; Wang, Z.; Zhong, Y., Chen, W.; Yang, J.; Lu, S.; Shi, X.; and Yin, X. Practical traffic-space adversarial attacks on learning-based Nidss. arXiv preprint arXiv:2005.07519, (2020).","journal-title":"arXiv preprint arXiv:2005.07519"},{"key":"e_1_3_4_28_1","doi-asserted-by":"publisher","DOI":"10.2307\/25148625"},{"key":"e_1_3_4_29_1","first-page":"245","volume-title":"AAAI Conference on Artificial Intelligence","author":"Hu W.","year":"2018","unstructured":"Hu, W.; and Tan, Y. Black-box attacks against RNN based malware detection algorithms. In AAAI Conference on Artificial Intelligence. The AAAI Press, Palo Alto, California, 2018, pp. 245\u2013255."},{"key":"e_1_3_4_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-19-8991-9_29"},{"key":"e_1_3_4_31_1","unstructured":"Hui K.-L.; Vance A. and Zhdanov D. Securing digital assets. 2018. https:\/\/static1.squarespace.com\/static\/5887a660b3db2b05bd09cf36\/t\/5deff7043d673974a391656f\/1576007428557\/MISQ+Curation+Securing+Digital+Assets+July+2018.pdf (accessed on September 30 2025)."},{"key":"e_1_3_4_32_1","first-page":"3800","article-title":"On the choice of modeling unit for sequence-to-sequence speech recognition","volume":"2019","author":"Irie K.","year":"2019","unstructured":"Irie, K.; Prabhavalkar, R.; Kannan, A.; Bruguier, A.; Rybach, D.; and Nguyen, P. On the choice of modeling unit for sequence-to-sequence speech recognition. Interspeech 2019, (2019), 3800\u20133804.","journal-title":"Interspeech"},{"key":"e_1_3_4_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11416-023-00498-7"},{"key":"e_1_3_4_34_1","doi-asserted-by":"publisher","DOI":"10.1287\/isre.2018.0786"},{"key":"e_1_3_4_35_1","first-page":"5156","volume-title":"International Conference on Machine Learning","author":"Katharopoulos A.","year":"2020","unstructured":"Katharopoulos, A.; Vyas, A.; Pappas, N.; and Fleuret, F. Transformers are RNNs: Fast autoregressive transformers with linear attention. In International Conference on Machine Learning. PMLR, 2020, pp. 5156\u20135165."},{"key":"e_1_3_4_36_1","first-page":"597","volume-title":"International Conference on Algorithmic Learning Theory","author":"Keles F.D.","year":"2023","unstructured":"Keles, F.D.; Wijewardena, P.M., and Hegde, C. On the computational complexity of self-attention. In International Conference on Algorithmic Learning Theory. Singapore: PMLR, 2023, pp. 597\u2013619."},{"key":"e_1_3_4_37_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10362"},{"key":"e_1_3_4_38_1","doi-asserted-by":"publisher","DOI":"10.23919\/EUSIPCO.2018.8553214"},{"key":"e_1_3_4_39_1","volume-title":"International Conference on Learning Representations (ICLR)","author":"Kr\u010d\u00e1l M.","year":"2018","unstructured":"Kr\u010d\u00e1l, M.; \u0160vec, O.; B\u00e1lek, M.; and Ja\u0161ek, O. Deep convolutional malware classifiers can learn from raw executables and labels only. In International Conference on Learning Representations (ICLR). British Columbia, Canada: ICLR, 2018."},{"key":"e_1_3_4_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/SPW50608.2020.00018"},{"key":"e_1_3_4_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103279"},{"issue":"06251","key":"e_1_3_4_42_1","article-title":"Deep independently recurrent neural network (IndRNN)","volume":"1910","author":"Li S.","year":"2020","unstructured":"Li, S.; Li, W.; Cook, C.; Gao, Y.; and Zhu, C. Deep independently recurrent neural network (IndRNN). CoRR, abs\/1910.06251, (2020).","journal-title":"CoRR, abs\/"},{"key":"e_1_3_4_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00572"},{"key":"e_1_3_4_44_1","doi-asserted-by":"publisher","DOI":"10.1080\/07421222.2022.2063549"},{"key":"e_1_3_4_45_1","doi-asserted-by":"publisher","DOI":"10.1080\/07421222.2016.1267528"},{"key":"e_1_3_4_46_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.02.075"},{"key":"e_1_3_4_47_1","volume-title":"International Conference on Learning Representations (ICLR)","author":"Madry A.","year":"2018","unstructured":"Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; and Vladu, A. Towards deep learning models resistant to adversarial attacks. In International Conference on Learning Representations (ICLR). British Colombia, Canada: ICLR, 2018."},{"key":"e_1_3_4_48_1","unstructured":"Morgan S. Global Ransomware Damage Costs. Cybercrime Magazine 2019. https:\/\/cybersecurityventures.com\/global-ransomware-damage-costs-predicted-to-reach-20-billion-usd-by-2021\/ (accessed on September 30 2025)."},{"key":"e_1_3_4_49_1","article-title":"Scripps Health CEO Confirms to Staff That Information Systems Damaged by Malware","author":"National Broadcasting Company (NBC)","year":"2021","unstructured":"National Broadcasting Company (NBC). Scripps Health CEO Confirms to Staff That Information Systems Damaged by Malware. NBC San Diego, 2021. https:\/\/www.nbcsandiego.com\/news\/local\/what-we-know-about-scripps-health-cyberattack\/2598969\/ (accessed on September 30, 2025).","journal-title":"NBC San Diego"},{"key":"e_1_3_4_50_1","doi-asserted-by":"publisher","DOI":"10.1080\/07421222.2015.1094961"},{"key":"e_1_3_4_51_1","first-page":"4","volume-title":"Proceedings of The 8th Workshop on Patent and Scientific Literature Translation","author":"Ono J.","year":"2019","unstructured":"Ono, J.; Utiyama, M.; and Sumita, E. Hybrid data-model parallel training for sequence-to-sequence recurrent neural network machine translation. In Proceedings of The 8th Workshop on Patent and Scientific Literature Translation. European Association for Machine Translation, Dublin, Ireland, 2019, pp. 4\u201312."},{"key":"e_1_3_4_52_1","article-title":"Extending defensive distillation","author":"Papernot N.","year":"2017","unstructured":"Papernot, N.; and McDaniel, P.D. Extending defensive distillation. arXiv preprint arXiv:1705.05264, (2017).","journal-title":"arXiv preprint arXiv:1705.05264"},{"issue":"04802","key":"e_1_3_4_53_1","article-title":"Creating adversarial malware examples using code insertion","volume":"1904","author":"Park D.","year":"2019","unstructured":"Park, D.; Khan, H.; and Yener, B. Creating adversarial malware examples using code insertion. CoRR, abs\/1904.04802, (2019).","journal-title":"CoRR"},{"key":"e_1_3_4_54_1","doi-asserted-by":"publisher","DOI":"10.3390\/app9050909"},{"issue":"8","key":"e_1_3_4_55_1","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford A.","year":"2019","unstructured":"Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; and Sutskever, I. Language models are unsupervised multitask learners. OpenAI Blog, 1, 8 (2019), 9.","journal-title":"OpenAI Blog"},{"key":"e_1_3_4_56_1","article-title":"Malware detection by eating a whole exe","author":"Raff E.","year":"2018","unstructured":"Raff, E.; Barker, J.; Sylvester, J.; Brandon, R.; Catanzaro, B.; and Nicholas, C. Malware detection by eating a whole exe. arXiv preprint arXiv:1710.09435, (2018).","journal-title":"arXiv preprint arXiv:1710.09435"},{"key":"e_1_3_4_57_1","doi-asserted-by":"publisher","DOI":"10.25300\/MISQ\/2017\/41.1.E0"},{"key":"e_1_3_4_58_1","first-page":"490","volume-title":"International Symposium on Research in Attacks, Intrusions, and Defenses","author":"Rosenberg I.","year":"2019","unstructured":"Rosenberg, I.; Shabtai, A.; Rokach, L.; and Elovici, Y. Generic black-box end-to-end attack against state of the art api call-based malware classifiers. In International Symposium on Research in Attacks, Intrusions, and Defenses. Beijing, China: Springer, 2019, pp. 490\u2013510."},{"key":"e_1_3_4_59_1","doi-asserted-by":"publisher","DOI":"10.25300\/MISQ\/2022\/15392"},{"key":"e_1_3_4_60_1","doi-asserted-by":"publisher","DOI":"10.1080\/07421222.2017.1394049"},{"key":"e_1_3_4_61_1","article-title":"Optimization-guided binary diversification to mislead neural networks for malware detection","author":"Sharif M.","year":"2019","unstructured":"Sharif, M.; Lucas, K.; Bauer, L.; Reiter, M.K.; and Shintre, S. Optimization-guided binary diversification to mislead neural networks for malware detection. arXiv preprint arXiv:1912.09064, (2019).","journal-title":"arXiv preprint arXiv:1912.09064"},{"key":"e_1_3_4_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3488932.3497768"},{"key":"e_1_3_4_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/SPW.2019.00015"},{"key":"e_1_3_4_64_1","article-title":"Colonial Pipeline Confirms It Paid $4.4m Ransom to Hacker Gang After Attack","author":"The Guardian","year":"2021","unstructured":"The Guardian. Colonial Pipeline Confirms It Paid $4.4m Ransom to Hacker Gang After Attack. The Guardian, 2021. https:\/\/www.theguardian.com\/technology\/2021\/may\/19\/colonial-pipeline-cyber-attack-ransom (accessed on September 30 2025).","journal-title":"The Guardian"},{"key":"e_1_3_4_65_1","volume-title":"Reinventing Cybersecurity with Artificial Intelligence: The new frontier in digital security","author":"Tolido R.","year":"2019","unstructured":"Tolido, R.; Linden, G. van der; Delabarre, L. Reinventing Cybersecurity with Artificial Intelligence: The new frontier in digital security. Capgemini Research Institute, 2019. https:\/\/www.capgemini.com\/us-en\/resource\/reinventing-cybersecurity-with-artificial-intelligence-the-new-frontier-in-digital-security\/ (accessed on September 30 2025)."},{"key":"e_1_3_4_66_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2017.09.001"},{"key":"e_1_3_4_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/IWCMC.2019.8766353"},{"key":"e_1_3_4_68_1","first-page":"5753","article-title":"XLNet: Generalized autoregressive pretraining for language understanding","volume":"32","author":"Yang Z.","year":"2019","unstructured":"Yang, Z.; Dai, Z.; Yang, Y.; Carbonell, J.; Salakhutdinov, R.R.; and Le, Q.V. XLNet: Generalized autoregressive pretraining for language understanding. Advances in Neural Information Processing Systems, 32, (2019), 5753\u20135763.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_4_69_1","doi-asserted-by":"publisher","DOI":"10.1080\/07421222.2018.1550550"},{"issue":"2","key":"e_1_3_4_70_1","first-page":"980","article-title":"Malfox: Camouflaged adversarial malware example generation based on conv-GANs against black-box detectors","volume":"73","author":"Zhong F.","year":"2023","unstructured":"Zhong, F.; Cheng, X.; Yu, D.; Gong, B.; Song, S.; and Yu, J. Malfox: Camouflaged adversarial malware example generation based on conv-GANs against black-box detectors. IEEE Transactions on Computers, 73, 2 (2023), 980\u2013993","journal-title":"IEEE Transactions on Computers"},{"key":"e_1_3_4_71_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dcan.2021.11.007"},{"key":"e_1_3_4_72_1","article-title":"Neural machine translation inspired binary code similarity comparison beyond function pairs","author":"Zuo F.","year":"2018","unstructured":"Zuo, F.; Li, X.; Young, P.; Luo, L.; Zeng, Q.; and Zhang, Z. Neural machine translation inspired binary code similarity comparison beyond function pairs. arXiv preprint arXiv:1808.04706, (2018).","journal-title":"arXiv preprint arXiv:1808.04706"}],"container-title":["Journal of Management Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/07421222.2025.2561384","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T17:18:34Z","timestamp":1763572714000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/07421222.2025.2561384"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,2]]},"references-count":71,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,10,2]]}},"alternative-id":["10.1080\/07421222.2025.2561384"],"URL":"https:\/\/doi.org\/10.1080\/07421222.2025.2561384","relation":{},"ISSN":["0742-1222","1557-928X"],"issn-type":[{"type":"print","value":"0742-1222"},{"type":"electronic","value":"1557-928X"}],"subject":[],"published":{"date-parts":[[2025,10,2]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=mmis20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=mmis20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2025-11-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}