{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T21:03:17Z","timestamp":1780606997533,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819790524","type":"print"},{"value":"9789819790531","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-97-9053-1_16","type":"book-chapter","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T19:02:33Z","timestamp":1729796553000},"page":"273-291","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Unveiling the\u00a0Efficacy of\u00a0BERT\u2019s Attention in\u00a0Memory Obfuscated Malware Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9000-2598","authenticated-orcid":false,"given":"Md","family":"Mashrur Arifin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7839-5133","authenticated-orcid":false,"given":"Troy","family":"Suyehara Tolman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1069-9655","authenticated-orcid":false,"given":"Jyh-haw","family":"Yeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"issue":"17","key":"16_CR1","doi-asserted-by":"publisher","first-page":"8482","DOI":"10.3390\/app12178482","volume":"12","author":"FA Aboaoja","year":"2022","unstructured":"Aboaoja, F.A., Zainal, A., Ghaleb, F.A., Al-Rimy, B.A.S., Eisa, T.A.E., Elnour, A.A.H.: Malware detection issues, challenges, and future directions: a survey. Appl. Sci. 12(17), 8482 (2022). https:\/\/doi.org\/10.3390\/app12178482","journal-title":"Appl. Sci."},{"key":"16_CR2","doi-asserted-by":"publisher","unstructured":"Aghaeikheirabady, M., Farshchi, S.M.R., Shirazi, H.: A new approach to malware detection by comparative analysis of data structures in a memory image. In: 2014 International Congress on Technology, Communication and Knowledge (ICTCK), pp.\u00a01\u20134. IEEE (2014). https:\/\/doi.org\/10.1109\/ICTCK.2014.7033519","DOI":"10.1109\/ICTCK.2014.7033519"},{"key":"16_CR3","doi-asserted-by":"publisher","unstructured":"Alvares, J., Troia, F.D.: Bert for malware classification. In: Artificial Intelligence for Cybersecurity, pp. 161\u2013181. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-030-97087-1_7","DOI":"10.1007\/978-3-030-97087-1_7"},{"key":"16_CR4","doi-asserted-by":"publisher","unstructured":"Banescu, S., Wuchner, T., Salem, A., Guggenmos, M., Ochoa, M., Pretschner, A.: A framework for empirical evaluation of malware detection resilience against behavior obfuscation. In: 2015 10th International Conference on Malicious and Unwanted Software (MALWARE), pp. 40\u201347. IEEE (2015). https:\/\/doi.org\/10.1109\/MALWARE.2015.7413683","DOI":"10.1109\/MALWARE.2015.7413683"},{"key":"16_CR5","doi-asserted-by":"publisher","unstructured":"Carrier, T., Victor, P., Tekeoglu, A., Lashkari, A.H.: Detecting obfuscated malware using memory feature engineering. In: ICISSP, pp. 177\u2013188 (2022). https:\/\/doi.org\/10.5220\/0010908200003120","DOI":"10.5220\/0010908200003120"},{"key":"16_CR6","doi-asserted-by":"publisher","first-page":"S3","DOI":"10.1016\/j.diin.2016.04.017","volume":"18","author":"A Case","year":"2016","unstructured":"Case, A., Richard, G.G., III.: Detecting objective-c malware through memory forensics. Digit. Investig. 18, S3\u2013S10 (2016). https:\/\/doi.org\/10.1016\/j.diin.2016.04.017","journal-title":"Digit. Investig."},{"key":"16_CR7","unstructured":"Clark, K., Luong, M.T., Le, Q.V., Manning, C.D.: Electra: pre-training text encoders as discriminators rather than generators (2020). arXiv:2003.10555"},{"key":"16_CR8","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.diin.2018.09.006","volume":"27","author":"Y Dai","year":"2018","unstructured":"Dai, Y., Li, H., Qian, Y., Lu, X.: A malware classification method based on memory dump grayscale image. Digit. Investig. 27, 30\u201337 (2018). https:\/\/doi.org\/10.1016\/j.diin.2018.09.006","journal-title":"Digit. Investig."},{"key":"16_CR9","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding (2018). arXiv:1810.04805"},{"key":"16_CR10","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.907","volume":"8","author":"WF Elsersy","year":"2022","unstructured":"Elsersy, W.F., Feizollah, A., Anuar, N.B.: The rise of obfuscated android malware and impacts on detection methods. PeerJ Comput. Sci. 8, e907 (2022). https:\/\/doi.org\/10.7717\/peerj-cs.907","journal-title":"PeerJ Comput. Sci."},{"key":"16_CR11","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3363469","author":"MA Ferrag","year":"2024","unstructured":"Ferrag, M.A., Ndhlovu, M., Tihanyi, N., Cordeiro, L.C., Debbah, M., Lestable, T., Thandi, N.S.: Revolutionizing cyber threat detection with large language models: a privacy-preserving Bert-based lightweight model for IoT\/IIoT devices. IEEE Access (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3363469","journal-title":"IEEE Access"},{"key":"16_CR12","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/s11277-017-4859-y","volume":"98","author":"D Javaheri","year":"2018","unstructured":"Javaheri, D., Hosseinzadeh, M.: A framework for recognition and confronting of obfuscated malwares based on memory dumping and filter drivers. Wireless Pers. Commun. 98, 119\u2013137 (2018). https:\/\/doi.org\/10.1007\/s11277-017-4859-y","journal-title":"Wireless Pers. Commun."},{"key":"16_CR13","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1016\/j.compeleceng.2019.06.014","volume":"77","author":"J Kang","year":"2019","unstructured":"Kang, J., Jang, S., Li, S., Jeong, Y.S., Sung, Y.: Long short-term memory-based malware classification method for information security. Comput. Electr. Eng. 77, 366\u2013375 (2019). https:\/\/doi.org\/10.1016\/j.compeleceng.2019.06.014","journal-title":"Comput. Electr. Eng."},{"key":"16_CR14","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). arXiv:1412.6980"},{"key":"16_CR15","doi-asserted-by":"publisher","unstructured":"Lashkari, A.H., Li, B., Carrier, T.L., Kaur, G.: Volmemlyzer: volatile memory analyzer for malware classification using feature engineering. In: 2021 Reconciling Data Analytics, Automation, Privacy, and Security: A Big Data Challenge (RDAAPS), pp.\u00a01\u20138. IEEE (2021). https:\/\/doi.org\/10.1109\/RDAAPS48126.2021.9452028","DOI":"10.1109\/RDAAPS48126.2021.9452028"},{"key":"16_CR16","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized Bert pretraining approach (2019). arXiv:1907.11692"},{"key":"16_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2024.103864","volume":"142","author":"P Maniriho","year":"2024","unstructured":"Maniriho, P., Mahmood, A.N., Chowdhury, M.J.M.: Memaldet: a memory analysis-based malware detection framework using deep autoencoders and stacked ensemble under temporal evaluations. Comput. Secur. 142, 103864 (2024). https:\/\/doi.org\/10.1016\/j.cose.2024.103864","journal-title":"Comput. Secur."},{"issue":"5","key":"16_CR18","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/MSP.2011.98","volume":"9","author":"P O\u2019Kane","year":"2011","unstructured":"O\u2019Kane, P., Sezer, S., McLaughlin, K.: Obfuscation: the hidden malware. IEEE Secur. Privacy 9(5), 41\u201347 (2011). https:\/\/doi.org\/10.1109\/MSP.2011.98","journal-title":"IEEE Secur. Privacy"},{"key":"16_CR19","doi-asserted-by":"publisher","unstructured":"Rahali, A., Akhloufi, M.A.: Malbert: malware detection using bidirectional encoder representations from transformers. In: 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3226\u20133231. IEEE (2021). https:\/\/doi.org\/10.1109\/SMC52423.2021.9659287","DOI":"10.1109\/SMC52423.2021.9659287"},{"issue":"2","key":"16_CR20","doi-asserted-by":"publisher","first-page":"60","DOI":"10.3390\/bdcc7020060","volume":"7","author":"A Rahali","year":"2023","unstructured":"Rahali, A., Akhloufi, M.A.: Malbertv2: code aware Bert-based model for malware identification. Big Data Cogn. Comput. 7(2), 60 (2023). https:\/\/doi.org\/10.3390\/bdcc7020060","journal-title":"Big Data Cogn. Comput."},{"key":"16_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.iswa.2023.200283","volume":"20","author":"KS Roy","year":"2023","unstructured":"Roy, K.S., Ahmed, T., Udas, P.B., Karim, M.E., Majumdar, S.: Malhystack: a hybrid stacked ensemble learning framework with feature engineering schemes for obfuscated malware analysis. Intell. Syst. Appl. 20, 200283 (2023). https:\/\/doi.org\/10.1016\/j.iswa.2023.200283","journal-title":"Intell. Syst. Appl."},{"key":"16_CR22","unstructured":"Sanh, V., Debut, L., Chaumond, J., Wolf, T.: Distilbert, a distilled version of Bert: smaller, faster, cheaper and lighter (2019). arXiv:1910.01108"},{"issue":"11","key":"16_CR23","doi-asserted-by":"publisher","first-page":"5348","DOI":"10.3390\/s23115348","volume":"23","author":"SS Shafin","year":"2023","unstructured":"Shafin, S.S., Karmakar, G., Mareels, I.: Obfuscated memory malware detection in resource-constrained IoT devices for smart city applications. Sensors 23(11), 5348 (2023). https:\/\/doi.org\/10.3390\/s23115348","journal-title":"Sensors"},{"issue":"18","key":"16_CR24","doi-asserted-by":"publisher","first-page":"3680","DOI":"10.3390\/app9183680","volume":"9","author":"R Sihwail","year":"2019","unstructured":"Sihwail, R., Omar, K., Zainol Ariffin, K.A., Al Afghani, S.: Malware detection approach based on artifacts in memory image and dynamic analysis. Appl. Sci. 9(18), 3680 (2019). https:\/\/doi.org\/10.3390\/app9183680","journal-title":"Appl. Sci."},{"key":"16_CR25","doi-asserted-by":"publisher","unstructured":"Souani, B., Khanfir, A., Bartel, A., Allix, K., Le\u00a0Traon, Y.: Android malware detection using Bert. In: International Conference on Applied Cryptography and Network Security, pp. 575\u2013591. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-16815-4_31","DOI":"10.1007\/978-3-031-16815-4_31"},{"key":"16_CR26","doi-asserted-by":"crossref","unstructured":"Tallarida, R.J., Murray, R.B., Tallarida, R.J., Murray, R.B.: Chi-square test. Manual of pharmacologic calculations: with computer programs, pp. 140\u2013142 (1987)","DOI":"10.1007\/978-1-4612-4974-0_43"},{"key":"16_CR27","doi-asserted-by":"publisher","unstructured":"Treadwell, S., Zhou, M.: A heuristic approach for detection of obfuscated malware. In: 2009 IEEE International Conference on Intelligence and Security Informatics, pp. 291\u2013299. IEEE (2009). https:\/\/doi.org\/10.1109\/ISI.2009.5137328","DOI":"10.1109\/ISI.2009.5137328"},{"key":"16_CR28","doi-asserted-by":"publisher","unstructured":"Wang, S., Xu, B.: A novel approach of evasive malware analysis through binary opcode and Bert. Research Square (2024). https:\/\/doi.org\/10.21203\/rs.3.rs-3840848\/v1","DOI":"10.21203\/rs.3.rs-3840848\/v1"},{"key":"16_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102458","volume":"111","author":"Z Xu","year":"2021","unstructured":"Xu, Z., Fang, X., Yang, G.: Malbert: a novel pre-training method for malware detection. Comput. Secur. 111, 102458 (2021). https:\/\/doi.org\/10.1016\/j.cose.2021.102458","journal-title":"Comput. Secur."},{"key":"16_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.fsidi.2019.200903","volume":"32","author":"\u00c7 Y\u00fccel","year":"2020","unstructured":"Y\u00fccel, \u00c7., Koltuksuz, A.: Imaging and evaluating the memory access for malware. For. Sci. Int. Dig. Investigation 32, 200903 (2020). https:\/\/doi.org\/10.1016\/j.fsidi.2019.200903","journal-title":"For. Sci. Int. Dig. Investigation"}],"container-title":["Lecture Notes in Computer Science","Information Security Practice and Experience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-9053-1_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T19:07:34Z","timestamp":1729796854000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-9053-1_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,25]]},"ISBN":["9789819790524","9789819790531"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-9053-1_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,25]]},"assertion":[{"value":"25 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISPEC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Security Practice and Experience","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wuhan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ispec2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ispec2024.github.io\/ISPEC2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}