{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T16:35:52Z","timestamp":1743093352536,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811980688"},{"type":"electronic","value":"9789811980695"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-19-8069-5_24","type":"book-chapter","created":{"date-parts":[[2022,11,19]],"date-time":"2022-11-19T10:07:42Z","timestamp":1668852462000},"page":"362-374","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of Machine Learning in Malware Detection"],"prefix":"10.1007","author":[{"given":"Trinh","family":"Van Quynh","sequence":"first","affiliation":[]},{"given":"Vu Thanh","family":"Hien","sequence":"additional","affiliation":[]},{"given":"Vu Thanh","family":"Nguyen","sequence":"additional","affiliation":[]},{"given":"Huynh Quoc","family":"Bao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,20]]},"reference":[{"key":"24_CR1","unstructured":"AVTest: AV-ATLAS analyzes for you. AV-TEST. Available: https:\/\/portal.av-atlas.org. Accessed 12 May 2021"},{"key":"24_CR2","unstructured":"The (ISC): Cybersecurity workforce study. The (ISC), 2020. Available: https:\/\/www.isc2.org\/Research\/Workforce-Study. Accessed 12 May 2021"},{"key":"24_CR3","unstructured":"Hyrum, S.: Anderson and Phil Roth. EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models (2018)"},{"key":"24_CR4","unstructured":"Roth Phil: EMBER Improvements. The Conference on Applied Machine Learning in Information Security, 2019. Available: https:\/\/www.camlis.org\/2019\/talks\/roth. Accessed 10 Nov 2020"},{"key":"24_CR5","unstructured":"Phil, R.: Elastic malware benchmark for empowering researchers. The Conference on Applied Machine Learning in Information Security, Available: https:\/\/github.com\/elastic\/ember. Accessed 10 Sep 2020"},{"key":"24_CR6","unstructured":"Harang, R., Rudd, E.M.: Sorel-20\u00a0m: a large scale benchmark dataset for malicious PE detection. Sophos-ReversingLabs, 2020. Available: https:\/\/ai.sophos.com\/2020\/12\/14\/sophos-reversinglabs-sorel-20-million-sample-malware-dataset\/. Accessed 10 Apr 2021"},{"key":"24_CR7","unstructured":"Harang, R., Rudd, E.M.: Sorel-20\u00a0m: a large scale benchmark dataset for malicious PE detection. Sophos-ReversingLabs, 2020. Available: https:\/\/github.com\/sophos-ai\/SOREL-20M. Accessed Mar 2021"},{"key":"24_CR8","unstructured":"Kolter, J.Z., Maloof, M.A.\u201d Learning to detect malicious executables in the wild. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2006)"},{"key":"24_CR9","unstructured":"Raman, K., et al.: Selecting features to classify malware. InfoSec Southwest (2012)"},{"key":"24_CR10","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1007\/978-3-319-40667-1_20","volume-title":"Detection of Intrusions and Malware, and Vulnerability Assessment","author":"W Huang","year":"2016","unstructured":"Huang, W., Stokes, J.W.: MtNet: a multi-task neural network for dynamic malware classification. In: Caballero, J., Zurutuza, U., Rodr\u00edguez, R.J. (eds.) Detection of Intrusions and Malware, and Vulnerability Assessment, pp. 399\u2013418. Springer International Publishing, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-40667-1_20"},{"key":"24_CR11","doi-asserted-by":"crossref","unstructured":"Pham, H.D., Le, T.D., Vu, T.N.: Static PE malware detection using gradient boosting decision trees algorithm. In: Dang, T., K\u00fcng, J., Wagner, R., Thoai, N., Takizawa, M. (eds) Future Data and Security Engineering. FDSE 2018. Lecture Notes in Computer Science","DOI":"10.1007\/978-3-030-03192-3_17"},{"key":"24_CR12","doi-asserted-by":"crossref","unstructured":"Oyama, Y., Miyashita, T., Kokubo, H.: Identifying useful features for malware detection in the ember dataset. In: Seventh International Symposium on Computing and Networking Workshops (CANDARW) (2019)","DOI":"10.1109\/CANDARW.2019.00069"},{"key":"24_CR13","doi-asserted-by":"crossref","unstructured":"Galen, C., Steele, R.: Evaluating performance maintenance and deterioration over time of machine learning-based malware detection models on the EMBER PE dataset. In: Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS) (2020)","DOI":"10.1109\/SNAMS52053.2020.9336538"},{"key":"24_CR14","unstructured":"El Merabet, H.: A first approach to malware detection using residual networks. In: International Journal of Computer Science, Communication & Information Technology (CSCIT) (2019)"},{"issue":"6","key":"24_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5815\/ijcnis.2019.06.01","volume":"11","author":"I Abdessadki","year":"2019","unstructured":"Abdessadki, I., Lazaar, S.: A new classification based model for malicious PE files detection. Int. J. Comput. Netw. Inf. Secur. 11(6), 1\u20139 (2019). https:\/\/doi.org\/10.5815\/ijcnis.2019.06.01","journal-title":"Int. J. Comput. Netw. Inf. Secur."},{"key":"24_CR16","unstructured":"Kolosnjaji, B., Zarras, A., Webster, G., Eckert, C.: Deep learning for classication of malware system call sequences. In: Australasian Joint Conference on Articial Intelligence (2019)"},{"key":"24_CR17","unstructured":"Heller, K., Svore, K., Keromytis, A.D., Stolfo, S.: Oneclass support vector machines for detecting anomalous windows registry accesses. In: ICDM Workshop on Data Mining for Computer Security (2003)"},{"issue":"2","key":"24_CR18","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/s11416-008-0105-1","volume":"5","author":"S Attaluri","year":"2009","unstructured":"Attaluri, S., McGhee, S., Stamp, M.: Profile hidden markov models and metamorphic virus detection. J. Comput. Virol. 5(2), 151\u2013169 (2009). https:\/\/doi.org\/10.1007\/s11416-008-0105-1","journal-title":"J. Comput. Virol."},{"key":"24_CR19","unstructured":"Ronen, R., Radu, M., Feuerstein, C., Yom-Tov, E., Ahmadi, M.: Microsoft malware classification challenge (2018)"},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Norouzi, M., Souri, A., Zamini, M.S.: A data mining classification approach for behavioral malware detection. J. Comput. Netw. Commun. (2016)","DOI":"10.1155\/2016\/8069672"},{"key":"24_CR21","doi-asserted-by":"crossref","unstructured":"Souri, A., Hosseini, R.: A state-of-the-art survey of malware detection approaches using data mining techniques. Hum. Cent. Comput. Inf. Sci. 8 (2018)","DOI":"10.1186\/s13673-018-0125-x"},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Bagga, N.: Measuring the Effectiveness of Generic Malware Models. San Jose State University (2017)","DOI":"10.5220\/0006921506080616"},{"key":"24_CR23","unstructured":"Roth, P.: Introducing ember: an open source classifier and dataset. Elastic . Available: https:\/\/www.elastic.co\/blog\/introducing-ember-open-source-classifier-and-dataset. Accessed 20 Sep 2020"},{"key":"24_CR24","unstructured":"Sophos, A.I.: Sophos-ReversingLabs (SOREL) 20 Million sample malware dataset. Sophos. Available: https:\/\/ai.sophos.com\/2020\/12\/14\/sophos-reversinglabs-sorel-20-million-sample-malware-dataset\/. Accessed 12 May 2021"}],"container-title":["Communications in Computer and Information Science","Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-8069-5_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,19]],"date-time":"2022-11-19T10:11:30Z","timestamp":1668852690000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-8069-5_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811980688","9789811980695"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-8069-5_24","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"20 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FDSE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Future Data and Security Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ho Chi Minh City","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"fdse2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/thefdse.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"170","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"12","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4 full papers from invited keynote speakers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}