{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T01:51:35Z","timestamp":1743040295252,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031337420"},{"type":"electronic","value":"9783031337437"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-33743-7_29","type":"book-chapter","created":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T13:03:02Z","timestamp":1685106182000},"page":"359-368","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Analysis of Malicious Intruder Threats to Data Integrity"],"prefix":"10.1007","author":[{"given":"Peter","family":"Padiet","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rafiqul","family":"Islam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. Arif","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,27]]},"reference":[{"issue":"1","key":"29_CR1","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/TNSM.2020.2967721","volume":"17","author":"DC Le","year":"2020","unstructured":"Le, D.C., Zincir-Heywood, N., Heywood, M.I.: Analyzing data granularity levels for insider threat detection using machine learning. IEEE Trans. Netw. Service Manag. 17(1), 30\u201344 (2020). https:\/\/doi.org\/10.1109\/TNSM.2020.2967721","journal-title":"IEEE Trans. Netw. Service Manag."},{"key":"29_CR2","unstructured":"The 2018 U.S. state of cybercrime survey. 2021 Insider Threat Report [Gurucul] (2018). https:\/\/www.cybersecurity-insiders.com\/portfolio\/2021-insider-threat-report-gurucul\/"},{"key":"29_CR3","doi-asserted-by":"publisher","first-page":"143266","DOI":"10.1109\/ACCESS.2021.3118297","volume":"9","author":"R Nasir","year":"2021","unstructured":"Nasir, R., Afzal, M., Latif, R., Iqbal, W.: Behavioral based insider threat detection using deep learning. IEEE Access 9, 143266\u2013143274 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3118297","journal-title":"IEEE Access"},{"issue":"2","key":"29_CR4","doi-asserted-by":"publisher","first-page":"1152","DOI":"10.1109\/TNSM.2021.3071928","volume":"18","author":"DC Le","year":"2021","unstructured":"Le, D.C., Zincir-Heywood, N.: Anomaly detection for insider threats using unsupervised ensembles. IEEE Trans. Netw. Service Manag. 18(2), 1152\u20131164 (2021). https:\/\/doi.org\/10.1109\/TNSM.2021.3071928","journal-title":"IEEE Trans. Netw. Service Manag."},{"key":"29_CR5","doi-asserted-by":"publisher","unstructured":"Igbe, O., Saadawi, T.: Insider threat detection using an artificial immune system algorithm. In: 2018 9th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), pp. 297\u2013302 (2018). https:\/\/doi.org\/10.1109\/UEMCON.2018.8796583","DOI":"10.1109\/UEMCON.2018.8796583"},{"key":"29_CR6","doi-asserted-by":"publisher","unstructured":"Padmavathi, G., Shanmugapriya, D., Asha, S.: A framework to detect the malicious insider threat in cloud environment using supervised learning methods. In: 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 354\u2013358 (2022). https:\/\/doi.org\/10.23919\/INDIACom54597.2022.9763205","DOI":"10.23919\/INDIACom54597.2022.9763205"},{"key":"29_CR7","doi-asserted-by":"publisher","unstructured":"Hall, A.J., Pitropakis, N., Buchanan, W.J., Moradpoor, N.: Predicting malicious insider threat scenarios using organizational data and a heterogeneous stack-classifier. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5034\u20135039 (2018). https:\/\/doi.org\/10.1109\/BigData.2018.8621922","DOI":"10.1109\/BigData.2018.8621922"},{"key":"29_CR8","doi-asserted-by":"publisher","unstructured":"Jiang, J., et al.: Prediction and detection of malicious insiders\u2019 motivation based on sentiment profile on webpages and emails. In: 2018 IEEE Military Communications Conference (MILCOM 2018), pp. 1\u20136 (2018). https:\/\/doi.org\/10.1109\/MILCOM.2018.8599790","DOI":"10.1109\/MILCOM.2018.8599790"},{"issue":"3","key":"29_CR9","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1109\/TCSS.2018.2857473","volume":"5","author":"P Chattopadhyay","year":"2018","unstructured":"Chattopadhyay, P., Wang, L., Tan, Y.P.: Scenario-based insider threat detection from cyber activities. IEEE Trans. Comput. Soc. Syst. 5(3), 660\u2013675 (2018). https:\/\/doi.org\/10.1109\/TCSS.2018.2857473","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"29_CR10","doi-asserted-by":"publisher","unstructured":"Le, D.C., Zincir-Heywood, N., Heywood, M.: Training regime influences to semi-supervised learning for insider threat detection. In: 2021 IEEE Security and Privacy Workshops (SPW), pp. 13\u201318 (2021). https:\/\/doi.org\/10.1109\/SPW53761.2021.00010","DOI":"10.1109\/SPW53761.2021.00010"},{"key":"29_CR11","doi-asserted-by":"publisher","unstructured":"Yang, G., Cai, L., Yu, A., Ma, J., Meng, D., Wu, Y.: Potential malicious insiders detection based on a comprehensive security psychological model. In: 2018 IEEE Fourth International Conference on Big Data Computing Service and Applications (BigDataService), pp. 9\u201316 (2018). https:\/\/doi.org\/10.1109\/BigDataService.2018.00011","DOI":"10.1109\/BigDataService.2018.00011"},{"key":"29_CR12","doi-asserted-by":"publisher","first-page":"11743","DOI":"10.1109\/ACCESS.2019.2959047","volume":"8","author":"AY Khan","year":"2020","unstructured":"Khan, A.Y., Latif, R., Latif, S., Tahir, S., Batool, G., Saba, T.: Malicious insider attack detection in IoTs using data analytics. IEEE Access 8, 11743\u201311753 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2019.2959047","journal-title":"IEEE Access"},{"key":"29_CR13","doi-asserted-by":"publisher","unstructured":"Meng, F., Lou, F., Fu, Y., Tian, Z.: Deep learning based attribute classification insider threat detection for data security. In: 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC), pp. 576\u2013581 (2018). https:\/\/doi.org\/10.1109\/DSC.2018.00092","DOI":"10.1109\/DSC.2018.00092"},{"key":"29_CR14","unstructured":"Le, D.C., Zincir-Heywood, A.N.: Machine learning based insider threat modelling and detection. In: 2019 IFIP\/IEEE Symposium on Integrated Network and Service Management (IM), pp. 1\u20136 (2019)"},{"key":"29_CR15","doi-asserted-by":"publisher","first-page":"183162","DOI":"10.1109\/ACCESS.2019.2957055","volume":"7","author":"L Liu","year":"2019","unstructured":"Liu, L., Chen, C., Zhang, J., De Vel, O., Xiang, Y.: Insider threat identification using the simultaneous neural learning of multi-source logs. IEEE Access 7, 183162\u2013183176 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2957055","journal-title":"IEEE Access"},{"key":"29_CR16","doi-asserted-by":"publisher","unstructured":"Greitzer, F.L.: Insider threats: it\u2019s the HUMAN, stupid! In: Proceedings of the Northwest Cybersecurity Symposium (NCS 2019), Article 4, pp. 1\u20138. Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1145\/3332448.3332458","DOI":"10.1145\/3332448.3332458"},{"issue":"2","key":"29_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3303771","volume":"52","author":"I Homoliak","year":"2019","unstructured":"Homoliak, I., Toffalini, F., Guarnizo, J., Elovici, Y., Ochoa, M.: Insight into insiders and it: a survey of insider threat taxonomies, analysis, modeling, and countermeasures. ACM Comput. Surv. 52(2), 1\u201340 (2019). https:\/\/doi.org\/10.1145\/3303771","journal-title":"ACM Comput. Surv."},{"key":"29_CR18","doi-asserted-by":"publisher","unstructured":"Moriano, P., Pendleton, J., Rich, S., Camp, L.J.: Insider threat event detection in user-system interactions. In: Proceedings of the 2017 International Workshop on Managing Insider Security Threats (MIST 2017), pp. 1\u201312. Association for Computing Machinery, New York (2017). https:\/\/doi.org\/10.1145\/3139923.3139928","DOI":"10.1145\/3139923.3139928"},{"key":"29_CR19","doi-asserted-by":"publisher","unstructured":"Harilal, A., Toffalini, F., Castellanos, J., Guarnizo, J., Homoliak, I., Ochoa, M.: TWOS: a dataset of malicious insider threat behavior based on a gamified competition. In: Proceedings of the 2017 International Workshop on Managing Insider Security Threats (MIST 2017), pp. 45\u201356. Association for Computing Machinery, New York (2017). https:\/\/doi.org\/10.1145\/3139923.3139929","DOI":"10.1145\/3139923.3139929"},{"key":"29_CR20","doi-asserted-by":"crossref","unstructured":"Sallam, A., Bertino, E.: Detection of temporal data Ex-filtration threats to relational databases. In: Proceedings of the 4th IEEE International Conference on Collaboration and Internet Computing (CIC 2018), pp. 146\u2013155. IEEE, Philadelphia (2018)","DOI":"10.1109\/CIC.2018.00030"},{"key":"29_CR21","doi-asserted-by":"crossref","unstructured":"Lu, Q., Qu, H., Zhuang, Y., Lin, X.J., Zhu, Y., Liu, Y.: A passive client-based approach to detect evil twin attacks. In: 2017 IEEE Trustcom\/BigDataSE\/ICESS, Sydney, NSW, pp. 233\u2013239 (2017)","DOI":"10.1109\/Trustcom\/BigDataSE\/ICESS.2017.242"},{"key":"29_CR22","doi-asserted-by":"crossref","unstructured":"Hall, A.J., Pitropakis, N., Buchanan, W.J., Moradpoor, N.: Predicting malicious insider threat scenarios using organizational data and a heterogeneous stack-classifier. In: 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, pp. 5034\u20135039 (2018)","DOI":"10.1109\/BigData.2018.8621922"},{"key":"29_CR23","doi-asserted-by":"crossref","unstructured":"ObserveIT: The Real Cost of Insider Threats in 2020. Retrieved from ObserveIT (2020)","DOI":"10.1016\/S1353-4858(20)30017-9"},{"key":"29_CR24","unstructured":"Le, D.C., Zincir-Heywood, A.N.: Machine learning based insider threat modelling and detection. In: 2019 IFIP\/IEEE Symposium on Integrated Network and Service Management (IM), Arlington, VA, USA, pp. 1\u20136 (2019)"},{"key":"29_CR25","doi-asserted-by":"publisher","unstructured":"Brian, L.: Insider threat test dataset. Carnegie Mellon University (2020).https:\/\/doi.org\/10.1184\/R1\/1284124","DOI":"10.1184\/R1\/1284124"}],"container-title":["Lecture Notes in Networks and Systems","Proceedings of the 2023 International Conference on Advances in Computing Research (ACR\u201923)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-33743-7_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T13:10:08Z","timestamp":1685106608000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-33743-7_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031337420","9783031337437"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-33743-7_29","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"27 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advances in Computing Research","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Orlando, FL","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 May 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 May 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acr2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iicser.org\/ACR23\/call_papers.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}