{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T00:43:11Z","timestamp":1753922591022,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030821524"},{"type":"electronic","value":"9783030821531"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-82153-1_23","type":"book-chapter","created":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T23:26:36Z","timestamp":1628292396000},"page":"275-286","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Interpretation of Learning-Based Automatic Source Code Vulnerability Detection Model Using LIME"],"prefix":"10.1007","author":[{"given":"Gaigai","family":"Tang","sequence":"first","affiliation":[]},{"given":"Long","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Lianxiao","family":"Meng","sequence":"additional","affiliation":[]},{"given":"Weipeng","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Meikang","family":"Qiu","sequence":"additional","affiliation":[]},{"given":"Shuangyin","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Lin","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Huiqiang","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,7]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Amershi, S., Chickering, et al.: Modeltracker: redesigning performance analysis tools for machine learning. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 337\u2013346 (2015)","key":"23_CR1","DOI":"10.1145\/2702123.2702509"},{"unstructured":"Checkmarx: In: https:\/\/www.checkmarx.com\/","key":"23_CR2"},{"doi-asserted-by":"crossref","unstructured":"Chernis, B., Verma, R.: Machine learning methods for software vulnerability detection. In: Proceedings of the Fourth ACM International Workshop on Security and Privacy Analytics, pp. 31\u201339 (2018)","key":"23_CR3","DOI":"10.1145\/3180445.3180453"},{"doi-asserted-by":"publisher","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder-decoder approaches, pp. 103\u2013111. Association for Computational Linguistics (2014). https:\/\/doi.org\/10.3115\/v1\/W14-4012","key":"23_CR4","DOI":"10.3115\/v1\/W14-4012"},{"issue":"1","key":"23_CR5","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/MCOM.2017.1600349CM","volume":"55","author":"W Dai","year":"2017","unstructured":"Dai, W., Qiu, M., Qiu, L., Chen, L., Wu, A.: Who moved my data? privacy protection in smartphones. IEEE Commun. Mag. 55(1), 20\u201325 (2017)","journal-title":"IEEE Commun. Mag."},{"unstructured":"FlawFinder: In: http:\/\/www.dwheeler.com\/flawfinder","key":"23_CR6"},{"unstructured":"Fortify, H.: In: https:\/\/www.hpfod.com\/","key":"23_CR7"},{"key":"23_CR8","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.asoc.2018.03.056","volume":"70","author":"K Gai","year":"2018","unstructured":"Gai, K., Qiu, M.: Optimal resource allocation using reinforcement learning for iot content-centric services. Appl. Soft Comput. 70, 12\u201321 (2018)","journal-title":"Appl. Soft Comput."},{"issue":"4","key":"23_CR9","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/MNET.2018.1700407","volume":"32","author":"K Gai","year":"2018","unstructured":"Gai, K., Qiu, M.: Reinforcement learning-based content-centric services in mobile sensing. IEEE Netw. 32(4), 34\u201339 (2018)","journal-title":"IEEE Netw."},{"doi-asserted-by":"crossref","unstructured":"Gai, K., Qiu, M., Zhao, H., Sun, X.: Resource management in sustainable cyber-physical systems using heterogeneous cloud computing. IEEE Transactions on Sustainable Computing, pp. 1\u20131 (2018)","key":"23_CR10","DOI":"10.1109\/TSUSC.2017.2723954"},{"doi-asserted-by":"crossref","unstructured":"Gai, K., Qiu, M., Elnagdy, S.A.: Security-aware information classifications using supervised learning for cloud-based cyber risk management in financial big data. In: 2016 IEEE 2nd International Conference on Big Data Security on Cloud, pp. 197\u2013202. IEEE (2016)","key":"23_CR11","DOI":"10.1109\/BigDataSecurity-HPSC-IDS.2016.66"},{"doi-asserted-by":"crossref","unstructured":"Gai, K., Qiu, M., Sun, X., Zhao, H.: Security and privacy issues: a survey on fintech. In: International Conference on Smart Computing and Communication, pp. 236\u2013247. Springer, Cham (2016)","key":"23_CR12","DOI":"10.1007\/978-3-319-52015-5_24"},{"doi-asserted-by":"crossref","unstructured":"Gai, K., Qiu, M., Zhao, H., Dai, W.: Anti-counterfeit scheme using monte carlo simulation for e-commerce in cloud systems. In: 2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing, pp. 74\u201379. IEEE (2015)","key":"23_CR13","DOI":"10.1109\/CSCloud.2015.75"},{"issue":"6","key":"23_CR14","doi-asserted-by":"publisher","first-page":"4156","DOI":"10.1109\/TII.2019.2948094","volume":"16","author":"K Gai","year":"2019","unstructured":"Gai, K., Wu, Y., Zhu, L., Zhang, Z., Qiu, M.: Differential privacy-based blockchain for industrial internet-of-things. IEEE Trans. Ind. Inf. 16(6), 4156\u20134165 (2019)","journal-title":"IEEE Trans. Ind. Inf."},{"doi-asserted-by":"crossref","unstructured":"Groce, A., Kulesza, T., Zhang, et al.: You are the only possible oracle: effective test selection for end users of interactive machine learning systems. IEEE Trans. Softw. Eng.40(3), 307\u2013323 (2013)","key":"23_CR15","DOI":"10.1109\/TSE.2013.59"},{"unstructured":"Harer, J.A., Kim, et al.: Automated software vulnerability detection with machine learning. CoRR abs\/1803.04497 (2018)","key":"23_CR16"},{"doi-asserted-by":"crossref","unstructured":"Huang, T., Zhu, Y., Zhang, Qiu, M., et al.: An lof-based adaptive anomaly detection scheme for cloud computing. In: 2013 IEEE 37th Annual Computer Software and Applications Conference Workshops, pp. 206\u2013211. IEEE (2013)","key":"23_CR17","DOI":"10.1109\/COMPSACW.2013.28"},{"doi-asserted-by":"crossref","unstructured":"Kulesza, T., Burnett, M., Wong, W.K., Stumpf, S.: Principles of explanatory debugging to personalize interactive machine learning. In: Proceedings of the 20th international conference on intelligent user interfaces, pp. 126\u2013137 (2015)","key":"23_CR18","DOI":"10.1145\/2678025.2701399"},{"doi-asserted-by":"crossref","unstructured":"Li, Z., Zou, Deqing, A.O.: Vuldeepecker: a deep learning-based system for vulnerability detection. In: 25th Annual Network and Distributed System Security Symposium, NDSS 2018, San Diego, California, USA, 18\u201321 February 2018","key":"23_CR19","DOI":"10.14722\/ndss.2018.23158"},{"issue":"12","key":"23_CR20","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1016\/j.jpdc.2012.08.006","volume":"72","author":"J Niu","year":"2012","unstructured":"Niu, J., Gao, Y., Qiu, M., Ming, Z.: Selecting proper wireless network interfaces for user experience enhancement with guaranteed probability. J. Parallel Distrib. Comput. 72(12), 1565\u20131575 (2012)","journal-title":"J. Parallel Distrib. Comput."},{"issue":"4","key":"23_CR21","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1109\/MCC.2014.71","volume":"1","author":"M Qiu","year":"2014","unstructured":"Qiu, M., Ming, Z., Wang, J., Yang, L.T., Xiang, Y.: Enabling cloud computing in emergency management systems. IEEE Cloud Comput. 1(4), 60\u201367 (2014)","journal-title":"IEEE Cloud Comput."},{"doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cwhy should i trust you?\u201d explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135\u20131144 (2016)","key":"23_CR22","DOI":"10.1145\/2939672.2939778"},{"doi-asserted-by":"crossref","unstructured":"Savchenko, A., Fokin, O., Chernousov, A., Sinelnikova, O., Osadchyi, S.: Deedp: vulnerability detection and patching based on deep learning. Theor. Appl. Cybersecur. 2(1), 1\u20137 (2020)","key":"23_CR23","DOI":"10.20535\/tacs.2664-29132020.1.209465"},{"doi-asserted-by":"crossref","unstructured":"Shuai, B., Li, H., Li, et al.: Automatic classification for vulnerability based on machine learning. In: 2013 IEEE International Conference on Information and Automation (ICIA), pp. 312\u2013318. IEEE (2013)","key":"23_CR24","DOI":"10.1109\/ICInfA.2013.6720316"},{"unstructured":"Srikant, S., Lesimple, N., O\u2019Reilly, U.M.: Dependency-based neural representations for classifying lines of programs. CoRR abs\/2004.10166 (2020)","key":"23_CR25"},{"doi-asserted-by":"crossref","unstructured":"Tao, L., Golikov, S., Gai, K., Qiu, M.: A reusable software component for integrated syntax and semantic validation for services computing. In: 2015 IEEE Symposium on Service-Oriented System Engineering, pp. 127\u2013132. IEEE (2015)","key":"23_CR26","DOI":"10.1109\/SOSE.2015.10"},{"doi-asserted-by":"crossref","unstructured":"Thakur, K., Qiu, M., Gai, K., Ali, M.L.: An investigation on cyber security threats and security models. In: 2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing, pp. 307\u2013311. IEEE (2015)","key":"23_CR27","DOI":"10.1109\/CSCloud.2015.71"},{"doi-asserted-by":"crossref","unstructured":"Wang, S., Liu, T., Tan, L.: Automatically learning semantic features for defect prediction. In: 2016 IEEE\/ACM 38th International Conference on Software Engineering (ICSE), pp. 297\u2013308. IEEE (2016)","key":"23_CR28","DOI":"10.1145\/2884781.2884804"},{"doi-asserted-by":"crossref","unstructured":"Zhang, Q., Huang, T., Zhu, Y., Qiu, M.: A case study of sensor data collection and analysis in smart city: provenance in smart food supply chain. Int. J. Distrib. Sensor Netw. 9(11), 382132 (2013)","key":"23_CR29","DOI":"10.1155\/2013\/382132"},{"doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wu, J., Deng, J., Qiu, M.: Jamming ack attack to wireless networks and a mitigation approach. In: IEEE GLOBECOM 2008\u20132008 IEEE Global Telecommunications Conference, pp. 1\u20135. IEEE (2008)","key":"23_CR30","DOI":"10.1109\/GLOCOM.2008.ECP.950"}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-82153-1_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T23:49:00Z","timestamp":1628293740000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-82153-1_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030821524","9783030821531"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-82153-1_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tokyo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 August 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 August 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ksem2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.cloud-conf.net\/ksem21\/index.html","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":"492","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":"164","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":"0","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":"33% - 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":"10","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)"}}]}}