{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T14:24:26Z","timestamp":1761402266861,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031213847"},{"type":"electronic","value":"9783031213854"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-21385-4_3","type":"book-chapter","created":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T05:03:52Z","timestamp":1670907832000},"page":"27-40","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An Appraisal of Cyber-Attacks and Countermeasures Using Machine Learning Algorithms"],"prefix":"10.1007","author":[{"given":"Akhilendranath","family":"Mummadi","sequence":"first","affiliation":[]},{"given":"B. Midhun Krishna","family":"Yadav","sequence":"additional","affiliation":[]},{"given":"Rachamalla","family":"Sadhwika","sequence":"additional","affiliation":[]},{"given":"S.","family":"Shitharth","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,14]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","unstructured":"Angra, S., Ahuja, S.: Machine learning and its applications: a review. In: 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), pp. 57\u201360 (2017). https:\/\/doi.org\/10.1109\/ICBDACI.2017.8070809","DOI":"10.1109\/ICBDACI.2017.8070809"},{"key":"3_CR2","unstructured":"Tiwari, Mohit, Kumar, Raj, Bharti, Akash, Kishan, Jai: Intrusion detection system. Int. J. Tech. Res. Appl. 5, 2320\u20138163 (2017)"},{"key":"3_CR3","doi-asserted-by":"publisher","unstructured":"Zeeshan Ahmad, Adnan Shahid Khan, Cheah Wai Shiang, Johari Abdullah, Farhan Ahmad: Network intrusion detection system: a systematic study of machine learning and deep learning approaches. Trans. Emerg. Telecommun. Technol. 32(1), e4150 (2021). https:\/\/doi.org\/10.1002\/ett.4150","DOI":"10.1002\/ett.4150"},{"key":"3_CR4","doi-asserted-by":"publisher","unstructured":"Navada, A., Ansari, A.N., Patil, S., Sonkamble, B.A.: Overview of use of decision tree algorithms in machine learning. In: 2011 IEEE Control and System Graduate Research Colloquium, pp. 37\u201342 (2011). https:\/\/doi.org\/10.1109\/ICSGRC.2011.5991826","DOI":"10.1109\/ICSGRC.2011.5991826"},{"key":"3_CR5","doi-asserted-by":"publisher","unstructured":"Selvarajan, Shitharth, Shaik, Masood, Ameerjohn, Sirajudeen, Kannan, Sangeetha: Mining of intrusion attack in SCADA network using clustering and genetically seeded flora-based optimal classification algorithm. IET Inf. Secur. 14(1), 1\u201311 (2020). https:\/\/doi.org\/10.1049\/iet-ifs.2019.0011","DOI":"10.1049\/iet-ifs.2019.0011"},{"key":"3_CR6","doi-asserted-by":"publisher","unstructured":"Hosameldin Ahmed, Asoke K. Nandi: Artificial Neural Networks (ANNs). In: Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines, pp.239\u2013258. IEEE (2019). https:\/\/doi.org\/10.1002\/9781119544678.ch12","DOI":"10.1002\/9781119544678.ch12"},{"key":"3_CR7","doi-asserted-by":"publisher","unstructured":"Ghosh, S., Dasgupta, A., Swetapadma, A.\u201d A study on support vector machine based linear and non-linear pattern classification. In: 2019 International Conference on Intelligent Sustainable Systems (ICISS), pp. 24\u201328 (2019). https:\/\/doi.org\/10.1109\/ISS1.2019.8908018","DOI":"10.1109\/ISS1.2019.8908018"},{"key":"3_CR8","doi-asserted-by":"publisher","unstructured":"Buczak, A.L., Guven, E.:. A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun. Surv. Tutor. 18(2), 1 (2015). https:\/\/doi.org\/10.1109\/COMST.2015.2494502","DOI":"10.1109\/COMST.2015.2494502"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Shitharth, S., Sangeetha, Praveen Kumar: Integrated probability relevancy classification (IPRC) for IDS in SCADA. In: Design Framework for Wireless Network, Lecture notes in network and systems, vol. 82, Issue 1, pp. 41\u201364. Springer (2019).","DOI":"10.1007\/978-981-13-9574-1_3"},{"key":"3_CR10","doi-asserted-by":"publisher","unstructured":"Kulhare, R., Singh, D.: Survey paper on intrusion detection techniques. Int. J. Comput. Technol. 6, 329\u2013335 (2007). https:\/\/doi.org\/10.24297\/ijct.v6i2.3498","DOI":"10.24297\/ijct.v6i2.3498"},{"key":"3_CR11","doi-asserted-by":"publisher","first-page":"108346","DOI":"10.1109\/ACCESS.2020.3001350","volume":"8","author":"S Zavrak","year":"2020","unstructured":"Zavrak, S., Iskefiyeli, M.: Anomaly-based intrusion detection from network flow features using variational autoencoder. IEEE Access. 8, 108346\u2013108358 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3001350","journal-title":"IEEE Access."},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Thomas, C., Sharma, V., Balakrishnan, N.: Usefulness of DARPA dataset for intrusion detection system evaluation. Proc. SPIE Int. Soc. Opt. Eng. 6973, 8pp (2008)","DOI":"10.1117\/12.777341"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.: A detailed analysis of the KDD CUP 99 data set. In: Submitted to Second IEEE Symposium on Computational Intelligencefor Security and Defense Applications (CISDA) (2009)","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Moustafa, N., Slay, J.: UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: Military Communications and Information Systems Conference (MilCIS). IEEE (2015)","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"3_CR15","doi-asserted-by":"publisher","first-page":"4396","DOI":"10.3390\/app9204396","volume":"9","author":"H Liu","year":"2019","unstructured":"Liu, H., Lang, B.: Machine learning and deep learning methods for intrusion detection systems: a survey. Appl. Sci. 9, 4396 (2019). https:\/\/doi.org\/10.3390\/app9204396","journal-title":"Appl. Sci."},{"key":"3_CR16","unstructured":"Halfond, W.G., Viegas, J., Orso, A.: A classification of SQL-injection attacks and countermeasures. In: Proceedings of the IEEE International Symposium on Secure Software Engineering, vol. 1, pp. 13\u201315. IEEE (March 2006)"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Schuba, C.L., et al.: Analysis of a denial-of-service attack on TCP.\u00a0In: Proceedings 1997 IEEE Symposium on Security and Privacy (Cat. No. 97CB36097). IEEE (1997)","DOI":"10.1109\/SECPRI.1997.601338"},{"key":"3_CR18","unstructured":"Kotei, D.N., Yeboah, J.A., Ansong, E.D.: The use of machine learning algorithms to detect man-in-the-middle (MITM) attack in user datagram protocol packet header. Res. J. Inform. Technol. (March 2020)"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Kolter, J., Maloof, M.: Learning to detect malicious executables in the wild. In: Proceedings of KDD\u201904, pp 470\u2013478 (2004)","DOI":"10.1145\/1014052.1014105"},{"key":"3_CR20","doi-asserted-by":"publisher","unstructured":"Jin, Y., Tomoishi, M., Matsuura, S.: A detection method against DNS cache poisoning attacks using machine learning techniques: work in progress. In: 2019 IEEE 18th International Symposium on Network Computing and Applications (NCA), pp. 1\u20133 (2019). https:\/\/doi.org\/10.1109\/NCA.2019.8935025","DOI":"10.1109\/NCA.2019.8935025"},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Do, V.T., Engelstad, P., Feng, B., Do, T.V.: Detection of DNS tunneling in mobile networks using machine learning. In: International Conference on Information Science and Applications, pp. 221\u2013230 (2017)","DOI":"10.1007\/978-981-10-4154-9_26"},{"key":"3_CR22","unstructured":"Mishra, S.: SQL injection detection using machine learning.\u00a0Master\u2019s Projects. 727 (2019)"},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Buczak, L., Hanke, P.A., Cancro, G.J., Toma, M.K., Watkins, L.A., Chavis, J.S.: Detection of tunnels in PCAP data by random forests. In: Proceedings of the 11th Annual Cyber and Information Security Research Conference, CISRC 2016 (April 2016)","DOI":"10.1145\/2897795.2897804"},{"key":"3_CR24","doi-asserted-by":"publisher","unstructured":"Ning, R., Wang, C., Xin, C., Li, J., Zhu, L., Wu, H.: CapJack: capture in-browser crypto-jacking by deep capsule network through behavioral analysis. In: IEEE INFOCOM 2019 - IEEE Conference on Computer Communications, pp. 1873\u20131881 (2019). https:\/\/doi.org\/10.1109\/INFOCOM.2019.8737381","DOI":"10.1109\/INFOCOM.2019.8737381"},{"key":"3_CR25","doi-asserted-by":"publisher","first-page":"1668","DOI":"10.1016\/j.procs.2018.05.137","volume":"132","author":"L Mathur","year":"2018","unstructured":"Mathur, L., Raheja, M., Chaudhary, P.: Botnet detection via mining of network traffic flow. Procedia Comput. Sci. 132, 1668\u20131677 (2018). https:\/\/doi.org\/10.1016\/j.procs.2018.05.137","journal-title":"Procedia Comput. Sci."},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Sambangi, S., Gondi, L.: A machine learning approach for DDoS attack detection using multiple linear regression. In: Presented at the 14th International Conference on Interdisciplinary in Engineering-INTER-ENG 2020, Targu mures, Romania (2020)","DOI":"10.3390\/proceedings2020063051"},{"key":"3_CR27","doi-asserted-by":"crossref","unstructured":"Najafabadi, M.M., Khoshgoftaar, T., Kemp, C., Seliya, N.: Machine learning for detecting brute force attacks at the network level. In: Conference: 2014 IEEE International Conference of Bioinformatics and Bioengineering (BIBE) (2014)","DOI":"10.1109\/BIBE.2014.73"},{"issue":"3","key":"3_CR28","first-page":"22","volume":"9","author":"A Kumar","year":"2019","unstructured":"Kumar, A.: Design of secure image fusion technique using cloud for privacy-preserving and copyright protection. Int. J. Cloud Appl. Comput. 9(3), 22\u201336 (2019)","journal-title":"Int. J. Cloud Appl. Comput."},{"issue":"1","key":"3_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13638-020-01826-x","volume":"2020","author":"A Kumar","year":"2020","unstructured":"Kumar, A., Zhang, Z.J., Lyu, H.: Object detection in real time based on improved single shot multi-box detector algorithm. EURASIP J. Wirel. Commun. Netw. 2020(1), 1\u201318 (2020). https:\/\/doi.org\/10.1186\/s13638-020-01826-x","journal-title":"EURASIP J. Wirel. Commun. Netw."},{"key":"3_CR30","unstructured":"Kumar, A.: A review on implementation of digital image watermarking techniques using LSB and DWT. In: The Third International Conference on Information and Communication Technology for Sustainable Development (ICT4SD 2018), held during August 30\u201331, 2018 at Hotel Vivanta by Taj, Goa, India (2018)"},{"key":"3_CR31","doi-asserted-by":"crossref","unstructured":"Shitharth, Prince Winston, D.: An enhanced optimization algorithm for intrusion detection in SCADA network. J. Comput. Secur., Elsevier, 70, 16\u201326 (2017)","DOI":"10.1016\/j.cose.2017.04.012"}],"container-title":["Communications in Computer and Information Science","Artificial Intelligence and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21385-4_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T15:24:46Z","timestamp":1691594686000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21385-4_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031213847","9783031213854"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21385-4_3","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":"14 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAIDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hyderabad","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"17 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icaids2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icaids.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"195","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":"43","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":"22% - 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":"4","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":"4","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)"}}]}}