{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:07:38Z","timestamp":1772042858227,"version":"3.50.1"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031942624","type":"print"},{"value":"9783031942631","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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\/978-3-031-94263-1_23","type":"book-chapter","created":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T19:14:45Z","timestamp":1748805285000},"page":"417-436","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MAD-HOT: Mixed Rate DDoS Attack Detection in IEEE 802.15 4e\/TSCH Networks Using Hoeffding Optimized Trees"],"prefix":"10.1007","author":[{"given":"Pradeepkumar","family":"Bhale","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Darpan","family":"Maurya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vaibhav","family":"Sodhi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tabish","family":"Farooqui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Harsh","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sonam","family":"Maurya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,2]]},"reference":[{"key":"23_CR1","unstructured":"Abdelmoumin, G.: Rawat: on the performance of machine learning models for anomaly-based intelligent intrusion detection systems for the internet of things. IEEE Internet Things J. PP(99), 1 (2021)"},{"issue":"1","key":"23_CR2","first-page":"4731953","volume":"2016","author":"I Almomani","year":"2016","unstructured":"Almomani, I., Al-Kasasbeh, M.: WSN-DS: a dataset for intrusion detection systems in wireless sensor networks. J. Sens. 2016(1), 4731953 (2016)","journal-title":"J. Sens."},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"Bhale, P., Biswas, S., Nandi, S.: An adaptive and lightweight solution to detect mixed rate ip spoofed ddos attack in iot ecosystem. In: 2018 15th IEEE India Council International Conference (INDICON), pp. 1\u20136. IEEE (2018)","DOI":"10.1109\/INDICON45594.2018.8987008"},{"key":"23_CR4","doi-asserted-by":"crossref","unstructured":"Bhale, P., Biswas, S., Nandi, S.: LORD: low rate ddos attack detection and mitigation using lightweight distributed packet inspection agent in iot ecosystem. In: 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), pp. 1\u20136. IEEE (2019)","DOI":"10.1109\/ANTS47819.2019.9118052"},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Bhale, P., Biswas, S., Nandi, S.: ML for IEEE 802.15. 4e\/TSCH: energy efficient approach to detect DDoS attack using machine learning. In: 2021 International Wireless Communications and Mobile Computing (IWCMC), pp. 1477\u20131482. IEEE (2021)","DOI":"10.1109\/IWCMC51323.2021.9498637"},{"issue":"4","key":"23_CR6","doi-asserted-by":"publisher","DOI":"10.1002\/itl2.433","volume":"6","author":"P Bhale","year":"2023","unstructured":"Bhale, P., Biswas, S., Nandi, S.: Effective injection of adversarial botnet attacks in IoT ecosystem using evolutionary computing. Internet Technol. Lett. 6(4), e433 (2023)","journal-title":"Internet Technol. Lett."},{"issue":"10","key":"23_CR7","doi-asserted-by":"publisher","first-page":"8357","DOI":"10.1109\/JIOT.2023.3234530","volume":"10","author":"P Bhale","year":"2023","unstructured":"Bhale, P., Chowdhury, D.R., Biswas, S., Nandi, S.: OPTIMIST: lightweight and transparent IDS with optimum placement strategy to mitigate mixed-rate DDoS attacks in IoT networks. IEEE Internet Things J. 10(10), 8357\u20138370 (2023)","journal-title":"IEEE Internet Things J."},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"Bhale, P., Dey, S., Biswas, S., Nandi, S.: Energy efficient approach to detect sinkhole attack using roving IDS in 6LoWPAN network. In: Innovations for Community Services: 20th International Conference, I4CS 2020, Bhubaneswar, India, 12\u201314 January 2020, Proceedings 20, pp. 187\u2013207. Springer (2020)","DOI":"10.1007\/978-3-030-37484-6_11"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Choudhary, S., Kesswani, S.: An Ensemble Intrusion Detection Model For Internet of Things Network. Research Square (April 2021). License: CC BY 4.0","DOI":"10.21203\/rs.3.rs-479157\/v1"},{"key":"23_CR10","doi-asserted-by":"crossref","unstructured":"Dietterich, T.G.: Ensemble methods in machine learning. In: International Workshop on Multiple Classifier Systems, pp. 1\u201315. Springer (2000)","DOI":"10.1007\/3-540-45014-9_1"},{"key":"23_CR11","unstructured":"Garcia, Sebastian, M.J.: IoT-23: a labeled dataset with malicious and benign IoT network traffic. Stratosphere Lab., Praha, Czech Republic, Technical report (2020)"},{"key":"23_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102871","volume":"173","author":"N Garcia","year":"2021","unstructured":"Garcia, N., Alcaniz, T., Gonz\u00e1lez-Vidal, A., Bernabe, J.B., Rivera, D., Skarmeta, A.: Distributed real-time SlowDoS attacks detection over encrypted traffic using Artificial Intelligence. J. Netw. Comput. Appl. 173, 102871 (2021)","journal-title":"J. Netw. Comput. Appl."},{"key":"23_CR13","doi-asserted-by":"crossref","unstructured":"Hosmer\u00a0Jr, D.W., Lemeshow, S., Sturdivant, R.X.: Applied Logistic Regression. John Wiley & Sons, Hoboken (2013)","DOI":"10.1002\/9781118548387"},{"key":"23_CR14","doi-asserted-by":"publisher","first-page":"163412","DOI":"10.1109\/ACCESS.2021.3131014","volume":"9","author":"F Hussain","year":"2021","unstructured":"Hussain, F.: A two-fold machine learning approach to prevent and detect IoT botnet attacks. IEEE Access 9, 163412\u2013163430 (2021)","journal-title":"IEEE Access"},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Khandelwal, M., Gupta, D.K., Bhale, P.: DoS attack detection technique using back propagation neural network. In: 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1064\u20131068. IEEE (2016)","DOI":"10.1109\/ICACCI.2016.7732185"},{"key":"23_CR16","doi-asserted-by":"crossref","unstructured":"Laghari, A.A., Wu, K., Laghari, R.A., Ali, M., Khan, A.A.: A review and state of art of Internet of Things (IoT). Arch. Comput. Methods Eng. 1\u201319 (2021)","DOI":"10.1007\/s11831-021-09622-6"},{"key":"23_CR17","doi-asserted-by":"crossref","unstructured":"Li, Y., Lu, Y.: LSTM-BA: DDoS detection approach combining LSTM and Bayes. In: 2019 Seventh International Conference on Advanced Cloud and Big Data (CBD), pp. 180\u2013185. IEEE (2019)","DOI":"10.1109\/CBD.2019.00041"},{"key":"23_CR18","doi-asserted-by":"crossref","unstructured":"Liu, B., Tang, D., Yan, Y., Zheng, Z., Zhang, S., Zhou, J.: TS-SVM: detect LDoS attack in SDN based on two-step self-adjusting SVM. In: 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp. 678\u2013685. IEEE (2021)","DOI":"10.1109\/TrustCom53373.2021.00100"},{"key":"23_CR19","doi-asserted-by":"crossref","unstructured":"Mahanta, K., Maringanti, H.B., Bhale, P.: Effective intrusion detection model using raptor optimized deep convolutional neural network. In: 2023 IEEE Guwahati Subsection Conference (GCON), pp. 1\u20136. IEEE (2023)","DOI":"10.1109\/GCON58516.2023.10183520"},{"key":"23_CR20","doi-asserted-by":"crossref","unstructured":"Muallem, A., Shetty, J., Biswal, B.: Hoeffding tree algorithms for anomaly detection in streaming datasets: a survey. J. Inf. Secur. 8(4) (2017)","DOI":"10.4236\/jis.2017.84022"},{"key":"23_CR21","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1016\/j.comnet.2016.03.018","volume":"107","author":"A Oliveira","year":"2016","unstructured":"Oliveira, A., Vazao, T.: Low-power and lossy networks under mobility: a survey. Comput. Netw. 107, 339\u2013352 (2016)","journal-title":"Comput. Netw."},{"key":"23_CR22","doi-asserted-by":"publisher","first-page":"155859","DOI":"10.1109\/ACCESS.2020.3019330","volume":"8","author":"JA Perez-Diaz","year":"2020","unstructured":"Perez-Diaz, J.A., Valdovinos, I.A., Choo, K., Zhu, D.: A flexible SDN-based architecture for identifying and mitigating low-rate DDoS attacks using machine learning. IEEE Access 8, 155859\u2013155872 (2020)","journal-title":"IEEE Access"},{"issue":"4","key":"23_CR23","doi-asserted-by":"publisher","first-page":"3559","DOI":"10.1109\/JIOT.2020.2973176","volume":"7","author":"N Ravi","year":"2020","unstructured":"Ravi, N., Shalinie, S.M.: Learning-driven detection and mitigation of ddos attack in iot via sdn-cloud architecture. IEEE Internet Things J. 7(4), 3559\u20133570 (2020)","journal-title":"IEEE Internet Things J."},{"issue":"9","key":"23_CR24","doi-asserted-by":"publisher","first-page":"8852","DOI":"10.1109\/JIOT.2020.2996425","volume":"7","author":"M Saharkhizan","year":"2020","unstructured":"Saharkhizan, M., Azmoodeh, A., Dehghantanha, A., Choo, K., Parizi, R.M.: An ensemble of deep recurrent neural networks for detecting IoT cyber attacks using network traffic. IEEE Internet Things J. 7(9), 8852\u20138859 (2020)","journal-title":"IEEE Internet Things J."},{"key":"23_CR25","doi-asserted-by":"crossref","unstructured":"Sharafaldin, I., Lashkari, A.H., Hakak, S., Ghorbani, A.A.: Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy. In: 2019 International Carnahan Conference on Security Technology (ICCST), pp. 1\u20138. IEEE (2019)","DOI":"10.1109\/CCST.2019.8888419"},{"key":"23_CR26","unstructured":"Velinov, A., Mileva, A.: Running and testing applications for Contiki OS using Cooja simulator. In: International Conference on Information Technology and Development of Education\u2013ITRO, vol. 2016 (2016)"},{"issue":"2","key":"23_CR27","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1109\/JSEN.2013.2285411","volume":"14","author":"X Vilajosana","year":"2013","unstructured":"Vilajosana, X., Wang, Q., Chraim, T., Pister, K.S.: A realistic energy consumption model for TSCH networks. IEEE Sens. J. 14(2), 482\u2013489 (2013)","journal-title":"IEEE Sens. J."}],"container-title":["Communications in Computer and Information Science","Innovations for Community Services"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-94263-1_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T19:14:50Z","timestamp":1748805290000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-94263-1_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031942624","9783031942631"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-94263-1_23","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"I4CS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Innovations for Community Services","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"i4cs2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.i4cs-conference.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}