{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T10:44:35Z","timestamp":1783161875081,"version":"3.54.6"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031790065","type":"print"},{"value":"9783031790072","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\/978-3-031-79007-2_8","type":"book-chapter","created":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T20:01:28Z","timestamp":1738094488000},"page":"139-157","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Detecting Cyber and Physical Attacks Against Mobile Robots Using Machine Learning: An Empirical Study"],"prefix":"10.1007","author":[{"given":"Levente","family":"Nyusti","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sabarathinam","family":"Chockalingam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick","family":"Bours","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Terje","family":"Bodal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,29]]},"reference":[{"key":"8_CR1","doi-asserted-by":"publisher","first-page":"114195","DOI":"10.1016\/j.eswa.2020.114195","volume":"167","author":"S Cebollada","year":"2021","unstructured":"Cebollada, S., Pay\u00e1, L., Flores, M., Peidr\u00f3, A., Reinoso, O.: A state-of-the-art review on mobile robotics tasks using artificial intelligence and visual data. Expert Syst. Appl. 167, 114195 (2021). https:\/\/doi.org\/10.1016\/j.eswa.2020.114195","journal-title":"Expert Syst. Appl."},{"key":"8_CR2","doi-asserted-by":"publisher","unstructured":"Kirschgens, L.A., Ugarte, I.Z., Uriarte, E.G., Rosas, A.M., Vilches, V.M.: Robot hazards: from safety to security (2021). http:\/\/arxiv.org\/abs\/1806.06681, https:\/\/doi.org\/10.48550\/arXiv.1806.06681","DOI":"10.48550\/arXiv.1806.06681"},{"key":"8_CR3","doi-asserted-by":"publisher","unstructured":"Zheng, X.-C., Sun, H.-M.: Hijacking unmanned aerial vehicle by exploiting civil GPS vulnerabilities using software-defined radio. Sens. Mater. 32, 2729 (2020). https:\/\/doi.org\/10.18494\/SAM.2020.2783","DOI":"10.18494\/SAM.2020.2783"},{"key":"8_CR4","doi-asserted-by":"publisher","unstructured":"Feng, Z., et al.: Efficient drone hijacking detection using onboard motion sensors. In: Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1414\u20131419 (2017). https:\/\/doi.org\/10.23919\/DATE.2017.7927214","DOI":"10.23919\/DATE.2017.7927214"},{"key":"8_CR5","doi-asserted-by":"publisher","unstructured":"Noh, J., et al.: Tractor beam: safe-hijacking of consumer drones with adaptive GPS spoofing. ACM Trans. Priv. Secur. 22(12), 1\u201312:26 (2019). https:\/\/doi.org\/10.1145\/3309735","DOI":"10.1145\/3309735"},{"key":"8_CR6","doi-asserted-by":"publisher","unstructured":"Vuong, T.P., Loukas, G., Gan, D.: Performance evaluation of cyber-physical intrusion detection on a robotic vehicle. In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, pp. 2106\u20132113 (2015). https:\/\/doi.org\/10.1109\/CIT\/IUCC\/DASC\/PICOM.2015.313","DOI":"10.1109\/CIT\/IUCC\/DASC\/PICOM.2015.313"},{"key":"8_CR7","doi-asserted-by":"publisher","unstructured":"Mitchell, R., Chen, I.-R.: A survey of intrusion detection techniques for cyber-physical systems. ACM Comput. Surv. 46, 55:1\u201355:29 (2014). https:\/\/doi.org\/10.1145\/2542049","DOI":"10.1145\/2542049"},{"key":"8_CR8","doi-asserted-by":"publisher","unstructured":"Liu, S.B., Roehm, H., Heinzemann, C., L\u00fctkebohle, I., Oehlerking, J., Althoff, M.: Provably safe motion of mobile robots in human environments. In: 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1351\u20131357 (2017). https:\/\/doi.org\/10.1109\/IROS.2017.8202313","DOI":"10.1109\/IROS.2017.8202313"},{"key":"8_CR9","doi-asserted-by":"publisher","unstructured":"Markis, A., Papa, M., Kaselautzke, D., Rathmair, M., Sattinger, V., Brandst\u00f6tter, M.: Safety of mobile robot systems in industrial applications. Presented at the May 9 (2019). https:\/\/doi.org\/10.3217\/978-3-85125-663-5-00","DOI":"10.3217\/978-3-85125-663-5-00"},{"key":"8_CR10","doi-asserted-by":"publisher","unstructured":"Guo, P., Kim, H., Virani, N., Xu, J., Zhu, M., Liu, P.: RoboADS: anomaly detection against sensor and actuator misbehaviors in mobile robots. In: 2018 48th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN), pp. 574\u2013585 (2018). https:\/\/doi.org\/10.1109\/DSN.2018.00065","DOI":"10.1109\/DSN.2018.00065"},{"key":"8_CR11","doi-asserted-by":"publisher","first-page":"181","DOI":"10.3390\/computers11120181","volume":"11","author":"SO Oruma","year":"2022","unstructured":"Oruma, S.O., S\u00e1nchez-Gord\u00f3n, M., Colomo-Palacios, R., Gkioulos, V., Hansen, J.K.: A systematic review on social robots in public spaces: threat landscape and attack surface. Computers. 11, 181 (2022). https:\/\/doi.org\/10.3390\/computers11120181","journal-title":"Computers."},{"key":"8_CR12","doi-asserted-by":"crossref","unstructured":"Dudek, W., Szynkiewicz, W.: Cyber-security for mobile service robots \u2013 challenges for cyber-physical system safety. J. Telecommun. Inf. Technol. 29\u201336 (2019)","DOI":"10.26636\/jtit.2019.131019"},{"key":"8_CR13","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s10207-021-00545-8","volume":"21","author":"J-PA Yaacoub","year":"2022","unstructured":"Yaacoub, J.-P.A., Noura, H.N., Salman, O., Chehab, A.: Robotics cyber security: vulnerabilities, attacks, countermeasures, and recommendations. Int. J. Inf. Secur. 21, 115\u2013158 (2022). https:\/\/doi.org\/10.1007\/s10207-021-00545-8","journal-title":"Int. J. Inf. Secur."},{"key":"8_CR14","doi-asserted-by":"publisher","first-page":"167575","DOI":"10.1155\/2013\/167575","volume":"9","author":"NA Alrajeh","year":"2013","unstructured":"Alrajeh, N.A., Khan, S., Shams, B.: Intrusion detection systems in wireless sensor networks: a review. Int. J. Distrib. Sens. Netw. 9, 167575 (2013). https:\/\/doi.org\/10.1155\/2013\/167575","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"8_CR15","doi-asserted-by":"publisher","unstructured":"Bezemskij, A., Loukas, G., Anthony, R.J., Gan, D.: Behaviour-based anomaly detection of cyber-physical attacks on a robotic vehicle. In: 2016 15th International Conference on Ubiquitous Computing and Communications and 2016 International Symposium on Cyberspace and Security (IUCC-CSS), pp. 61\u201368 (2016). https:\/\/doi.org\/10.1109\/IUCC-CSS.2016.017","DOI":"10.1109\/IUCC-CSS.2016.017"},{"key":"8_CR16","doi-asserted-by":"publisher","unstructured":"Olivato, M., Cotugno, O., Brigato, L., Bloisi, D., Farinelli, A., Iocchi, L.: A comparative analysis on the use of autoencoders for robot security anomaly detection. In: 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 984\u2013989 (2019). https:\/\/doi.org\/10.1109\/IROS40897.2019.8968105","DOI":"10.1109\/IROS40897.2019.8968105"},{"key":"8_CR17","doi-asserted-by":"publisher","unstructured":"Kaur, H., Singh, G., Minhas, J.: A review of machine learning based anomaly detection techniques (2013). http:\/\/arxiv.org\/abs\/1307.7286, https:\/\/doi.org\/10.48550\/arXiv.1307.7286","DOI":"10.48550\/arXiv.1307.7286"},{"key":"8_CR18","first-page":"26","volume":"28","author":"V Jyothsna","year":"2011","unstructured":"Jyothsna, V., Prasad, R., Prasad, K.M.: A review of anomaly based intrusion detection systems. Int. J. Comput. Appl. 28, 26\u201335 (2011)","journal-title":"Int. J. Comput. Appl."},{"key":"8_CR19","doi-asserted-by":"publisher","unstructured":"Luo, Y., Xiao, Y., Cheng, L., Peng, G., Yao, D. (Daphne): deep learning-based anomaly detection in cyber-physical systems: progress and opportunities. ACM Comput. Surv. 54, 106:1\u2013106:36 (2021). https:\/\/doi.org\/10.1145\/3453155","DOI":"10.1145\/3453155"},{"key":"8_CR20","doi-asserted-by":"publisher","first-page":"726","DOI":"10.1109\/TETCI.2021.3100641","volume":"5","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Ti\u0148o, P., Leonardis, A., Tang, K.: A survey on neural network interpretability. IEEE Trans. Emerg. Top. Comput. Intell. 5, 726\u2013742 (2021). https:\/\/doi.org\/10.1109\/TETCI.2021.3100641","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"8_CR21","unstructured":"Spot. https:\/\/bostondynamics.com\/products\/spot\/. Accessed 31 Oct 2023"},{"key":"8_CR22","unstructured":"Spot CORE Payload (Legacy). https:\/\/support.bostondynamics.com\/s\/article\/Spot-CORE-payload-reference. Accessed 04 June 2024"},{"key":"8_CR23","unstructured":"tshark(1). https:\/\/www.wireshark.org\/docs\/man-pages\/tshark.html. Accessed 03 Oct 2023"},{"key":"8_CR24","unstructured":"sklearn.preprocessing.MinMaxScaler. https:\/\/scikit-learn\/stable\/modules\/generated\/sklearn.preprocessing.MinMaxScaler.html. Accessed 12 Nov 2023"},{"key":"8_CR25","doi-asserted-by":"publisher","unstructured":"Iliou, T., Anagnostopoulos, C.-N., Nerantzaki, M., Anastassopoulos, G.: A novel machine learning data preprocessing method for enhancing classification algorithms performance. In: Proceedings of the 16th International Conference on Engineering Applications of Neural Networks (INNS), pp. 1\u20135. Association for Computing Machinery, New York (2015). https:\/\/doi.org\/10.1145\/2797143.2797155","DOI":"10.1145\/2797143.2797155"},{"key":"8_CR26","doi-asserted-by":"publisher","unstructured":"Muhammad Ali, P., Faraj, R.: Data normalization and standardization: a technical report (2014). https:\/\/doi.org\/10.13140\/RG.2.2.28948.04489","DOI":"10.13140\/RG.2.2.28948.04489"},{"key":"8_CR27","doi-asserted-by":"publisher","first-page":"791","DOI":"10.1109\/COMST.2022.3208196","volume":"25","author":"M Shen","year":"2023","unstructured":"Shen, M., et al.: Machine learning-powered encrypted network traffic analysis: a comprehensive survey. IEEE Commun. Surv. Tutor. 25, 791\u2013824 (2023). https:\/\/doi.org\/10.1109\/COMST.2022.3208196","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"8_CR28","doi-asserted-by":"publisher","unstructured":"Shao, E.: Encoding IP address as a feature for network intrusion detection (2019). https:\/\/hammer.purdue.edu\/articles\/thesis\/Encoding_IP_Address_as_a_Feature_for_Network_Intrusion_Detection\/11307287\/1, https:\/\/doi.org\/10.25394\/PGS.11307287.v1","DOI":"10.25394\/PGS.11307287.v1"},{"key":"8_CR29","unstructured":"sklearn.preprocessing.LabelEncoder. https:\/\/scikit-learn\/stable\/modules\/generated\/sklearn.preprocessing.LabelEncoder.html. Accessed 13 Nov 2023"},{"key":"8_CR30","doi-asserted-by":"publisher","unstructured":"Xu, J., Wu, H., Wang, J., Long, M.: Anomaly transformer: time series anomaly detection with association discrepancy (2022). http:\/\/arxiv.org\/abs\/2110.02642, https:\/\/doi.org\/10.48550\/arXiv.2110.02642","DOI":"10.48550\/arXiv.2110.02642"},{"key":"8_CR31","doi-asserted-by":"publisher","unstructured":"Zhou, C., Paffenroth, R.C.: Anomaly detection with robust deep autoencoders. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 665\u2013674. Association for Computing Machinery, New York (2017). https:\/\/doi.org\/10.1145\/3097983.3098052","DOI":"10.1145\/3097983.3098052"},{"key":"8_CR32","doi-asserted-by":"publisher","unstructured":"Li, D., Chen, D., Goh, J., Ng, S.: Anomaly detection with generative adversarial networks for multivariate time series (2019). http:\/\/arxiv.org\/abs\/1809.04758, https:\/\/doi.org\/10.48550\/arXiv.1809.04758","DOI":"10.48550\/arXiv.1809.04758"},{"key":"8_CR33","doi-asserted-by":"publisher","unstructured":"Cheng, Z., Zou, C., Dong, J.: Outlier detection using isolation forest and local outlier factor. In: Proceedings of the Conference on Research in Adaptive and Convergent Systems, pp. 161\u2013168. Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1145\/3338840.3355641","DOI":"10.1145\/3338840.3355641"},{"key":"8_CR34","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","volume":"35","author":"A Creswell","year":"2018","unstructured":"Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A.A.: Generative adversarial networks: an overview. IEEE Signal Process. Mag. 35, 53\u201365 (2018). https:\/\/doi.org\/10.1109\/MSP.2017.2765202","journal-title":"IEEE Signal Process. Mag."},{"key":"8_CR35","doi-asserted-by":"publisher","unstructured":"Bank, D., Koenigstein, N., Giryes, R.: Autoencoders. In: Rokach, L., Maimon, O., Shmueli, E. (eds.) Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, pp. 353\u2013374. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-24628-9_16","DOI":"10.1007\/978-3-031-24628-9_16"},{"key":"8_CR36","unstructured":"Case Studies \u2013 Spot. https:\/\/bostondynamics.com\/case-studies\/. Accessed 11 Nov 2023"},{"key":"8_CR37","doi-asserted-by":"publisher","unstructured":"Nguyen, T.D., Nguyen, D.H.M., Tran, B.N., Vu, H., Mittal, N.: A Lightweight solution for defending against deauthentication\/disassociation attacks on 802.11 networks. In: 2008 Proceedings of 17th International Conference on Computer Communications and Networks, pp. 1\u20136 (2008). https:\/\/doi.org\/10.1109\/ICCCN.2008.ECP.51","DOI":"10.1109\/ICCCN.2008.ECP.51"},{"key":"8_CR38","doi-asserted-by":"publisher","first-page":"e49","DOI":"10.1002\/spy2.49","volume":"2","author":"S Hijazi","year":"2019","unstructured":"Hijazi, S., Obaidat, M.S.: Address resolution protocol spoofing attacks and security approaches: a survey. Secur. Priv. 2, e49 (2019). https:\/\/doi.org\/10.1002\/spy2.49","journal-title":"Secur. Priv."},{"key":"8_CR39","doi-asserted-by":"publisher","unstructured":"Son, S., Shmatikov, V.: The Hitchhiker\u2019s guide to DNS cache poisoning. In: Jajodia, S., Zhou, J. (eds.) SecureComm 2010. LNICST, vol. 50, pp. 466\u2013483. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-16161-2_27","DOI":"10.1007\/978-3-642-16161-2_27"},{"key":"8_CR40","doi-asserted-by":"crossref","unstructured":"Tan, X.Z., V\u00e1zquez, M., Carter, E.J., Morales, C.G., Steinfeld, A.: Inducing bystander interventions during robot abuse with social mechanisms. In: 2018 13th ACM\/IEEE International Conference on Human-Robot Interaction (HRI), pp. 169\u2013177 (2018)","DOI":"10.1145\/3171221.3171247"},{"key":"8_CR41","doi-asserted-by":"crossref","unstructured":"Br\u0161\u010di\u0107, D., Kidokoro, H., Suehiro, Y., Kanda, T.: Escaping from children\u2019s abuse of social robots. In: 2015 10th ACM\/IEEE International Conference on Human-Robot Interaction (HRI), pp. 59\u201366 (2015)","DOI":"10.1145\/2696454.2696468"}],"container-title":["Lecture Notes in Computer Science","Secure IT Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-79007-2_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T20:01:36Z","timestamp":1738094496000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-79007-2_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031790065","9783031790072"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-79007-2_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"29 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"NordSec","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nordic Conference on Secure IT Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Karlstad","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sweden","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nordsec2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/nordsec2024.kau.se\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}