{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T17:18:51Z","timestamp":1780939131262,"version":"3.54.1"},"reference-count":32,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,4,7]],"date-time":"2024-04-07T00:00:00Z","timestamp":1712448000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Mobile crowdsensing (MCS) systems rely on the collective contribution of sensor data from numerous mobile devices carried by participants. However, the open and participatory nature of MCS renders these systems vulnerable to adversarial attacks or data poisoning attempts where threat actors can inject malicious data into the system. There is a need for a detection system that mitigates malicious sensor data to maintain the integrity and reliability of the collected information. This paper addresses this issue by proposing an adaptive and robust model for detecting malicious data in MCS scenarios involving sensor data from mobile devices. The proposed model incorporates an adaptive learning mechanism that enables the TCN-based model to continually evolve and adapt to new patterns, enhancing its capability to detect novel malicious data as threats evolve. We also present a comprehensive evaluation of the proposed model\u2019s performance using the SherLock datasets, demonstrating its effectiveness in accurately detecting malicious sensor data and mitigating potential threats to the integrity of MCS systems. Comparative analysis with existing models highlights the performance of the proposed TCN-based model in terms of detection accuracy, with an accuracy score of 98%. Through these contributions, the paper aims to advance the state of the art in ensuring the trustworthiness and security of MCS systems, paving the way for the development of more reliable and robust crowdsensing applications.<\/jats:p>","DOI":"10.3390\/s24072353","type":"journal-article","created":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T06:04:58Z","timestamp":1712556298000},"page":"2353","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["An Adaptive Temporal Convolutional Network Autoencoder for Malicious Data Detection in Mobile Crowd Sensing"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4840-9345","authenticated-orcid":false,"given":"Nsikak","family":"Owoh","sequence":"first","affiliation":[{"name":"Department of Cyber Security and Networks, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Cowcaddens Road, Glasgow G4 0BA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7610-8702","authenticated-orcid":false,"given":"Jackie","family":"Riley","sequence":"additional","affiliation":[{"name":"Department of Cyber Security and Networks, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Cowcaddens Road, Glasgow G4 0BA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1016-0791","authenticated-orcid":false,"given":"Moses","family":"Ashawa","sequence":"additional","affiliation":[{"name":"Department of Cyber Security and Networks, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Cowcaddens Road, Glasgow G4 0BA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6253-5287","authenticated-orcid":false,"given":"Salaheddin","family":"Hosseinzadeh","sequence":"additional","affiliation":[{"name":"Department of Cyber Security and Networks, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Cowcaddens Road, Glasgow G4 0BA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2723-3301","authenticated-orcid":false,"given":"Anand","family":"Philip","sequence":"additional","affiliation":[{"name":"Department of Cyber Security and Networks, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Cowcaddens Road, Glasgow G4 0BA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3739-8521","authenticated-orcid":false,"given":"Jude","family":"Osamor","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Westminster, 309 Regent Street, London W1B 2HW, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102011","DOI":"10.1016\/j.sysarc.2021.102011","article-title":"On blockchain integration into mobile crowdsensing via smart embedded devices: A comprehensive survey","volume":"115","author":"Chen","year":"2021","journal-title":"J. Syst. Archit."},{"key":"ref_2","first-page":"1","article-title":"When sharing economy meets iot: Towards fine-grained urban air quality monitoring through mobile crowdsensing on bike-share system","volume":"4","author":"Wu","year":"2020","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1924","DOI":"10.1109\/TMC.2020.2973980","article-title":"PACE: Privacy-preserving and quality-aware incentive mechanism for mobile crowdsensing","volume":"20","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6274114","DOI":"10.1155\/2022\/6274114","article-title":"IOTA-based Mobile crowd sensing: Detection of fake sensing using logit-boosted machine learning algorithms","volume":"2022","author":"Hameed","year":"2022","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.aci.2018.10.002","article-title":"Security analysis of mobile crowd sensing applications","volume":"18","author":"Owoh","year":"2020","journal-title":"Appl. Comput. Inform."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.chb.2018.10.028","article-title":"Mobile crowd sensing\u2013Taxonomy, applications, challenges, and solutions","volume":"101","author":"Boubiche","year":"2019","journal-title":"Comput. Hum. Behav."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dasari, V.S., Kantarci, B., Pouryazdan, M., Foschini, L., and Girolami, M. (2020). Game theory in mobile crowdsensing: A comprehensive survey. Sensors, 20.","DOI":"10.3390\/s20072055"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/TSMC.2020.2968516","article-title":"Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series","volume":"52","author":"Yin","year":"2020","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"107156","DOI":"10.1016\/j.comnet.2020.107156","article-title":"User satisfaction aware maximum utility task assignment in mobile crowdsensing","volume":"172","author":"Yucel","year":"2020","journal-title":"Comput. Netw."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Owoh, N.P., and Singh, M.M. (2020). SenseCrypt: A security framework for mobile crowd sensing applications. Sensors, 20.","DOI":"10.3390\/s20113280"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"106017","DOI":"10.1016\/j.asoc.2019.106017","article-title":"APAD: Autoencoder-based Payload Anomaly Detection for industrial IoE","volume":"88","author":"Kim","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"107751","DOI":"10.1016\/j.asoc.2021.107751","article-title":"Temporal convolutional autoencoder for unsupervised anomaly detection in time series","volume":"112","author":"Thill","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Dhole, H., Sutaone, M., and Vyas, V. (2019, January 6\u20138). Anomaly Detection using Convolutional Spatiotemporal Autoencoder. Proceedings of the 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kanpur, India.","DOI":"10.1109\/ICCCNT45670.2019.8944523"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Aloul, F., Zualkernan, I., Abdalgawad, N., Hussain, L., and Sakhnini, D. (2021, January 14\u201315). Network intrusion detection on the IoT edge using adversarial autoencoders. Proceedings of the 2021 International Conference on Information Technology (ICIT), Amman, Jordan.","DOI":"10.1109\/ICIT52682.2021.9491694"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Shahid, M.R., Blanc, G., Zhang, Z., and Debar, H. (2019, January 26\u201328). Anomalous communications detection in IoT networks using sparse autoencoders. Proceedings of the 2019 IEEE 18th International Symposium on Network Computing and Applications (NCA), Cambridge, MA, USA.","DOI":"10.1109\/NCA.2019.8935007"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Khanam, S., Ahmedy, I., Idris, M.Y.I., and Jaward, M.H. (2022). Towards an Effective Intrusion Detection Model Using Focal Loss Variational Autoencoder for Internet of Things (IoT). Sensors, 22.","DOI":"10.3390\/s22155822"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"8434","DOI":"10.1109\/ACCESS.2022.3144208","article-title":"Optimized deep autoencoder model for internet of things intruder detection","volume":"10","author":"Lahasan","year":"2022","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1109\/TNSM.2021.3088326","article-title":"Chronos: Ddos attack detection using time-based autoencoder","volume":"19","author":"Salahuddin","year":"2021","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yang, K., Zhang, J., Xu, Y., and Chao, J. (2020, January 20\u201324). DDoS Attacks Detection with AutoEncoder. Proceedings of the NOMS 2020\u20142020 IEEE\/IFIP Network Operations and Management Symposium, Budapest, Hungary.","DOI":"10.1109\/NOMS47738.2020.9110372"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"65520","DOI":"10.1109\/ACCESS.2020.2985089","article-title":"IMPACT: Impersonation attack detection via edge computing using deep autoencoder and feature abstraction","volume":"8","author":"Lee","year":"2020","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bernieri, G., Conti, M., and Turrin, F. (2019, January 10). KingFisher: An Industrial Security Framework based on Variational Autoencoders. Proceedings of the 1st Workshop on Machine Learning on Edge in Sensor Systems, New York, NY, USA.","DOI":"10.1145\/3362743.3362961"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/MPRV.2018.03367731","article-title":"N-baiot\u2014Network-based detection of iot botnet attacks using deep autoencoders","volume":"17","author":"Meidan","year":"2018","journal-title":"IEEE Pervasive Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1390","DOI":"10.1109\/TIFS.2018.2878538","article-title":"Generative neural networks for anomaly detection in crowded scenes","volume":"14","author":"Wang","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"100713","DOI":"10.1016\/j.iot.2023.100713","article-title":"A deep learning framework for target localization in error-prone environment","volume":"22","author":"Mohammed","year":"2023","journal-title":"Internet Things"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Alharam, A., Otrok, H., Elmedany, W., Bakht, A.B., and Alkaabi, N. (2021, January 29\u201330). AI-Based Anomaly and Data Posing Classification in Mobile Crowd Sensing. Proceedings of the 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), Zallaq, Bahrain.","DOI":"10.1109\/3ICT53449.2021.9581443"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Afzal-Houshmand, S., Homayoun, S., and Giannetsos, T. (2021, January 7\u201310). A Perfect Match: Deep Learning Towards Enhanced Data Trustworthiness in Crowd-Sensing Systems. Proceedings of the 2021 IEEE International Mediterranean Conference on Communications and Networking (MeditCom), Athens, Greece.","DOI":"10.1109\/MeditCom49071.2021.9647554"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compenvurbsys.2017.09.005","article-title":"Automatic detection of traffic lights, street crossings and urban roundabouts combining outlier detection and deep learning classification techniques based on GPS traces while driving","volume":"68","year":"2018","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Venkatesh, S.V., Prasannakumaran, D., Bosco, J.J., Kumaar, R.P., and Vijayaraghavan, V. (2021, January 16\u201318). A Non-intrusive Machine Learning Solution for Malware Detection and Data Theft Classification in Smartphones. Proceedings of the International Conference on Computational Science, Krakow, Poland.","DOI":"10.1007\/978-3-030-77967-2_17"},{"key":"ref_29","first-page":"1572","article-title":"A comparison of machine learning techniques for android malware detection using apache spark","volume":"14","author":"Memon","year":"2019","journal-title":"J. Eng. Sci. Technol."},{"key":"ref_30","unstructured":"Zheng, Y., and Srinivasan, S. (2020). Advanced Information Networking and Applications: Proceedings of the 33rd International Conference on Advanced Information Networking and Applications (AINA-2019), Springer."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Mirsky, Y., Shabtai, A., Rokach, L., Shapira, B., and Elovici, Y. (2016, January 28). Sherlock vs moriarty: A smartphone dataset for cybersecurity research. Proceedings of the 2016 ACM workshop on Artificial Intelligence and Security, Vienna, Austria.","DOI":"10.1145\/2996758.2996764"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.patrec.2017.07.016","article-title":"A study of deep convolutional auto-encoders for anomaly detection in videos","volume":"105","author":"Ribeiro","year":"2018","journal-title":"Pattern Recognit. Lett."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2353\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:24:30Z","timestamp":1760106270000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2353"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,7]]},"references-count":32,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["s24072353"],"URL":"https:\/\/doi.org\/10.3390\/s24072353","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,7]]}}}