Smart City Gnosys

Smart city article details

Title Anomaly Behavior Analysis For Fog Nodes Availability Assurance In Iot Applications
ID_Doc 9608
Authors Pacheco J.; Benitez V.H.; Tunc C.; Grijalva C.
Year 2019
Published Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA, 2019-November
DOI http://dx.doi.org/10.1109/AICCSA47632.2019.9035338
Abstract The Internet of Things (IoT) is the new trend to make devices interact among themselves. The IoT will connect not only computers and mobile devices, but also wearable devices, smart buildings, smart cities, electrical grids, and automobiles just to name few. IoT will lead to the development of a wide range of advanced information services that need to be processed in real-time and require large storage and computational power than can be provided by Cloud and Fog Computing. The integration of IoT with Cloud and Fog Computing make IoT capable of processing large-scale geo-distributed information. In any IoT application, communications are critical to deliver the required information to the end user, device or application, for instance to take actions during crisis events. However, IoT communication elements such as gateways or Fog nodes, will introduce major security challenges as they contribute to increase the attack surface, preventing the IoT to deliver accurate information. In this paper, we propose a methodology to develop an Intrusion Detection System (IDS) based on Anomaly Behavior Analysis (ABA) to detect when a Fog node has been compromised. The preliminary experimental results show that our proposed approach accurately detects known and unknown anomalies due to misuses or cyber-attacks, with high detection rate and low false alarms. © 2019 IEEE.
Author Keywords Anomaly Behavior Analysis; Cloud Computing; Fog Computing; Internet of Things; Intrusion Detection System


Similar Articles


Id Similarity Authors Title Published
33619 View0.883Chatterjee A.; Ahmed B.S.Iot Anomaly Detection Methods And Applications: A SurveyInternet of Things (Netherlands), 19 (2022)
36913 View0.871Girubagari N.; Ravi T.N.Methods Of Anomaly Detection For The Prevention And Detection Of Cyber AttacksInternational Journal of Intelligent Engineering Informatics, 11, 4 (2024)
7367 View0.87Albulayhi K.; Sheldon F.T.An Adaptive Deep-Ensemble Anomaly-Based Intrusion Detection System For The Internet Of Things2021 IEEE World AI IoT Congress, AIIoT 2021 (2021)
6153 View0.866Alrashdi I.; Alqazzaz A.; Aloufi E.; Alharthi R.; Zohdy M.; Ming H.Ad-Iot: Anomaly Detection Of Iot Cyberattacks In Smart City Using Machine Learning2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019 (2019)
17578 View0.865Dave S.; Degadwala S.; Vyas D.Ddos Detection At Fog Layer In Internet Of ThingsInternational Conference on Edge Computing and Applications, ICECAA 2022 - Proceedings (2022)
46467 View0.864Chiba Z.; Abghour N.; Moussaid K.; Lifandali O.; Kinta R.Review Of Recent Intrusion Detection Systems And Intrusion Prevention Systems In Iot Networks2022 30th International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2022 (2022)
9646 View0.86Maniriho P.; Niyigaba E.; Bizimana Z.; Twiringiyimana V.; Mahoro L.J.; Ahmad T.Anomaly-Based Intrusion Detection Approach For Iot Networks Using Machine LearningCENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020 (2020)
17981 View0.86Himdi T.; Ishaque M.Deep Learning-Enhanced Anomaly Detection For Iot Security In Smart CitiesARPN Journal of Engineering and Applied Sciences, 19, 6 (2024)
17026 View0.859Sinaeepourfard A.; Sengupta S.; Krogstie J.; Delgado R.R.Cybersecurity In Large-Scale Smart Cities: Novel Proposals For Anomaly Detection From Edge To Cloud2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019 - Proceedings (2019)
33032 View0.859Dawoud A.; Sianaki O.A.; Shahristani S.; Raun C.Internet Of Things Intrusion Detection: A Deep Learning Approach2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (2020)