Volume 7 , Issue 1 , PP: 18-27, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Prashant Kumar Shukla 1 *
Doi: https://doi.org/10.54216/IJWAC.070102
The Internet of Things (IoT) is a cutting-edge piece of cybernetic infrastructure that will eventually link all manner of previously disconnected physical objects to the web. The IoT is rapidly expanding into many facets of human life. IoT's attack surface has grown as a result of the technology's hyper-connectivity and inherent heterogeneity. In addition, IoT devices are used in both managed and unmanaged settings, leaving them open to innovative attacks. Fog computing is used in the proposed intrusion detection system for IoT applications to implement intrusion detection in a decentralised manner. Attack detection at fog nodes and summarization on a cloud server make up the proposed system's two parts. The local fog nodes in the IoT environment examine the traffic, and then they send a report to the cloud server that summarises the current global security state of the IoT application. According to the results of the experiments, the fog nodes are able to identify the attack 27% more quickly while also reducing the number of false alarms. The work that has been recommended provides a beginning point for the creation of a fog-based intrusion detection system that can be used for applications related to the IoT. The proposed system has a false alarm rate of only 0.32% and an accuracy of 98.15 percent. The proposed method can only identify attacks that conform to specific patterns.
Fog , IoT , ANN , OSELM.
[1]. Alaba, FA, Othman, M, Hashem, IAT & Alotaibi, F 2017, 'IoTs security: A survey', Journal of Network and Computer Applications, vol. 88, pp. 10-28.
[2]. Diro, AA & Chilamkurti, N 2017, ‘Distributed attack detection scheme using deep learning approach for IoTs’, Future Generation Computer Systems, vol.82, pp 761-768.
[3]. Sedjelmaci, H, Senouci, SM & Al-Bahri, M 2016, ‘A lightweight anomaly detection technique for low-resource iot devices: A game-theoretic methodology’, IEEE International Conference on Communications (ICC), pp. 1-6.
[4]. Alsmadi, A.M.; Aloglah, R.M.A.; Abu-Darwish, N.J.S.; al Smadi, A.; Alshabanah, M.; Alrajhi, D.; Alkhaldi, H.; Alsmadi, M.K. Fog Computing Scheduling Algorithm for Smart City. Int. J. Electr. Comput. Eng. 2021, 11, 2219–2228.
[5]. Yakubu, J.; Abdulhamid, S.M.; Christopher, H.A.; Chiroma, H.; Abdullahi, M. Security Challenges in Fog-Computing Environment: A Systematic Appraisal of Current Developments. J. Reliab. Intell. Environ. 2019, 5, 209–233.
[6]. Wang, J.; Li, D. Adaptive Computing Optimization in Software-Defined Network-Based Industrial IoTs with Fog Computing. Sensors 2018, 18, 2509.
[7]. Zahmatkesh, H.; Al-Turjman, F. Fog Computing for Sustainable Smart Cities in the IoT Era: Caching Techniques and Enabling Technologies—An Overview. Sustain. Cities Soc. 2020, 59, 102139. [8]. Kraemer, F.A.; Braten, A.E.; Tamkittikhun, N.; Palma, D. Fog Computing in Healthcare-A Review and Discussion. IEEE Access 2017, 5, 9206–9222.
[9]. Dar, B.K.; Shah, M.A.; Islam, S.U.; Maple, C.; Mussadiq, S.; Khan, S. Delay-Aware Accident Detection and Response System Using Fog Computing. IEEE Access 2019, 7, 70975–70985.
[10]. Sahil; Sood, S.K. Fog-Cloud Centric IoT-Based Cyber Physical Framework for Panic Oriented Disaster Evacuation in Smart Cities. Earth Sci. Inform. 2022, 15, 1449–1470.
[11]. Mahmud, R.; Ramamohanarao, K.; Buyya, R. Application Management in Fog Computing Environments: A Taxonomy, Review and Future Directions. ACM Comput. Surv. 2020, 53, 1–43.
[12]. Puliafito, C.; Gonçalves, D.M.; Lopes, M.M.; Martins, L.L.; Madeira, E.; Mingozzi, E.; Rana, O.; Bittencourt, L.F. MobFogSim: Simulation of Mobility and Migration for Fog Computing. Simul. Model. Pract. Theory 2020, 101, 188–218.
[13]. Javadzadeh, G.; Rahmani, A.M. Fog Computing Applications in Smart Cities: A Systematic Survey. Wirel. Netw. 2020, 26, 1433–1457.
[14]. Villegas-Ch., W.; García-Ortiz, J.; Urbina-Camacho, I.; Mera-Navarrete, A. Proposal for a System for the Identification of the Concentration of Students Who Attend Online Educational Models. Computers 2023, 12, 74.
[15]. Alzoubi, Y.I.; Al-Ahmad, A.; Jaradat, A. Fog Computing Security and Privacy Issues, Open Challenges, and Blockchain Solution: An Overview. Int. J. Electr. Comput. Eng. 2021, 11, 5081–5088.
[16]. Khan, S.; Parkinson, S.; Qin, Y. Fog Computing Security: A Review of Current Applications and Security Solutions. J. Cloud Comput. 2017, 6, 1–22.
[17]. Roy V. "An Improved Image Encryption Consuming Fusion Transmutation and Edge Operator." Journal of Cybersecurity and Information Management, Vol. 8, No. 1, 2021 ,PP. 42-52.
[18]. Zhang, P.Y.; Zhou, M.C.; Fortino, G. Security and Trust Issues in Fog Computing: A Survey. Future Gener. Comput. Syst. 2018, 88, 16–27.
[19]. Hu, P.; Dhelim, S.; Ning, H.; Qiu, T. Survey on Fog Computing: Architecture, Key Technologies, Applications and Open Issues. J. Netw. Comput. Appl. 2017, 98, 27–42.
[20]. Abdali, T.A.N.; Hassan, R.; Aman, A.H.M.; Nguyen, Q.N. Fog Computing Advancement: Concept, Architecture, Applications, Advantages, and Open Issues. IEEE Access 2021, 9, 75961–75980.
[21]. Margariti, S.V.; Dimakopoulos, V.V.; Tsoumanis, G. Modeling and Simulation Tools for Fog Computing-A Comprehensive Survey from a Cost Perspective. Future Internet 2020, 12, 89.
[22]. Joseph B. Awotunde , Hrudaya K. Tripathy , Anjan Bandyopadhyay, Hybrid Particle Swarm Optimization with Firefly based Resource Provisioning Technique for Data Fusion Fog-Cloud Computing Platforms, Fusion: Practice and Applications, Vol. 8 , No. 2 , (2022) : 25-35 (Doi : https://doi.org/10.54216/FPA.080203)
[23]. Tuli, Shreshth & Mirhakimi, Fatemeh & Pallewatta, Samodha & Zawad, Syed & Casale, Giuliano & Javadi, Bahman & Yan, Feng & Buyya, Rajkumar & Jennings, Nicholas. (2023). AI augmented Edge and Fog computing: Trends and challenges. Journal of Network and Computer Applications. 216. 103648. 10.1016/j.jnca.2023.103648.
