Abstract
IoT has eased our life by providing various smart applications such as Smart Homes, Industrial Automation, Smart Healthcare, Smart Traffic Monitoring, and Fleet Management, to name a few. Due to various vulnerabilities in IoT infrastructure and software, Anomaly Detection is one of the major concerns for application developers. In this paper, Smart Home application of the IoT paradigm has been presented. We proposed an Anomaly Detection System (ADS) for the Kitchen Area Network (KAN), which has been deployed with the help of the MQTT Broker and various sensors on the IoT Flock Emulator. IoT Network has been created with the help of an IoT Network Emulator. Network Traffic packet sniffer (i.e., Wireshark) is used to capture the flows. The KNN machine learning model is used for binary classification to detect anomalies. The proposed model achieves accuracy, recall, precision, and F-1 score of 94.37%, 94.31%, 95.40%, and 94.85%, respectively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Raj H, Kumar M, Kumar P, Singh A, Verma OP (2022) Issues and challenges related to privacy and security in healthcare using IoT, fog, and cloud computing. In: Advanced healthcare systems. Wiley, pp 21–32. https://doi.org/10.1002/9781119769293.ch2
Singh S, Kumar M, Verma OP, Kumar R, Gill SS (2023) An IIoT based secure and sustainable smart supply chain system using sensor networks. Trans Emerg Telecommun Technol 34(2). https://doi.org/10.1002/ett.4681
Wei Z, Wang F (2022) Detecting anomaly data for IoT sensor networks. Sci Program 2022(1):1–7. https://doi.org/10.1155/2022/4671381
Chen L, Li Y, Deng X, Liu Z, Lv M, Zhang H (2022) Dual auto-encoder GAN-based anomaly detection for industrial control system. Appl Sci 12(10):4986. https://doi.org/10.3390/app12104986
Ullah I, Mahmoud QH (2022) Design and development of RNN-based anomaly detection model for IoT networks. IEEE Access 1. https://doi.org/10.1109/access.2022.3176317
Wang Y, Du X, Lu Z, Duan Q, Wu J (2022) Improved LSTM-based time-series anomaly detection in rail transit operation environments. IEEE Trans Ind Inform 3203(c):1–11. https://doi.org/10.1109/TII.2022.3164087
de Melo PHAD, Miani RS, Rosa PF (2022) FamilyGuard: a security architecture for anomaly detection in home networks. Sensors (Basel, Switzerland) 22(8):2895. https://doi.org/10.3390/s22082895
Hu J, Kaur K, Lin H, Wang X, Hassan MM, Razzak I, Hammoudeh M (2023) Intelligent anomaly detection of trajectories for IoT empowered maritime transportation systems. IEEE Trans Intell Transp Syst 1–10. https://doi.org/10.1109/TITS.2022.3162491
Best L, Foo E A hybrid approach: utilising k-means clustering and Naive Bayes for IoT anomaly detection, pp 1–43
Bovenzi G, Foggia A, Santella S, Testa A, Persico V, Pescap A (2022) Data poisoning attacks against autoencoder-based anomaly detection models: a robustness analysis, no May
Iot I, Feng X, Xiangyu S (2012) Fog computing based distributed forecasting of cyber-attacks in Internet of Things, no Kott 2015
Cook AA, Misirli G, Fan Z (2020) Anomaly detection for IoT time-series data: a survey. IEEE Internet Things J 7(7):6481–6494. https://doi.org/10.1109/JIOT.2019.2958185
Hasan M, Islam MM, Zarif MII, Hashem MMA (2019) Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet Things (Neth) 7:100059. https://doi.org/10.1016/j.iot.2019.100059
Aminanto ME, Purbomukti IR, Chandra H, Kim K (2022) Two-dimensional projection-based wireless intrusion classification using lightweight EfficientNet. Comput, Mater Contin 72(3):5301–5314. https://doi.org/10.32604/cmc.2022.026749
Popoola SI, Adebisi B, Gui G, Hammoudeh M, Gacanin H, Dancey D (2022) Optimizing deep learning model hyperparameters for botnet attack detection in IoT networks attack detection in IoT networks. IEEE Internet Things J 0–16. https://doi.org/10.36227/techrxiv.19501885.v1
Hussain F, Abbas SG, Shah GA, Pires IM, Fayyaz UU, Shahzad F, Garcia NM, Zdravevski E (2021) A framework for malicious traffic detection in IoT healthcare environment. Sensors (Basel, Switzerland) 21(9):3025. https://doi.org/10.3390/s21093025
Moustafa N (2021) A new distributed architecture for evaluating AI-based security systems at the edge: network TON_IoT datasets. Sustain Cities Soc 72. https://doi.org/10.1016/j.scs.2021.102994
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Siddiqui, M.A., Kalra, M., Rama Krishna, C. (2024). Anomaly Detection for IoT-Enabled Kitchen Area Network Using Machine Learning. In: Verma, O.P., Wang, L., Kumar, R., Yadav, A. (eds) Machine Intelligence for Research and Innovations. MAiTRI 2023. Lecture Notes in Networks and Systems, vol 832. Springer, Singapore. https://doi.org/10.1007/978-981-99-8129-8_17
Download citation
DOI: https://doi.org/10.1007/978-981-99-8129-8_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8128-1
Online ISBN: 978-981-99-8129-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)