Smart City Gnosys

Smart city article details

Title	Malicious Node Detection In Heterogeneous Internet Of Things
ID_Doc	36208
Authors	Goel S.; Somya; Sutar S.; Mekala P.
Year	2023
Published	Lecture Notes in Electrical Engineering, 1027 LNEE
DOI	http://dx.doi.org/10.1007/978-981-99-1312-1_13
Abstract	In recent years, security has been gaining popularity in resource-constrained IoT ecosystems as it has less capability to run conventional encryption standards. IoT comprises several communication protocols like Bluetooth Low Energy, Wi-Fi, and Zigbee for different applications, including home automation, smart city, etc. With such a heterogeneous system, it becomes complex to secure it as with every different protocol comes more vulnerabilities in the network. Anomaly-based detection methods have increasingly attracted the scientific community in recent years. Deep packet inspection evaluates the network traffic and forms a set of informative features that formalizes the system’s normal and anomalous nature. We classify a normal or abnormal activity using machine learning algorithms and present the results of our detection system implemented on a heterogeneous IoT testbed. This system is applicable for companies, offices, government organizations, or secret agencies to increase network security to protect their systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Author Keywords	Anomaly detection; BLE; Communication protocol; IoT; Security; Wi-Fi

Similar Articles

Id	Similarity	Authors	Title	Published
33032	0.898	Dawoud A.; Sianaki O.A.; Shahristani S.; Raun C.	Internet Of Things Intrusion Detection: A Deep Learning Approach	2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (2020)
9646	0.887	Maniriho P.; Niyigaba E.; Bizimana Z.; Twiringiyimana V.; Mahoro L.J.; Ahmad T.	Anomaly-Based Intrusion Detection Approach For Iot Networks Using Machine Learning	CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020 (2020)
47766	0.884	Plazas Olaya M.K.; Vergara Tejada J.A.; Aedo Cobo J.E.	Securing Microservices-Based Iot Networks: Real-Time Anomaly Detection Using Machine Learning	Journal of Computer Networks and Communications, 2024 (2024)
9648	0.883	Alsoufi M.A.; Razak S.; Siraj M.M.; Nafea I.; Ghaleb F.A.; Saeed F.; Nasser M.	Anomaly-Based Intrusion Detection Systems In Iot Using Deep Learning: A Systematic Literature Review	Applied Sciences (Switzerland), 11, 18 (2021)
33346	0.875	Berhili M.; Chaieb O.; Benabdellah M.	Intrusion Detection Systems In Iot Based On Machine Learning: A State Of The Art	Procedia Computer Science, 251 (2024)
33619	0.874	Chatterjee A.; Ahmed B.S.	Iot Anomaly Detection Methods And Applications: A Survey	Internet of Things (Netherlands), 19 (2022)
2510	0.872	Florrence J.M.; Antoinette A.; Buvaneswari S.; Wanare A.L.; Vashistha A.; Mulpuri M.; Rambabu R.	A Mathematical Model For Enhancing Cybersecurity In Iot Networks Using Lstm-Based Anomaly Detection And Optimization	Communications on Applied Nonlinear Analysis, 32, 2 (2025)
6153	0.872	Alrashdi I.; Alqazzaz A.; Aloufi E.; Alharthi R.; Zohdy M.; Ming H.	Ad-Iot: Anomaly Detection Of Iot Cyberattacks In Smart City Using Machine Learning	2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019 (2019)
36080	0.871	Ahanger T.A.; Ullah I.; Algamdi S.A.; Tariq U.	Machine Learning-Inspired Intrusion Detection System For Iot: Security Issues And Future Challenges	Computers and Electrical Engineering, 123 (2025)
7014	0.869	Reis M.J.C.S.	Ai-Driven Anomaly Detection For Securing Iot Devices In 5G-Enabled Smart Cities	Electronics (Switzerland), 14, 12 (2025)