Article: Effective anomaly detection in hybrid wireless IoT environment through machine learning model: a survey Journal: International Journal of Mobile Network Design and Innovation (IJMNDI) 2023 Vol.10 No.4 pp.175 - 181 Abstract: Wireless hybrid environment has seamlessly integrated inside all smart homes and in smart environments. Assaults and Anomalies are probably going to happen in open platform of IoT frameworks and which can result easily by sending fake alarms and the inability to appropriately identify basic occasions. To address that, IoT frameworks must be outfitted with peculiarity recognition preparing notwithstanding the necessary occasion identification capacity. This is a key component that empowers unwavering quality and productivity in IoT. An effective assault and oddity ready framework are a lot of important in IoT based climate, since these are power starving gadgets and convey extremely delicate information's. A proficient K-nearest neighbour (KNN) and Random Forest algorithm-based sensor information inconsistency recognition is anticipated in an edge gadget in this paper. The sensor hardware failure and software failures are identified in the IoT based environment and effective identification was achieved through KNN and Random Forest algorithm. Inderscience Publishers - linking academia, business and industry through research

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Title: Effective anomaly detection in hybrid wireless IoT environment through machine learning model: a survey

Authors: V. Shanmuganathan; Suresh Annamalai

Addresses: Department of Networking and Communications, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chengalpattu – 603203, Chennai, Tamil Nadu, India ' Department of Networking and Communications, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chengalpattu – 603203, Chennai, Tamil Nadu, India

Abstract: Wireless hybrid environment has seamlessly integrated inside all smart homes and in smart environments. Assaults and Anomalies are probably going to happen in open platform of IoT frameworks and which can result easily by sending fake alarms and the inability to appropriately identify basic occasions. To address that, IoT frameworks must be outfitted with peculiarity recognition preparing notwithstanding the necessary occasion identification capacity. This is a key component that empowers unwavering quality and productivity in IoT. An effective assault and oddity ready framework are a lot of important in IoT based climate, since these are power starving gadgets and convey extremely delicate information's. A proficient K-nearest neighbour (KNN) and Random Forest algorithm-based sensor information inconsistency recognition is anticipated in an edge gadget in this paper. The sensor hardware failure and software failures are identified in the IoT based environment and effective identification was achieved through KNN and Random Forest algorithm.

Keywords: anomaly detection; wireless IoT environment; KNN; K-nearest neighbour algorithm; Random Forest algorithm (RF); machine learning; IoT; Internet of Things.

DOI: 10.1504/IJMNDI.2023.133238

International Journal of Mobile Network Design and Innovation, 2023 Vol.10 No.4, pp.175 - 181

Received: 29 Jun 2022
Accepted: 22 Sep 2022
Published online: 03 Sep 2023 *

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Title: Effective anomaly detection in hybrid wireless IoT environment through machine learning model: a survey

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