Research Article Artificial Intelligence-Based Security Protocols to Resist Attacks in Internet of Things Rashmita Khilar , 1 K. Mariyappan , 2 Mary Subaja Christo , 3 J. Amutharaj , 4 T. Anitha , 1 T. Rajendran , 5 and Areda Batu 6 1 Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India 2 Department of Computer Science and Engineering, CMR University, Bangalore, India 3 Department of Computer Science, School of Computing, SRM Institute of Science and Technology, Kattankulathur, India 4 Department of Information Science & Engineering, RajaRajeswari College of Engineering, Bangalore, India 5 Makeit Technologies (Center for Industrial Research), Coimbatore, India 6 Department of Chemical Engineering, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, Ethiopia Correspondence should be addressed to T. Rajendran; rajendranthavasimuthuphd@gmail.com Received 23 December 2021; Accepted 21 February 2022; Published 5 April 2022 Academic Editor: Fei Hao Copyright © 2022 Rashmita Khilar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. IoT (Internet of Things) usage in industrial and scientific domains is progressively increasing. Currently, IoTs are utilized in numerous applications in different domains, similar to communication technology, environmental monitoring, agriculture, medical services, and manufacturing purposes. But, the IoT systems are vulnerable against various intrusions and attacks in the perspective on the security view. It is essential to create an intrusion detection model to detect and secure the network from different attacks and anomalies that continually happen in the network. In this paper, the anomaly detection model for an IoT network using deep neural networks (DNN) with chicken swarm optimization (CSO) algorithm was proposed. Presently, the DNN has demonstrated its efficiency in different fields that are applicable to its usage. Deep learning is the type of algorithm based on machine learning which used many layers to gradually extricate more significant features of level from the raw inputs. The UNSW-NB15 dataset was utilized to evaluate the anomaly detection model. The proposed model obtained 94.85% accuracy and 96.53% detection rate which is better than other compared techniques like GA-NB, GSO, and PSO for validation. The DNN-CSO model has performed well in detecting most of the attacks, and it is appropriate for detecting anomalies in the IoT network. 1. Introduction Recently, IoT has acquired the interest of academic groups and of the ICT (information and communication technology) industry. IoT systems take on a number of facets of our daily lives, including health care, home environments, and trans- portation. Threats to IoT protection can cause serious privacy problems and economic damage [1]. IoT’s development comes along with the emergence of numerous challenges. Any of these problems also arise as exceptions to the network anomalies, i.e., abnormal network traffic flow. A flash crowd, network failure, or variations in the network traffic may pro- duce anomalies in terms of results, while attacks like probing and flooding attacks can also cause anomalies when it comes to security, attacks like Remote to Local (R2L) and User to Root (U2R) attacks [2]. Anomalies may be related to performance or security- related. Anomaly identification is an extremely important task for network operators in both situations. In particular, net- work operators needs an efficient method for quickly identify- ing abnormal unknown trends in traffic data to recognize irregular flows of traffic or the reasons of further handling anomalies [3]. In the sense of the IoT, a general description of an anomaly is the observable effects of an unpredicted Hindawi Wireless Communications and Mobile Computing Volume 2022, Article ID 1440538, 10 pages https://doi.org/10.1155/2022/1440538