Barnacles Mating Optimizer with Hopfield Neural Network Based Intrusion Detection in Internet of Things Environment

Owing to the development and expansion of energy-aware sensing devices and autonomous and intelligent systems, the Internet of Things (IoT) has gained remarkable growth and found uses in several day-to-day applications. Currently, the Internet of Things (IoT) network is gradually developing ubiquitous connectivity amongst distinct new applications namely smart homes, smart grids, smart cities, and several others. The developing network of smart devices and objects allows people to make smart decisions with machine to machine (M2M) communications. One of the real-world security and IoT-related challenges was vulnerable to distinct attacks which poses several security and privacy challenges. Thus, an IoT provides effective and efficient solutions. An Intrusion Detection System (IDS) is a solution for addressing security and privacy challenges with identifying distinct IoT attacks. This study develops a new Barnacles Mating Optimizer with Hopfield Neural Network based Intrusion Detection (BMOHNN-ID) in IoT environment. The presented BMOHNN-ID technique majorly concentrates on the detection and classification of intrusions from IoT environments. In order to attain this, the BMOHNN-ID technique primarily pre-processes the input data for transforming it into a compatible format. Next, the HNN model was employed for the effectual recognition and classification of intrusions from IoT environments. Moreover, the BMO technique was exploited to optimally modify the parameters related to the HNN model. When a list of possible susceptibilities of every device is ordered, every device is profiled utilizing data related to every device. It comprises routing data, the reported hostname, network flow, and topology. This data was offered to the external modules for digesting the data via REST API model. The experimental values assured that the BMOHNN-ID model has gained effectual intrusion classification performance over the other models

Modeling of Intrusion Detection System Using Double Adaptive Weighting Arithmetic Optimization Algorithm with Deep Learning on Internet of Things Environment

Abstract The Internet of Things (IoT) has experienced rapid development in area-specific applications, including smart transportation systems, healthcare, industries, and smart agriculture, to enhance socio-economic development over the past few years. This IoT system includes different actuators, interconnected sensors, and network-enabled devices that exchange various data through private networks and the Internet infrastructure. The intrusion detection system (IDS) is deployed with preventive security mechanisms, namely access control and authentication. The usual behaviors of the mechanism distinguish malicious and normal activities based on specific patterns or rules of IDSs. Therefore, this article focuses on developing IDS using Double Adaptive Weighting Arithmetic Optimization Algorithm with Deep Learning (DAWAOA-DL) approach in the IoT environment. The DAWAOA-DL methodology's objective is to recognise and classify intrusions in the IoT platform accurately. To execute this, the presented DAWAOA-DL approach involves the design of the DAWAOA technique for the feature selection procedure. Next, the convolutional neural network-gated recurrent unit (CNN-GRU) technique is used for the intrusion detection task. Finally, the Adam optimizer is exploited as a hyperparameter optimizer of the CNN-GRU methodology. A series of simulations were performed on the BoT-IoT dataset to exhibit the effectual detection performance of the DAWAOA-DL method. A widespread experimental validation demonstrated the betterment of the DAWAOA-DL method over other recent models under several metrics