Development of novel intrusion detection in Internet of Things using improved dart game optimizer-derived optimal cascaded ensemble learning

Background of the StudyInternet of things (IoT) industry has accelerated its development with the support of advanced information technology and economic expansion. A complete industrial foundation includes software, chips, electronic components, IoT services, integrated systems, machinery, and telecom operators, which the gradual improvement in the IoT industry system has formulated. As the exponential growth of IoT devices increases, the attack surface available to cybercriminals enables them to carry out potentially more damaging operations. As a result, the security sector has witnessed a rise in cyberattacks. Hackers use several methods to copy and modify the information in the IoT environment. Machine learning techniques are used by the intrusion detection (ID) model to determine and categorize attacks in IoT networks.ObjectivesThus, this study explores the ID system with the heuristic-assisted deep learning approaches for effectively detect the attacks in the IoT. At first, the IoT data are garnered in benchmark resources. Then, the gathered data is preprocessed to perform data cleaning. Next, the data is transformed and fed to the feature extraction stage. The feature extraction is performed with the help of one-dimensional convolutional neural network (1D-CNN), where the features are extracted from the target-based pooling layer. Then, these attained deep features are fed to the ID phase, where the cascaded ensemble learning (CEL) approach is adopted for detecting the intrusions. Here, the hyperparameter tuning is done with a new suggested improved darts game optimizer (IDGO) algorithm. Here, the main objective of the developed algorithm helps to maximize accuracy in ID.FindingsThroughout the experimental findings, the developed model provides 86% of accuracy. Thus, the finding of the developed model shows less detecting time and higher detection efficiency. Delivering a consistent and reliable cyber security strategy for IoT networks is complicated by using many IoT protocols. Due to several flaws in IoT protocols, adversaries can attack and hack network information. Threats in any situation include device disruption, data theft, and interruption. Proactive protection technology is now very helpful in improving the security of critical systems due to the development of malicious threats against critical infrastructure. The ID system is more popular as reactive network security. To preserve the network's security, the network ID analyzes the various network data through different behavioral evaluations of the network. Typically, the key components of commercial security products are signatures, thresholds, statistics, or heuristic-driven methods. On IoT networks, many critical vulnerabilities are there, which could be very dangerous. Many cyberattacks increase the sophistication, complexity, confidentiality, availability, and data integrity. The other disadvantage is it does not detect the source of the attack. Early detection of cyberattacks is used to decrease illegal activity in the network system, and also it prevents the risk of intruders during communication. To overcome these issues, the researchers develop an ID model. The architectural representation of the implemented ID model is given in the figure. image