IoT botnet attack detection of ensemble classifier of customised POA optimisation

IoT-based attacks have steadily increased in quantity due to the growing use of IoT devices. The method is useful for resolving optimisation issues since it finds a balance between exploitation and exploration. However, the current intrusion detection systems may have trouble spotting intricate attack patterns if they are not familiar with IoT gadgets and their vulnerabilities to mitigate this challenge. This work introduces the IoT botnet attack detection of customised pelican optimisation algorithm (IoT-BADS-CPOA). Initially, the data normalisation is carried out. From the normalised data, higher-order features, improved correlation, statistical features, and improved technical features are derived as the features. The features are extracted; the presence of attacks is detected by a deep ensemble of classification models. Particularly, a new self-improved version of pelican algorithm is introduced, to tune DQN. In particular, the C-POA has an accuracy of 93.86%.