Enhancing cybersecurity in cloud-assisted Internet of Things environments: A unified approach using evolutionary algorithms and ensemble learning

Internet of Things (IoT) security is an umbrella term for the strategies and tools that protect devices connected to the cloud, and the network they use to connect. The IoT connects different objects and devices through the internet to communicate with similarly connected machines or devices. An IoT botnet is a network of infected or cooperated IoT devices that can be remotely organized by cyber attackers for malicious purposes such as spreading malware, stealing data, distributed denial of service (DDoS) attacks, and engaging in other types of cybercrimes. The compromised devices can be included in any device connected to the internet and communicate data with, e.g., cameras, smart home appliances, routers, etc. Millions of devices can include an IoT botnet, making it an attractive tool for cyber attackers to launch attacks. Lately, cyberattack detection using deep learning (DL) includes training neural networks on different datasets to automatically detect patterns indicative of cyber threats, which provides an adaptive and proactive approach to cybersecurity. This study presents an evolutionary algorithm with an ensemble DL-based botnet detection and classification (EAEDL-BDC) approach. The goal of the study is to enhance cybersecurity in the cloud-assisted IoT environment via a botnet detection process. In the EAEDL-BDC technique, the primary stage of data normalization using Z-score normalization is performed. For the feature selection process, the EAEDL-BDC technique uses a binary pendulum search algorithm (BPSA). Moreover, a weighted average ensemble of three models, such as the modified Elman recurrent neural network (MERNN), gated recurrent unit (GRU), and long short-term memory (LSTM), are used. Additionally, the hyperparameter choice of the DL approaches occurs utilizing the reptile search algorithm (RSA). The experimental outcome of the EAEDL-BDC approach can be examined on the N-BaIoT database. The extensive comparison study implied that the EAEDL-BDC technique reaches a superior accuracy value of 99.53% compared to other approaches concerning distinct evaluation metrics.