Enhancing IoT Security Using GA-HDLAD: A Hybrid Deep Learning Approach for Anomaly Detection

The adoption and use of the Internet of Things (IoT) have increased rapidly over recent years, and cyber threats in IoT devices have also become more common. Thus, the development of a system that can effectively identify malicious attacks and reduce security threats in IoT devices has become a topic of great importance. One of the most serious threats comes from botnets, which commonly attack IoT devices by interrupting the networks required for the devices to run. There are a number of methods that can be used to improve security by identifying unknown patterns in IoT networks, including deep learning and machine learning approaches. In this study, an algorithm named the genetic algorithm with hybrid deep learning-based anomaly detection (GA-HDLAD) is developed, with the aim of improving security by identifying botnets within the IoT environment. The GA-HDLAD technique addresses the problem of high dimensionality by using a genetic algorithm during feature selection. Hybrid deep learning is used to detect botnets; the approach is a combination of recurrent neural networks (RNNs), feature extraction techniques (FETs), and attention concepts. Botnet attacks commonly involve complex patterns that the hybrid deep learning (HDL) method can detect. Moreover, the use of FETs in the model ensures that features can be effectively extracted from spatial data, while temporal dependencies are captured by RNNs. Simulated annealing (SA) is utilized to select the hyperparameters necessary for the HDL approach. In this study, the GA-HDLAD system is experimentally assessed using a benchmark botnet dataset, and the findings reveal that the system provides superior results in comparison to existing detection methods.