Enhanced Intrusion Detection System Using Hybrid-Inspired Algorithms and Conditional Generative Adversarial Networks for Internet of Things Security

This research proposes a new Intrusion Detection System architecture that aims at improving the protection of IoT through the integration of multiple techniques. The system utilizes a dataset called Bot-IoT that contains unbalanced data, to develop a stable Intrusion Detection System. The methodology is divided into three stages: data pre-processing, synthetic data generation and bio-inspired hybrid features selection. The first process includes cleaning, encoding as well as scaling the data set for the machine learning algorithms such as Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, Deep Neural Network, Support Vector Machine, and Decision Tree to be affected. The second stage involves the use of a novel architecture of a Conditional Generative Adversarial Network, with a two-discriminator structure to enhance the generation of synthetic data that will improve the balance and the overall quality of the dataset used for training. It is described how the validity of the synthetic data is assessed statistically, and how the generated data affects different models of machine learning. The last step uses meta-heuristic bio-hybrid algorithms for selecting features. The two methods of crocodile hunting search and bee optimization are integrated with Recursive Feature Elimination to select superior features from the dataset used in the experiment. The integration of these models allows for achieving the highest levels of detection with a minimum of false positives. The research advances the field of artificial intelligence by enhancing Conditional Generative Adversarial Networks (CGAN) with a two-discriminator architecture for synthetic data generation, coupled with a novel hybrid feature selection algorithm. This AI innovation is applied to the development of an Intrusion Detection System (IDS) aimed at improving the cybersecurity of Internet of Things (IoT) networks.“