Study for Integrating IoT-IDS Datasets: Machine and Deep Learning for Secure IoT Network System

The rapid expansion of Internet of Things (IoT) devices has introduced a new phase of inter connectivity and convenience, while also presenting notable security obstacles. This research paper investigates novel methodologies for enhancing the security of IoT networks by using machine learning and deep learning methodologies. In order to accomplish this objective, two well acknowledged datasets, namely UNSW-NB15 and BoT-IoT, are used for the purpose of constructing and assessing resilient security models. The UNSW-NB15 dataset is well recognized for its extensive compilation of network traffic data, while the BoT-IoT dataset is especially designed to facilitate study on security issues related to the IoT. These datasets provide a wide array of attack scenarios and network traffic patterns, therefore enhancing the diversity of available resources for researchers in this field. By using these datasets, our research endeavours to examine many crucial facets of security inside IoT networks. Initially, machine learning techniques are used for the purpose of conducting intrusion detection on IoT networks. This stage encompasses the categorization of network traffic into two distinct categories: normal and malicious. This process facilitates the prompt detection and reaction to potential threats in real-time. Deep learning methodologies demonstrate exceptional proficiency in capturing complicated patterns and behaviours inherent in IoT network traffic, hence enhancing the capacity to detect intricate and dynamic security risks. In order to assure the practical usability of the presented models, we take into account variables such as computing efficiency and scalability. This is particularly important since IoT devices often have limited resources available. Furthermore, we investigate approaches aimed at enhancing the interpretability of models, therefore offering valuable insights into the underlying decision-making mechanisms, ultimately fostering trust and transparency.

This study makes a valuable contribution to the emerging area of IoT security by showcasing the effectiveness of machine learning and deep learning techniques in enhancing the protection of IoT networks. Our work contributes to the advancement of knowledge about IoT risks and the construction of adaptive, resilient security mechanisms by using the extensive UNSW-NB15 and BoT-IoT datasets.