Intrusion detection system using machine learning

This study delves into the field of Intrusion Detection Systems (IDS) within the rapidly growing domain of the Internet of Things (IoT). A thorough examination of the primary types of IDS, including network intrusion detection systems (NIDS), host intrusion detection systems (HIDS), and cloud intrusion detection systems, which combine cloud, network, and host layers for a strong security model, is conducted. Additionally, common detection techniques such as signature-based detection, anomaly-based detection, and stateful protocol analysis are critically evaluated. The main contribution of this study is the development and implementation of an innovative hybrid machine learning model that combines the strength of the Random Forest algorithm with the sequence comprehension capabilities of Bidirectional Long Short-Term Memory (BI-LSTM) to significantly improve IDS performance. Unlike previous studies that often used a subset-based approach, this research rigorously tested the hybrid model using the entire Bot-IoT dataset. This study also includes a detailed analysis of the complex landscape of IoT security, with a focus on the challenges posed for intrusion detection. This provides the context for choosing the hybrid machine learning model. Impressively, when applied to the complete Bot-IoT dataset, the model demonstrated a 100% accuracy rate, significantly outperforming traditional IDS models. This research offers valuable insights into the field of IoT security and suggests an effective path towards developing comprehensive and accurate intrusion detection systems. The success of the hybrid model in utilizing the complete Bot-IoT dataset demonstrates its robustness and adaptability, making it a potential key solution for securing IoT networks in an increasingly interconnected digital world