Exploring Intrusion Detection Systems (IDS) in IoT Environments

The security of IoT networks has become a significant concern owing to the increasing count of cyber threats. Traditional Intrusion Detection Systems (IDS) struggle to detect sophisticated attacks in real-time due to resource constraints and evolving attack patterns. This study proposes a novel IDS that integrates deep learning (DL) and machine learning (ML) approaches to improve IoT security. The main objective is to develop a hybrid IDS combining Feed Forward Neural Networks (FFNN) and XGBoost to improve attack detection accuracy while minimizing computational overhead. The proposed methodology involves data preprocessing, feature selection utilizing Principal Component Analysis (PCA), and classification employing FFNN and XGBoost. The model is trained and evaluated on the CIC IoT 2023 dataset, which comprises real-time attack data, ensuring its practical relevance. The proposed model is estimated on the CIC IoT 2023 dataset, demonstrating superior accuracy (99%) compared to existing IDS techniques. This study provides valuable insights into improving IDS models for IoT security, addressing challenges such as dataset imbalance, feature selection, and classification accuracy. Results demonstrate that the hybrid FFNN-XGBoost model outperforms standalone FFNN and XGBoost classifiers, achieving an accuracy of 99%. Compared to existing IDS models, the proposed approach significantly enhances precision, recall, and F1-score, ensuring robust intrusion detection. This research contributes to IoT security by introducing a scalable and efficient hybrid IDS model. The findings offer a strong basis for future advancements in intrusion detection using DL and ML approaches.

The rise of the Internet of Things (IoT) has transformed our daily lives by connecting objects to the Internet, thereby creating interactive, automated environments. However, this rapid expansion raises major security concerns, particularly regarding intrusion detection. Traditional intrusion detection systems (IDSs) are often ill-suited to the dynamic and varied networks characteristic of the IoT. Machine learning is emerging as a promising solution to these challenges, offering the intelligence and flexibility needed to counter complex and evolving threats. This comprehensive review explores different machine learning approaches for intrusion detection in IoT systems, covering supervised, unsupervised, and deep learning methods, as well as hybrid models. It assesses their effectiveness, limitations, and practical applications, highlighting the potential of machine learning to enhance the security of IoT systems. In addition, the study examines current industry issues and trends, highlighting the importance of ongoing research to keep pace with the rapidly evolving IoT security ecosystem.