Cyber Security Intrusion Detection and Bot Data Collection using Deep Learning in the IoT

Abstract

In the digital age, cybersecurity is a growing concern, especially as IoT continues to grow rapidly. Cybersecurity intrusion detection systems are critical in protecting IoT environments from malicious activity. Deep learning approaches have emerged as promising intrusion detection techniques due to their ability to automatically learn complex patterns and features from large-scale data sets. In this research, we give a detailed assessment of the use of deep learning algorithms for cybersecurity intrusion detection in IoT contexts. The study discusses the challenges of securing IoT systems, such as device heterogeneity, limited computational resources, and the dynamic nature of IoT networks. To detect intrusions in IoT environments, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been used. The NF-UQ-NIDS and NF-Bot-IoT data sets are used for training and assessing deep learning-based intrusion detection systems. Our study also explores using deep learning approaches to identify botnets in IoT settings to counter the growing threat of botnets. Also, analyze representative bot data sets and explain their significance in understanding botnet behavior and effective defenses. The study evaluated IDS performance and traffic flow in the IoT context using various machine learning algorithms. For IoT environments, the results highlight the importance of selecting appropriate algorithms and employing effective data pre-processing techniques to improve accuracy and performance. Cyber-attack detection with the proposed system is highly accurate when compared with other algorithms for both NF-UQ-NIDS and NF-BoT-IoT data sets.