A novel reinforcement learning-based hybrid intrusion detection system on fog-to-cloud computing
Year: 2024
Type: article
Abstract: The increasing growth of the Internet of Things and its open and shared character has led to a rise in new attacks exponentially. As a consequence, the quick detection of attacks in IoT environments is essential. The intrusion detection system (IDS) is responsible for protecting and detecting the type of attacks. Creating an IDS that works in real-time and adapts to environmental changes is critical. In this paper, we propose a deep reinforcement learning-based (DRL) adaptive IDS that addresses the mentioned challenges. DRL-based IDS helps to create a decision agent, who controls the interaction with the indeterminate environment and performs binary detection (normal/intrusion) in fog. For multi-class classification of attacks, we use the ensemble method in the cloud. The proposed approach is evaluated on the CICIDS2018 dataset, in binary and multiclass classification. The results show that the proposed model achieves a comparable performance in detecting intrusions and identifying attacks and it reduces the prediction time significantly. Furthermore, it is a suitable solution for underrepresented attacks. Overall, combining multiple methods can be a great way for an intrusion detection system.
Source: The Journal of Supercomputing
Institution: Qazvin Islamic Azad University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4401976140