Design of Blockchain enabled intrusion detection model for detecting security attacks using deep learning

Abstract

• Lockchain applications are seeing a tremendous growth in all the fields. • Yet it is prone to a lot of attacks that is of serious concern. • Designed an efficient intrusion detection system to detect the attacks. • Deep learning technique (LSTM and RNN CNN) are used to detect the attacks. • We can also predict the likelihood of attack in near future using our model. Cyber-attacks are getting more sophisticated and nuanced. Intrusion Detection Systems (IDSs) are commonly used in a variety of networks to assist in the timely detection of intrusions. In recent years, blockchain technology has got a lot of attention as a way to share data without the need for a trusted third party. In particular, data recorded in a single block cannot be modified without impacting all subsequent blocks. For an effective update, an attacker will need to monitor the majority of network nodes, which is not feasible given the current network size. This work aims to create a deep learning-based IDS model with the potential of integrating blockchain technology with intrusion detection, inspired by the ability to apply blockchain in all fields. The proposed model outperforms the conventional systems with respect to accuracy in detecting the security attacks.