A review on deep anomaly detection in blockchain

Abstract

The last few years have witnessed the widespread use of blockchain technology in several works because of its effectiveness in terms of privacy, security, and trustworthiness. However, the challenges of cyber-attacks represent a real threat to systems based on this technology. The resort to the systems of anomaly detection focused on deep learning, also called deep anomaly detection, is an appropriate and efficient means to tackle cyber-attacks on the blockchain. This paper provides an overview of the blockchain technology concept, including its characteristics, challenges and limitations, and its system taxonomy. Numerous blockchain cyber-attacks are discussed, such as 51% attacks, selfish mining attacks, double spending attacks, and Sybil attacks. Furthermore, we survey an overview of deep anomaly detection systems with their challenges and unresolved issues. In addition, this article gives a glimpse of various deep learning approaches implemented for anomaly detection in the blockchain environment and presents several methods that enhance the security features of anomaly detection systems. Finally, we discuss the benefits and drawbacks of these recent advanced approaches in light of three categories—discriminative learning, generative learning, and hybrid learning—with other methods based on graphs, and we highlight the ability of the proposed approaches to perform real-time anomaly detection.