Machine Learning and Blockchain Integration for Security Applications
Aradhita Bhandari, Aswani Kumar Cherukuri, Firuz Kamalov
Abstract
The modern world is connected by technology, with endless end devices, servers, infrastructures, and other resources creating a complex and interesting cyber landscape, constantly changing and improving, with new technologies discovered and vulnerabilities uncovered every day. In the last few years, machine learning and Blockchain technology have independently gained robust solutions for many cybersecurity problems. Machine learning has allowed for the development of smarter security systems that can automate processes such as intrusion or anomaly detection, allocation of resources, and scalability of operations. Blockchain technology has opened up more possibilities for decentralized systems that maintain or enhance security and privacy. Further, Blockchain technology has also renewed interest in smart contracts that automatically uphold agreements between parties involved. Unfortunately, their popularity has also exposed new vulnerabilities to other threats. The unique features of Blockchain and machine learning leave room for each to improve the other. Blockchain offers decentralized security and trust for the models and data for machine learning, while machine learning provides intelligent decision-making to anomaly detection, scalability, and efficiency in Blockchain networks. This work highlights how an interplay between Blockchain and ML would allow both technologies to assist 130cybersecurity-related use cases. In this chapter, cybersecurity is defined, and common vulnerabilities are outlined, following which each of the technologies mentioned above is discussed in detail. Finally, developments involving both technologies are presented, and areas for future research are identified.