Cyber Threat Mitigation Using Machine Learning, Deep Learning, Artificial Intelligence, and Blockchain
Snehal Paddalwar, Leena R. Ragha, Vanita Manikrao Mane · 2023
In today’s era, tasks that were meant to be impossible have now become possible with advanced technologies. It is now easy for every one of us to store, manage and share information within seconds. The world has chosen to be digital in every aspect and at a fast pace. Digitalization has made everything work at lightning speed. With all these easy access services, a huge amount of data is generated and shared in an environment in which communication occurs and this environment is nothing but cyber space. Cyber criminals perform illegal activities in this cyber space and we need technologies to prevent them from carrying out such activities. Advancements in technology have also resulted in an increase in cybercrimes. Human interventions and other devices are not sufficient for protection against cyber threats. There is a need for some advanced technology that can be used to defend against them. This chapter focuses on technologies that can play an important role in cyber threat detection, prevention and by using blockchain information, make a real-time decision to counter cyber-attacks. Cyber datasets typically contain several collections of information from various sources generated by the users’ cyber activities. This information can be analyzed to build an intelligent security model using various technologies such as machine learning, deep learning, artificial intelligence, and so on. Additionally, blockchain&s;s capability to provide data confidentiality, integrity and availability can help in mitigating various cyber threats by providing essential secured information. Thus, we can create a cyberinfrastructure using all these technologies to build an automated system capable of mitigating cyber threats and attacks. The proposed solution has four parts. The first part is to aggregate the data from various nodes in the network regarding cyber-criminal activities using artificial intelligence techniques, namely information agents. The second part is to mine the aggregated data further to extract the meaningful data highlighting possible criminal activities using machine learning techniques. The third part is to build the knowledge for classification using deep learning techniques that identify the threats and the system vulnerabilities intelligently and futuristically so that the cyber threats and attacks can be mitigated. The last part is to secure all collected insights so that attackers cannot tamper with it.