The Role of Artificial Intelligence in Enhancing Automated Threat Hunting

Abstract : An increasing number of enterprises are using artificial intelligence (AI) to improve their cyber security and threat intelligence. AI is a type of AI that generates new data independently of preexisting data or expert knowledge. One emerging cyberthreat to systems that has been increasing is adversarial attacks. By generating fictitious accounts and transactions, adversarial attacks can interfere with and take advantage of decentralized apps that operate on the Ethereum network. Because fraudulent materials (such as accounts and transactions) used as malicious payloads can be mistaken for legitimate data, detecting adversarial attacks can be difficult. This paper suggests a paradigm for cyber threat hunting in the Ethereum blockchain that makes use of Adversarial Networks (GAN) and Deep Recurrent Neural Networks (RNN). By considering a variety of sources and data points, this technology enables decision support systems to automatically and rapidly identify threats posed by hackers or other harmful actors. The likelihood of a successful assault can be further decreased by using AI to find weaknesses in an organization's infrastructure. Because security operations centers (SOCs) need to quickly identify threats and take defensive action, this technology is particularly well-suited for them. AI can give businesses an extra line of defense against increasingly complex threats by integrating intriguing and useful data items that would have otherwise gone unnoticed

Keyword Artificial intelligence, Threat intelligent, machine learning, threat hunting, deep learning, autonomous treat intelligent, Adversarial Networks (GAN), Deep Recurrent Neural Networks (RNN).