DDoS Attack Detection on Bitcoin Ecosystem using Deep-Learning

Abstract

Since the inception of Bitcoin, the first cryptocurrency to implement blockchain technology, the cryptocurrency market has experienced significant growth.However, this growth has also brought about numerous vulnerabilities and attacks that pose a threat to the Bitcoin ecosystem.These attacks are not only focused on the Bitcoin network itself but also extend to the services that utilize it.Recent surveys have indicated the need to analyze and identify Distributed Denial of Service (DDoS) attacks, considering the interconnectedness between network-level data and service-level DDoS attacks within the Bitcoin system.Typically, the Bitcoin network is considered resilient against DDoS attacks due to the decentralized nature of its ledger.Nevertheless, there are potential vulnerabilities that could be exploited, such as message spoofing using the Transmission Control Protocol (TCP).Additionally, DDoS attacks often target services associated with Bitcoin usage rather than directly impacting the network's performance or stealing currency.Although these service-level attacks may not have an immediate impact, they can ultimately undermine the value of Bitcoin, leading to depreciation.The majority of DDoS attacks on Bitcoin-related services occur on exchanges and mining pools.Our approach involves evaluating experimental outcomes based on proposed metrics to establish a correlation between network-level data and service-level DDoS attacks in the Bitcoin system.By doing so, we aim to detect and analyze these attacks, thereby identifying potential associations.Furthermore, we posit that the methodology employed in this study could be applicable to other blockchain systems, extending its usefulness beyond the Bitcoin network.