| Title | DDoS Attack Detection on Bitcoin Ecosystem using Deep-Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Baek, Ui-Jun, Ji, Se-Hyun, Park, Jee Tae, Lee, Min-Seob, Park, Jun-Sang, Kim, Myung-Sup |
| Conference Name | 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS) |
| Keywords | bitcoin, Bitcoin ecosystem, bitcoin network-level, bitcoin security, blockchain technology, computer network security, cryptocurrencies, cryptocurrency market, DDoS, DDoS attack detection, Deep-learning, detection, financial data processing, Human Behavior, network-level data, pubcrawl, Scalability, service-level DDoS attacks |
| Abstract | Since Bitcoin, the first cryptocurrency that applied blockchain technology was developed by Satoshi Nakamoto, the cryptocurrency market has grown rapidly. Along with this growth, many vulnerabilities and attacks are threatening the Bitcoin ecosystem, which is not only at the bitcoin network-level but also at the service level that applied it, according to the survey. We intend to analyze and detect DDoS attacks on the premise that bitcoin's network-level data and service-level DDoS attacks with bitcoin are associated. We evaluate the results of the experiment according to the proposed metrics, resulting in an association between network-level data and service-level DDoS attacks of bitcoin. In conclusion, we suggest the possibility that the proposed method could be applied to other blockchain systems. |
| DOI | 10.23919/APNOMS.2019.8892837 |
| Citation Key | baek_ddos_2019 |
DDoS Attack Detection on Bitcoin Ecosystem using Deep-Learning