Monamo, PatrickMarivate, Vukosi NTwala, B2017-06-072017-06-072016-08Monamo, P., Marivate, V. and Twala, B. 2016. Unsupervised learning for robust bitcoin fraud detection. Proceedings 15th International Information Security South Africa Conference, 17-18 August 2016, at 54 on Bath Hotel, Rosebank, Johannesburg. DOI: 10.1109/ISSA.2016.7802939978-1-5090-2473-5DOI: 10.1109/ISSA.2016.7802939http://ieeexplore.ieee.org/document/7802939/http://hdl.handle.net/10204/9252Copyright: 2016 IEEE. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, kindly consult the publisher's website.The rampant absorption of Bitcoin as a cryptographic currency, along with rising cybercrime activities, warrants utilization of anomaly detection to identify potential fraud. Anomaly detection plays a pivotal role in data mining since most outlying points contain crucial information for further investigation. In the financial world which the Bitcoin network is part of by default, anomaly detection amounts to fraud detection. This paper investigates the use of trimmed k-means, that is capable of simultaneous clustering of objects and fraud detection in a multivariate setup, to detect fraudulent activity in Bitcoin transactions. The proposed approach detects more fraudulent transactions than similar studies or reports on the same dataset.enCybercrime activitiesBitcoinCryptographic currenciesInformation SecurityConference PresentationMonamo, P., Marivate, V. N., & Twala, B. (2016). Unsupervised learning for robust bitcoin fraud detection. IEEE. http://hdl.handle.net/10204/9252Monamo, Patrick, Vukosi N Marivate, and B Twala. "Unsupervised learning for robust bitcoin fraud detection." (2016): http://hdl.handle.net/10204/9252Monamo P, Marivate VN, Twala B, Unsupervised learning for robust bitcoin fraud detection; IEEE; 2016. http://hdl.handle.net/10204/9252 .TY - Conference Presentation AU - Monamo, Patrick AU - Marivate, Vukosi N AU - Twala, B AB - The rampant absorption of Bitcoin as a cryptographic currency, along with rising cybercrime activities, warrants utilization of anomaly detection to identify potential fraud. Anomaly detection plays a pivotal role in data mining since most outlying points contain crucial information for further investigation. In the financial world which the Bitcoin network is part of by default, anomaly detection amounts to fraud detection. This paper investigates the use of trimmed k-means, that is capable of simultaneous clustering of objects and fraud detection in a multivariate setup, to detect fraudulent activity in Bitcoin transactions. The proposed approach detects more fraudulent transactions than similar studies or reports on the same dataset. DA - 2016-08 DB - ResearchSpace DP - CSIR KW - Cybercrime activities KW - Bitcoin KW - Cryptographic currencies KW - Information Security LK - https://researchspace.csir.co.za PY - 2016 SM - 978-1-5090-2473-5 T1 - Unsupervised learning for robust bitcoin fraud detection TI - Unsupervised learning for robust bitcoin fraud detection UR - http://hdl.handle.net/10204/9252 ER -