The BitcoinHeist: Classifications of Ransomware Crime Families

Ransomware attacks are on the rise and attackers are hijacking valuable information from different critical infrastructures and businesses requiring ransom payments to release the encrypted files. Payments in cryptocurrencies are designed to evade tracing the transactions and the recipients. With anonymity being paramount, tracing cryptocurrencies payments due to malicious activity and criminal transactions is a complicated process. Therefore, the need to identify these transactions and label them is crucial to categorize them as legitimate digital currency trade and exchange or malicious activity operations. Machine learning techniques are utilized to train the machine to recognize specific transactions and trace them back to malicious transactions or benign ones. I propose to work on the Bitcoin Heist data set to classify the different malicious transactions. The different transactions features are analyzed to predict a classifier label among the classifiers that have been identified as ransomware or associated with malicious activity. I use decision tree classifiers and ensemble learning to implement a random forest classifier. Results are assessed to evaluate accuracy, precision, and recall. I limit the study design to known ransomware identified previously and made available under the Bitcoin transaction graph from January 2009 to December 2018.

A Parallelized Database Damage Assessment Approach after Cyberattack for Healthcare Systems

In the current Internet of things era, all companies shifted from paper-based data to the electronic format. Although this shift increased the efficiency of data processing, it has security drawbacks. Healthcare databases are a precious target for attackers because they facilitate identity theft and cybercrime. This paper presents an approach for database damage assessment for healthcare systems. Inspired by the current behavior of COVID-19 infections, our approach views the damage assessment problem the same way. The malicious transactions will be viewed as if they are COVID-19 viruses, taken from infection onward. The challenge of this research is to discover the infected transactions in a minimal time. The proposed parallel algorithm is based on the transaction dependency paradigm, with a time complexity O((M+NQ+N^3)/L) (M = total number of transactions under scrutiny, N = number of malicious and affected transactions in the testing list, Q = time for dependency check, and L = number of threads used). The memory complexity of the algorithm is O(N+KL) (N = number of malicious and affected transactions, K = number of transactions in one area handled by one thread, and L = number of threads). Since the damage assessment time is directly proportional to the denial-of-service time, the proposed algorithm provides a minimized execution time. Our algorithm is a novel approach that outperforms other existing algorithms in this domain in terms of both time and memory, working up to four times faster in terms of time and with 120,000 fewer bytes in terms of memory.