The Occupational Information Network O*NET is considered the primary source of occupational information in the U.S. I explore here possible uses of O*NET data to inform cybersecurity workforce readiness certification programs. The O*NET database is used to map out education requirements and how they relate to professional certifications as required by employers and job designers in accordance with the National Initiative for Cybersecurity Careers and Studies (NICCS). The search focuses on the “Information Security Analysts” occupation as listed on O*NET, Careeronestop, U.S. Bureau of Labor Statistics (BLS), and finally tied back to NICCS source work role to identify certifications requirements. I found that no site has listed any certification as required, desirable or mandatory. NICCS offered general guidance to potential topics and areas of certification. Careeronestop site provided the ultimate guidance for this role certification. Professional certifications are still not integrated in the Cybersecurity Workforce Framework official guidance.

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.

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