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Farhadi, M., Haddad, H., Shahriar, H..  2019.  Compliance Checking of Open Source EHR Applications for HIPAA and ONC Security and Privacy Requirements. 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). 1:704–713.
Electronic Health Record (EHR) applications are digital versions of paper-based patient's health information. They are increasingly adopted to improved quality in healthcare, such as convenient access to histories of patient medication and clinic visits, easier follow up of patient treatment plans, and precise medical decision-making process. EHR applications are guided by measures of the Health Insurance Portability and Accountability Act (HIPAA) to ensure confidentiality, integrity, and availability. Furthermore, Office of the National Coordinator (ONC) for Health Information Technology (HIT) certification criteria for usability of EHRs. A compliance checking approach attempts to identify whether or not an adopted EHR application meets the security and privacy criteria. There is no study in the literature to understand whether traditional static code analysis-based vulnerability discovered can assist in compliance checking of regulatory requirements of HIPAA and ONC. This paper attempts to address this issue. We identify security and privacy requirements for HIPAA technical requirements, and identify a subset of ONC criteria related to security and privacy, and then evaluate EHR applications for security vulnerabilities. Finally propose mitigation of security issues towards better compliance and to help practitioners reuse open source tools towards certification compliance.
Vyamajala, S., Mohd, T. K., Javaid, A..  2018.  A Real-World Implementation of SQL Injection Attack Using Open Source Tools for Enhanced Cybersecurity Learning. 2018 IEEE International Conference on Electro/Information Technology (EIT). :0198–0202.

SQL injection is well known a method of executing SQL queries and retrieving sensitive information from a website connected database. This process poses a threat to those applications which are poorly coded in the today's world. SQL is considered as one of the top 10 vulnerabilities even in 2018. To keep a track of the vulnerabilities that each of the websites are facing, we employ a tool called Acunetix which allows us to find the vulnerabilities of a specific website. This tool also suggests measures on how to ensure preventive measures. Using this implementation, we discover vulnerabilities in an actual website. Such a real-world implementation would be useful for instructional use in a foundational cybersecurity course.

Wilson, Rodney, Chi, Hongmei.  2017.  A Case Study for Mobile Device Forensics Tools. Proceedings of the SouthEast Conference. :154–157.
Smartphones have become a prominent part of our technology driven world. When it comes to uncovering, analyzing and submitting evidence in today's criminal investigations, mobile phones play a more critical role. Thus, there is a strong need for software tools that can help investigators in the digital forensics field effectively analyze smart phone data to solve crimes. This paper will accentuate how digital forensic tools assist investigators in getting data acquisition, particularly messages, from applications on iOS smartphones. In addition, we will lay out the framework how to build a tool for verifying data integrity for any digital forensics tool.
Shanthi, K., Seenivasan, D..  2015.  Detection of botnet by analyzing network traffic flow characteristics using open source tools. 2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO). :1–5.

Botnets are emerging as the most serious cyber threat among different forms of malware. Today botnets have been facilitating to launch many cybercriminal activities like DDoS, click fraud, phishing attacks etc. The main purpose of botnet is to perform massive financial threat. Many large organizations, banks and social networks became the target of bot masters. Botnets can also be leased to motivate the cybercriminal activities. Recently several researches and many efforts have been carried out to detect bot, C&C channels and bot masters. Ultimately bot maters also strengthen their activities through sophisticated techniques. Many botnet detection techniques are based on payload analysis. Most of these techniques are inefficient for encrypted C&C channels. In this paper we explore different categories of botnet and propose a detection methodology to classify bot host from the normal host by analyzing traffic flow characteristics based on time intervals instead of payload inspection. Due to that it is possible to detect botnet activity even encrypted C&C channels are used.