Methodology Development for Improving the Performance of Critical Classification Applications

People interact with different critical applications in day-to-day life. Some examples of critical applications include computer programs, anonymous vehicles, digital healthcare, smart homes, etc. There are inherent risks in these critical applications if they fail to perform properly. In my dissertation, we mainly focus on developing methodologies for performance improvement for software security and healthcare prognosis. Cryptographic vulnerability tools are used to detect misuses of Java cryptographic APIs and thus classify secure and insecure parts of code. These detection tools are critical applications as misuse of cryptographic libraries and APIs causes devastating security and privacy implications. We develop two benchmarks that help developers to identify secure and insecure code usage as well as improve their tools. We also perform a comparative analysis of four static analysis tools. The developed benchmarks enable the first scientific comparison of the accuracy and scalability of cryptographic API misuse detection. Many published detection tools (CryptoGuard, CrySL, Oracle Parfait) have used our benchmarks to improve their performance in terms of the detection capability of insecure cases. We also examine the need for performance improvement for healthcare applications. Numerous prediction applications are developed to predict patients' health conditions. These are critical applications where misdiagnosis can cause serious harm to patients, even death. Due to the imbalanced nature of many clinical datasets, our work provides empirical evidence showing various prediction deficiencies in a typical machine learning model. We observe that missed death cases are 3.14 times higher than missed survival cases for mortality prediction. Also, existing sampling methods and other techniques are not well-equipped to achieve good performance. We design a double prioritized (DP) technique to mitigate representational bias or disparities across race and age groups. we show DP consistently boosts the minority class recall for underrepresented groups, by up to 38.0%. Our DP method also shows better performance than the existing methods in terms of reducing relative disparity by up to 88% in terms of minority class recall. Incorrect classification in these critical applications can have significant ramifications. Therefore, it is imperative to improve the performance of critical applications to alleviate risk and harm to people.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Computer Science and Applications

Department dc:contributor.department

Computer Science and Applications

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2023

Author dc:creator

• Afrose, Sharmin

Chair dc:contributor.committeechair

• Yao, Danfeng

Committee members dc:contributor.committeemember

• Meng, Na
• Prakash, Bodicherla Aditya
• Huang, Sharon Xialoei
• Murali, T. M.

• Software Security
• Machine Learning Bias

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:36250

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/113210