Culminating Project Title
Date of Award
Culminating Project Type
Information Assurance: M.S.
Information Assurance and Information Systems
Herberger School of Business
Abdullah Abu Hussein
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
In the modern era, world has completely relied on software technology. As software applications became highly demanded, security concerns have arrived. Application security has become one of the chief concerns where companies have to protect their systems from vulnerabilities. Various other securities include mobile or end-point security, operation system security and network security. All these security categories are intended to protect their users and clients from the malicious intents and hackers. Application security became a prime requirement. Security risks of the applications are enveloped and lead to direct threat to the available business. All the application vulnerabilities take the advantage to compromise the software application security. Once a flaw is been found and private data access is determined, attacker will have capability to exploit the software application vulnerability to facilitate cyber crimes. The confidentiality of the data, availability and integrity of resources are targeted by the cyber crimes (“What is Application Security?” 2019). Overall, more than 13% of the reviewed sites were compromised with the web application security vulnerabilities and they are not completely extinct even with the traditional security methodologies (Application Security Vulnerability, 2014). In order to resolve these numerous common security issues, few of the detection, remediation and prevention techniques are to be used which includes defensive programming, sophisticated input validation, dynamic checks, and static source code analysis. In this paper, runtime environment framework is been introduced. This research study extracted few publications. All the publications considered various approaches to resolve the issue. In this research paper framework, machine learning is utilized to train and predict the output. Firstly, a sample java code is executed in various CPU cores and the generated output files are collected. These output files are then used to train machine learning. Machine learning results are then compared with actual output for decision statement.
Thaduri, Lakshmipriya, "DETECTING APPLICATION ANOMALIES: MACHINE LEARNING APPROACH" (2020). Culminating Projects in Information Assurance. 108.