| Title | Hybrid Model for Web Application Vulnerability Assessment Using Decision Tree and Bayesian Belief Network |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Thirumaran, M., Moshika, A., Padmanaban, R. |
| Conference Name | 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN) |
| Keywords | Bayes methods, Bayesian belief network, Bayesian Belief Network (BBN), belief networks, business data processing, business process, Classification algorithms, composability, consumer relationship, Decision Tree, Decision trees, Entropy, feature extraction, hybrid model, Internet, learning (artificial intelligence), logical Web pages, machine learning, machine learning algorithms, pubcrawl, Resiliency, security of data, vulnerability assessment, Web application vulnerability assessment |
| Abstract | In the existing situation, most of the business process are running through web applications. This helps the enterprises to grow their business efficiently which creates a good consumer relationship. But the main problem is that they failed to provide a vulnerable free environment. To overcome this issue in web applications, vulnerability assessment should be made periodically. They are many vulnerability assessment methodologies which occur earlier are not much proactive. So, machine learning is needed to provide a combined solution to determine vulnerability occurrence and percentage of vulnerability occurred in logical web pages. We use Decision Tree and Bayesian Belief Network (BBN) as a collective solution to find either vulnerability occur in web applications and the vulnerability occurred percentage on different logical web pages. |
| DOI | 10.1109/ICSCAN.2019.8878686 |
| Citation Key | thirumaran_hybrid_2019 |
Hybrid Model for Web Application Vulnerability Assessment Using Decision Tree and Bayesian Belief Network