Visible to the public Vulnerability prediction through self-learning model

TitleVulnerability prediction through self-learning model
Publication TypeConference Paper
Year of Publication2017
AuthorsMajumder, R., Som, S., Gupta, R.
Conference Name2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS)
Date Publisheddec
ISBN Number978-1-5386-0514-1
KeywordsActivation Function, artificial neural network, Artificial neural networks, compositionality, Human Behavior, learning (artificial intelligence), Mathematical model, Metrics, neural nets, Predictive models, pubcrawl, Resiliency, self-learning model, Software, Software algorithms, software defect, software development phase, software fault, software fault tolerance, software reliability, Software Vulnerability, Training, vulnerability detection, Vulnerability impact, Vulnerability prediction

Vulnerability being the buzz word in the modern time is the most important jargon related to software and operating system. Since every now and then, software is developed some loopholes and incompleteness lie in the development phase, so there always remains a vulnerability of abruptness in it which can come into picture anytime. Detecting vulnerability is one thing and predicting its occurrence in the due course of time is another thing. If we get to know the vulnerability of any software in the due course of time then it acts as an active alarm for the developers to again develop sound and improvised software the second time. The proposal talks about the implementation of the idea using the artificial neural network, where different data sets are being given as input for being used for further analysis for successful results. As of now, there are models for studying the vulnerabilities in the software and networks, this paper proposal in addition to the current work, will throw light on the predictability of vulnerabilities over the due course of time.

Citation Keymajumder_vulnerability_2017