Penetration Testing An Effective And Versatile Tool For Software Security
Journal Title: International Journal of engineering Research and Applications - Year 2018, Vol 8, Issue 1
Abstract
Software is being developed and released for public utility in applications of mobile phone technologies to aircraft and spaceship explorations at a very fast pace which requires proper testing prior to their release so as to make them full proof and free of malware attacks. Artificial Neural Network (ANN) has a vital role in Penetration Testing. Artificial Intelligence (AI) algorithms learn from test cases to provide sagacious insights like application stability, failure patterns, defect hotspots, failure prediction. These insights further help to anticipate, automate, and enhance decision-making capabilities. ANN makes Penetration Testing far better and user friendly for a tester by elimination of hundreds and thousands of redundant, vague, undesirable teat cases, thereby, reducing project costs. It is envisaged that in future, development of intense algorithms, AI will make Penetration Testing even better by generating new and relevant test cases for a given test system.
Authors and Affiliations
Sachin Chaudhury
Keywords
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- EP ID EP393371
- DOI 10.9790/9622-0801025258.
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