A Robust Framework to Countermeasure from Hackers Attacks on Software to Achieve Secured SDLC using Agile Model

Abstract

ABSTRACT newlineSDLC (Software development lifecycle) is essentially a series of steps or phases that provide a framework for developing software and managing software throughout its lifecycle. A robust SDLC strategy delivers higher quality software, fewer vulnerabilities, and less time and resources. Not only does it help to develop and maintain software, it also provides an advantage when it comes to obsolete code. newlineThe Software Development Life Cycle (SDLC) is a series of phases that provide a common understanding of the software build process. It includes all the phases needed to ensure the development of useful and powerful software products, and involves cost-effective and traceable processes. The lifecycle even describes whether the architecture can prevent the threat from occurrence. The research work has utilized a process of SDLC by means of company s framework for providing the security. The prevention mechanism in this work has utilized OOPS (Object oriented programming) architecture. newlineAgile SDLC model is a combination of iterative and incremental process models with focus on process adaptability and customer satisfaction by rapid delivery of working software product. Agile Methods break the product into small incremental builds. These builds are provided in iterations. With the help of this if the fault arises it could be changed on the particular stage rather than completion of whole process. There are various steps involved in the criteria such as planning, analysis, design, coding, testing. In this process agile model is implied as if the malware attacks it could be cleared at a particular stage rather than after the completion of whole process. newlineFor the training of OOPS, FFBP (Feed forward back propagation) method has been used. For the computation, parameters such as prediction accuracy are used with respect to AI (Artificial intelligence) and SVM (Support vector machine). The comparison has been defined with 1000 files. 93.625 is the prediction accuracy using AI and 82.47 is the prediction accuracy usin