Biblio

In this study, a systematic mapping study was conducted to systematically evaluate publications on Intrusion Detection Systems with Deep Learning. 6088 papers have been examined by using systematic mapping method to evaluate the publications related to this paper, which have been used increasingly in the Intrusion Detection Systems. The goal of our study is to determine which deep learning algorithms were used mostly in the algortihms, which criteria were taken into account for selecting the preferred deep learning algorithm, and the most searched topics of intrusion detection with deep learning algorithm model. Scientific studies published in the last 10 years have been studied in the IEEE Explorer, ACM Digital Library, Science Direct, Scopus and Wiley databases.

Software security is an important aspect that needs to be considered during the entire software development life cycle (SDLC). Integrating software security at each phase of SDLC has become an urgent need. To address software security, various approaches, techniques, methods, practices, and models have been proposed and developed. However, recent research shows that many software development methodologies do not explicitly include methods for incorporating software security during the development of software as it evolves from requirements engineering to its final disposal. The primary objective of this research is to study the state-of-the-art of security in the context of SDLC by following systematic mapping study (SMS). In the second phase, we will identify, through systematic literature review (SLR) and empirical study in the industry, the software security contributions, security challenges and their practices for global software development (GSD) vendors. The ultimate aim is to develop a Software Security Assurance Model (SSAM) to assist GSD vendor organisations in measuring their readiness towards the development of secure software.