A Detailed Review and Study of Implementation of Security System Design for Machine Learning-Based Software Systems: Issues And Challenges

The rapidly growing technology specially in the field of software, Machine Learning (ML) has played an important role in a range of tasks, including voice, video, and computer vision. It is currently being utilised in software systems to automate the crucial processes more and more. Machine learning-based modern software systems (MLBSS) are currently difficult to build safely, which will severely limit the uses in security and safety-critical domains. Recently, majority of articles are published and still research work is going on the safety problems for ML and Deep Learning (DL), which place a strong prominence on the models and data both, adversaries’ threats have been taken into consideration. In this paper, we address the prospect that system bugs or external adversarial assaults might lead to security vulnerabilities for machine learning-based software systems, and we propose that safe development techniques ought to be applied throughout the whole lifecycle. We conclude by providing a thorough study of the security for MLBSS, which includes a comprehensive analysis based on a review of the structure of three distinctive features in terms of security issues. The entire state-of-the-art for MLBSS secure development is also provided.