Protecting Data Integrity from Cyber Attacks with an Effective ML-Based Framework

In cybersecurity, machine learning is becoming more and more common. Machine learning has many potential applications in cybersecurity, one of which is to replace human malware detection methods. Making them more actionable, scalable, and effective is the aim. In the subject of cybersecurity, there are machine learning difficulties that require effective theoretical and systematic solutions. Numerous statistical and machine learning methods have proven effective in lessening the damage caused by cyberattacks. Some examples of these methods are Bayesian classification, deep learning, and support vector machines. In order to thwart such attacks, intelligent security system design must be able to spot previously unseen insights and patterns in network data. The next step in protecting against these types of attacks is to construct a data-driven machine learning model. Because of the increased development and deployment of complex analytics solutions, machine learning (ML) algorithms are being exploited by novel theft attempts to achieve a high success rate and do significant harm. Governments, organisations, and people should prioritise ML-based theft attacks due to the urgency and difficulty of detecting and protecting against them. This review covers the most up-to-date information on this emerging attack type and the defences put in place to stop it. This article examines the ML-based theft threat from the vantage point of three types of controlled information: authentication data, data pertaining to ML models, and data pertaining to restricted user behaviours. This paper reviews recent research in order to draw conclusions on the strengths and weaknesses of ML-based theft attacks, as well as their potential future applications. In addition, steps to build robust defences against discovery, disruption, and isolation are suggested.