A Data Driven Model for Identifying Potential Poisoning Attacks on Machine Learning Model

Due to the largeness of data to be analysed by online platforms, most of the big data models leverage machine learning in the backend. This has led to a new type of attack by users termed as adversarial machine learning in which the machine learning model used in the backend is targeted rather than the from end or the APIs. In conventional attacks, the first line of attack is the front end of the software. In this case, the machine learning model used in the backend is fed with bogus and/or deliberately falsified data to make it inactive. This is termed as adversarial machine learning attack or adversarial cyber-attack. It is extremely challenging to detect such attacks as there are no clear signs of attacks such as redirections, malicious code scripts, auto refresh tags etc. Instead, the data fed to the back-end machine learning model is targeted using adversarial data feeds. In this paper, a deep learning based model is used to detect such attacks. The proposed approach attains a precision value of 81% which is significantly higher compared to existing work.