Method and system for secure online-learning against data poisoning attack

Abstract

This disclosure relates generally to online learning against data poisoning attack. Conventional methods used data sanitization techniques for online learning against data poisoning attack. However, these methods do not remove poisoned data points from training dataset completely. Embodiments of the present disclosure method provide an influence based defense method for secure online learning against data poisoning attack. The method initially filters a subset of poisoned data points in the training dataset for training a machine learning model using data sanitization technique. Further the method computes an influence of the data points and performs an influence minimization based on a predefined threshold. Updated data points for the learning model are generated and used for training the machine learning model. The disclosed method can be used against data poisoning attacks in applications such as spam filtering, malware detection, recommender system and so on.