A Review on Adversarial Poisoning Attacks on Machine Learning Models

A Review on Adversarial Poisoning Attacks on Machine Learning Models

Pooja Thakur 1, Prof. Sanmati Jain2

Research Scholar1, Asst. Professor and Head2

VITM, Indore, India1,2

Abstract: With the increasing popularity of neural networks for classification, the chances of poisoning attacks have also increased. One major area of research that has emerged is the detection of poisoning for android systems using neural networks due to the complexity of data set of attacks.  Several approaches have been used so far for the effective classification of poisoning attacks. The paper investigates the different contemporary neural network based approaches used thus far in the detection of poisoning attacks. The approaches used and their findings have been illustrated with their salient points. Moreover, an analysis in the form of shortcoming in previous work has been cited so as to define a problem statement to work upon.

Keywords: Manipulating Machine Learning, Poisoning Attacks, Neural Networks, Accuracy of classification