  Deep learning is gaining importance in many applications. However, Neural
Networks face several security and privacy threats. This is particularly
significant in the scenario where Cloud infrastructures deploy a service with
Neural Network model at the back end. Here, an adversary can extract the Neural
Network parameters, infer the regularization hyperparameter, identify if a data
point was part of the training data, and generate effective transferable
adversarial examples to evade classifiers. This paper shows how a Neural
Network model is susceptible to timing side channel attack. In this paper, a
black box Neural Network extraction attack is proposed by exploiting the timing
side channels to infer the depth of the network. Although, constructing an
equivalent architecture is a complex search problem, it is shown how
Reinforcement Learning with knowledge distillation can effectively reduce the
search space to infer a target model. The proposed approach has been tested
with VGG architectures on CIFAR10 data set. It is observed that it is possible
to reconstruct substitute models with test accuracy close to the target models
and the proposed approach is scalable and independent of type of Neural Network
architectures.
