Publications

2021

1. DiffAct: A Unifying Framework for Activation Functions

   Saha, Snehanshu, Pandey, Aditya, Mathur, Archana,  and Arun Kumar, Harshith

   In 2021 International Joint Conference on Neural Networks (IJCNN) 2021

   Abs

   The evolution of activation functions in deep neural networks is usually driven by fixed goals and incremental steps toward solving specific problems. We introduce differential equation activations (DiffAct), an umbrella of activation units to improve the process of learning activation functions in complex neural network structures on different data sets. Our stated objective is to enable a feed-forward neural network (FNN) to learn nonlinear activation functional forms from a family of solutions to an ordinary differential equation. In this paper, we also present new activation functions in parabolic forms, as offspring of the family of DiffActs. We demonstrate the effectiveness of the exercise of exploiting DiffAct based units in accomplishing comparable, sometimes superior, performance against single, fixed activation functions. The stated objective is to discover the internal dynamics of such activation functions via a common framework instead of producing State-of-the-Art (SOTA) results. The paper also hypothesizes on the ability to draw effective inferences on simple, 1-hidden layer architectures, wherever possible.

2020

1. Intrusion Detection Using a Hybrid Sequential Model

   Pandey, Aditya, Sinha, Abhishek,  and Aishwarya, P. S.

   In Advances in Machine Learning and Computational Intelligence 2020

   Abs

   A large amount of work has been done on the KDD 99 dataset, most of which include the use of a hybrid anomaly and misuse detection model done in parallel with each other. In order to further classify the intrusions, our approach to network intrusion detection includes the use of two different anomaly detection models followed by misuse detection applied to the combined output obtained from the previous step. The end goal of this is to verify the anomalies detected by the anomaly detection algorithm and clarify whether they are actually intrusions or random outliers from the trained normal (and thus to try and reduce the number of false positives). We aim to detect a pattern in this novel intrusion technique itself, and not the handling of such intrusions. The intrusions were detected to a very high degree of accuracy.