Browsing by Author "Sujan Reddy, A."

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Detection of injections in API requests using recurrent neural networks and transformers
(Inderscience Publishers, 2022) Sujan Reddy, A.; Rudra, B.
Application programming interfaces (APIs) are playing a vital role in every online business. The objective of this study is to analyse the incoming requests to a target API and flag any malicious activity. This paper proposes a solution based on sequence models and transformers for the identification of whether an API request has SQL injections, code injections, XSS attacks, operating system (OS) command injections, and other types of malicious injections or not. In this paper, we observe that transformers outperform B-RNNs in detecting malicious activity which is present in API requests. We also propose a novel heuristic procedure that minimises the number of false positives. We observe that the RoBERTa transformer outperforms and gives an accuracy of 100% on our dataset. We observe that the heuristic procedure works well in reducing the number of false positives when a large number of false positives exist in the predictions of the models. © © 2022 Inderscience Enterprises Ltd.
Ensemble Learning Approach for Short-term Energy Consumption Prediction
(Association for Computing Machinery, 2022) Sujan Reddy, A.; Akashdeep; Harshvardhan; Kamath Sâ€¤, S.
Predicting electricity consumption accurately is crucial for garnering insights and potential trends into energy consumption for effective resource management. Due to the linearity/non-linearity in usage patterns, electricity consumption prediction is challenging and cannot be adequately solved by using a single model. In this paper, we propose ensemble learning based approaches for short-term electricity consumption on an open dataset. The ensemble model is built on the combined predictions of supervised machine learning and deep learning base models. Experimental validation showed that the proposed ensemble model is more accurate and decreases the training time of the second layer of the ensemble by a factor close to ten, compared to the state-of-the-art. We observed a reduction of approximately 34% in the Root mean squared error for the same size of historical window. Â© 2022 Owner/Author.
Stacking Deep learning and Machine learning models for short-term energy consumption forecasting
(Elsevier Ltd, 2022) Sujan Reddy, A.; Akashdeep, S.; Harshvardhan, R.; Kamath S․, S.
Accurate prediction of electricity consumption is essential for providing actionable insights to decision-makers for managing volume and potential trends in future energy consumption for efficient resource management. A single model might not be sufficient to solve the challenges that result from linear and non-linear problems that occur in electricity consumption prediction. Moreover, these models cannot be applied in practice because they are either not interpretable or poorly generalized. In this paper, a stacking ensemble model for short-term electricity consumption is proposed. We experimented with machine learning and deep models like Random Forests, Long Short Term Memory, Deep Neural Networks, and Evolutionary Trees as our base models. Based on the experimental observations, two different ensemble models are proposed, where the predictions of the base models are combined using Gradient Boosting and Extreme Gradient Boosting (XGB). The proposed ensemble models were tested on a standard dataset that contains around 500,000 electricity consumption values, measured at periodic intervals, over the span of 9 years. Experimental validation revealed that the proposed ensemble model built on XGB reduces the training time of the second layer of the ensemble by a factor of close to 10 compared to the state-of-the-art, and also is more accurate. An average reduction of approximately 39% was observed in the Root mean square error. © 2022 Elsevier Ltd