Conference Papers

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    Deep Learning Based LSTM and SeqToSeq Models to Detect Monsoon Spells of India
    (Springer Verlag service@springer.de, 2019) Saicharan, S.; Saha, M.; Mitra, P.; Nanjundiah, R.S.
    Monsoon spells are important climatic phenomenon modulating the quality and quantity of monsoon over a year. India being an agricultural country, identification of monsoon spells is extremely important to plan agricultural policies following the phases of monsoon to attain maximum productivity. Monsoon spells’ detection involve analyzing and predicting monsoon at daily levels which make it more challenging as daily-variability is higher as compared to monsoon over a month or an year. In this article, deep-learning based long short-term memory and sequence-to-sequence models are utilized to classify monsoon days, which are finally assembled to detect the spells. Dry and wet days are classified with precision of 0.95 and 0.87, respectively. Break spells are observed to be forecast with higher accuracy than the active spells. Additionally, sequence-to-sequence model is noted to perform superior to that of long-short term memory model. The proposed models also outperform traditional classification models for monsoon spell detection. © Springer Nature Switzerland AG 2019.
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    A hybrid model of convo-GAN to detect fake images
    (Grenze Scientific Society, 2021) Saha, S.; Rudra, B.
    With advancements in the field of Deep Learning, it has become easy to generate face swaps, thereby creating fake images which look extremely realistic, leaving few traces which cannot be detected by bare human eyes. Such images are known as ‘DeepFakes’ that can be used to create a ruckus and affect the quality of public discourse on sensitive issues, defame an individual’s profile, create political distress, blackmail a person or envision fake cyber terrorists. This paper proposes methods to detect fake images with the help of hybrid models having Convolutional Neural Network with Error Level Analysis, Gated Recurrent Unit neural network, Long Short Term Memory recurrent neural network and Generative Adversarial Network respectively. The 2019 ‘Real and Fake Face Detection’ dataset from Kaggle [7] is used to train the models and by experimentation we are able to prove that the combined model of Convolutional Neural Network and Generative Adversarial Network outperforms other models. © Grenze Scientific Society, 2021.
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    Evaluation of Recurrent Neural Networks for Detecting Injections in API Requests
    (Institute of Electrical and Electronics Engineers Inc., 2021) Reddy, S.A.; Rudra, B.
    Application programming interfaces (APIs) are a vital part of every online business. APIs are responsible for transferring data across systems within a company or to the users through the web or mobile applications. Security is a concern for any public-facing application. The objective of this study is to analyze incoming requests to a target API and flag any malicious activity. This paper proposes a solution using sequence models to identify whether or not an API request has SQL, XML, JSON, and other types of malicious injections. We also propose a novel heuristic procedure that minimizes the number of false positives. False positives are the valid API requests that are misclassified as malicious by the model. © 2021 IEEE.