Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Deep Learning Techniques for Artistic Image Transformations: A Survey(Institute of Electrical and Electronics Engineers Inc., 2021) Aralikatti, R.C.; Sangeeth, S.V.; Chandavarkar, B.R.Deep learning has greatly revolutionized the ways in which computers tackle problems in vision, speech recognition, machine translation, etc., and has produced results which are almost inconceivable to conventional algorithms. Creative tasks such as fine arts and music composition, which were initially thought to be impossible to computers, are now possible. In this paper, we look at a particular class of problems called image-to-image translation problems and see how it can be leveraged to perform artistic image transformations. Generative Adversarial Networks (GANs) and related neural networks are particularly useful for this task. We explore some of the artistic image transformation tasks that deep learning can be used for and discuss the different machine learning architectures used, the results produced and the advancements made in literature towards tackling such tasks. © 2021 IEEE.Item Data Processing in IoT, Sensor to Cloud: Survey(Institute of Electrical and Electronics Engineers Inc., 2021) Sandeep, M.; Chandavarkar, B.R.IoT is connecting Things over the Internet and the realization of the environment through smart things to create a responsive space. Many surveys predicted the growth of IoT devices is going to be around 50 billion and an average of 7 devices per person. IoT has shown promising future with its applications like smart city, connected factories, buildings, roadways, smart health and many more. To make the promise a reality IoT has to overcome many hurdles like scalability, connectivity, architectural, big data, analysis, security, and privacy. In this literature survey, an attempt has been made to identify current challenges faced by IoT implementation and possible solutions, future opportunities, and research openings. Further, the processing of sensed data at IoT device, edge/fog layer, and the cloud is discussed in detail. © 2021 IEEE.Item Deep Learning based framework for dynamic Detection and Mitigation of ARP Spoofing attacks(Institute of Electrical and Electronics Engineers Inc., 2023) Puram, H.; Kumar, R.; Chandavarkar, B.R.Address Resolution Protocol (ARP) is a protocol that links the IP address of a network node to the Media Access Control (MAC) address of another node for communication. An attack known as ARP spoofing affects a network's data-link layer and permits malicious access to network data. The sending device can be tricked, and potentially valuable data can be stolen, by connecting the attacker's MAC address to the IP address of the receiving device. Several approaches exist today to detect ARP attacks accurately and efficiently but have drawbacks in various aspects such as speed of detection, accuracy, dynamicity, and scalability. To overcome these issues, we propose DL-ARP, a novel dynamic framework based on an XGBoost Classifier followed by a CNN-LSTM architecture. This technique can identify and mitigate ARP spoofing assaults in real-time by collecting packets of data as they are received. The model automatically categorizes them and creates entry cache logs in the process. This paper aims to show the effectiveness and the potential of the suggested methodology for real-time ARP spoofing detection and prevention, this study also assess the performance of the proposed methodology in comparison to other existing methods. © 2023 IEEE.