Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Generalizing a Secure Framework for Domain Transfer Network for Face Anti-spoofing(Springer, 2023) Chandavarkar, B.R.; Rana, A.; Ketkar, M.M.; Pai, P.G.An essential field in cyber-security is the technology behind the authentication of users. In the contemporary era, alphanumeric passwords have been the primary tool used. A password is easy to understand but theoretically complex to brute force and is very easy to store in large databases since, in essence, they are an array of characters. In practical use, however, passwords tend to be very ineffective as the attributes that make for “strong†passwords are generally really abstract and random. However, human memories tend to remember constructed language, which can be intelligently guessed with sufficient information regarding the person. A widely accepted remedy to this problem is to replace passwords with face recognition software which involves minimal effort for the user and, though not completely immune, is quite challenging to misuse. Misuse is often done by spoofing faces. Spoofing faces means presenting a 2D or even a 3D copy of a face to the camera, pretending it is the real face. Many discovered spoofing methods are taken into consideration and protected against in most software. Even though various methods have been suggested for anti-spoofing, there are challenges that these methods go through, e.g., washed-out images, lack of colour, poor illumination. Our proposal involves the use of OpenCV and deep learning to accurately colourize the image pre-processing and put it through the GAN framework (Wang et al., From RGB to Depth: Domain Transfer Network for Face Anti-Spoofing (2021). https://ieeexplore.ieee.org/document/9507460. Accessed 07 Feb 2022). Furthermore, to secure the image to prevent ill use, the final generated image pair will be encrypted so that only the user can access it. The aim is to combine the power of re-colourization with the GAN framework’s strong anti-spoofing capabilities and make the whole framework secure for the user. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.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.