Faculty Publications
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Item Batch verification of Digital Signatures: Approaches and challenges(Elsevier Ltd, 2017) Kittur, A.S.; Pais, A.R.Digital Signatures can be considered analogous to an ordinary handwritten signature for signing messages in the Digital world. Digital signature must be unique and exclusive for each signer. Multiple Digital Signatures signed by either single or multiple signers can be verified at once through Batch Verification. There are two main issues with respect to Batch Verification of Digital Signatures; first is the security problem and the second is the computational speed. Due to e-commerce proliferation, quick verification of Digital Signatures through specific hardware or efficient software becomes critical. Internet companies, banks, and other such organizations use Batch verification to accelerate verification of large number of Digital Signatures. Many Batch Verification techniques have been proposed for various Digital Signature algorithms. But most of them lack the security requirements such as signature authenticity, integrity, and non-repudiation. Hence there is a need for the study of batch verification of Digital Signatures. The main contributions of our survey include: (a) Identifying and categorizing various Batch verification techniques for RSA, DSS, and ECDSA(includes schemes based on Bilinear Pairing) (b) Providing a comparative analysis of these Batch Verification techniques (c) Identifying various research challenges in the area of Batch verification of signatures. © 2017 Elsevier LtdItem Randomised visual secret sharing scheme for grey-scale and colour images(Institution of Engineering and Technology journals@theiet.org, 2018) Mhala, N.C.; Jamal, R.; Pais, A.R.Randomised visual secret sharing is an encryption technique that utilises block-based progressive visual secret sharing and discrete cosine transform (DCT) based reversible data embedding technique to recover a secret image. The recovery method is based on progressive visual secret sharing, which recovers the secret image block by block. The existing block based schemes achieve the highest contrast level of 50% for noise-like and meaningful shares. The proposed scheme achieves a contrast level of 70-90% for noise-like and 70-80% for meaningful shares. The enhancement of contrast is achieved by embedding additional information in the shares using DCT-based reversible data embedding technique. Experimental results showed that the proposed scheme restores the secret image with better visual quality in terms of human visual system based parameters. © The Institution of Engineering and Technology 2017.Item Verifiable XOR-based visual secret sharing scheme for hyperspectral images(SPIE, 2021) Srujana, O.S.; Mhala, N.C.; Pais, A.R.Hyperspectral images (HSIs) are the spectral images that provide spatial and spectral information. Unlike multispectral images, these images consist of 100 to 200 bands, which provide a large amount of data to identify minute details of the scene with the help of the spectral signatures. This information is valuable and should be secured while transmitting the HSI over the network. Visual cryptography is a well-known cryptographic method for securing images. It helps in securely transmitting images among n users. It converts visual data into unreadable shares that are transmitted and, stacking these shares together will reveal the image. Many visual secret sharing (VSS) schemes have been proposed in the past decade, which made it easier to hide the visual information from unauthorized users. We have proposed a verifiable XOR-based VSS method for the secure transmission of HSIs. We have introduced a preprocessing step for the image with a band selection technique, which reduces the size of the image and eliminates redundancy. It also includes the detection of the tampered shares. Embedding a verifiable bit in each pixel is performed to test the integrity of the shares. We have assessed the visual quality of the recovered image using quantitative measuring parameters. We also compared them with existing VSS methods. The proposed method recovers the HSI with a self-similarity index of 95% to 99%. The proposed method's experimental results show that the HSI is restored with better visual quality. © 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).Item A novel fingerprint template protection and fingerprint authentication scheme using visual secret sharing and super-resolution(Springer, 2021) Muhammed, A.; Mhala, N.C.; Pais, A.R.Fingerprint is the most recommended and extensively practicing biometric trait for personal authentication. Most of the fingerprint authentication systems trust minutiae as the characteristic for authentication. These characteristics are preserved as fingerprint templates in the database. However, it is observed that the databases are not secure and can be negotiated. Recent studies reveal that, if a person’s minutiae points are dripped, fingerprint can be restored from these points. Similarly, if the fingerprint records are lost, it is a permanent damage. There is no mechanism to replace the fingerprint as it is part of the human body. Hence there is a necessity to secure the fingerprint template in the database. In this paper, we introduce a novel fingerprint template protection and fingerprint authentication scheme using visual secret sharing and super-resolution. During enrollment, a secret fingerprint image is encrypted into n shares. Each share is stored in a distinct database. During authentication, the shares are collected from various databases. The original secret fingerprint image is restored using a multiple image super-resolution procedure. The experimental results show that the reconstructed fingerprints are similar to the original fingerprints. The proposed method is robust, secure, and efficient in terms of fingerprint template protection and authentication. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.Item High-performance medical image secret sharing using super-resolution for CAD systems(Springer, 2022) Holla, M.R.; Pais, A.R.Visual Secret Sharing (VSS) is a field of Visual Cryptography (VC) in which the secret image (SI) is distributed to a certain number of participants in the form of different encrypted shares. The decryption then uses authorized shares in a pre-defined manner to obtain that secret information. Medical image secret sharing (MISS) is an emerging VSS field to address the performance challenges in sharing medical images, such as efficiency and effectiveness. Here, we propose a novel MISS for the histopathological medical images to achieve high performance in these two parameters. The novelty here is the Graphics Processing Unit (GPU) to exploit the data-parallelism in MISS during encryption and super-resolution (SR), supplementing effectiveness with efficiency. A Convolution Neural Network (CNN) for SR produces a high-contrast reconstructed image. We evaluate the presented model using standard objective assessment parameters and the Computer-Aided Diagnosis (CAD) systems. The result analysis confirmed the high-performance of the proposed MISS with a 98% SSIM of the deciphered image. Compared with the state-of-art deep learning models designed for the histopathological medical images, MISS outperformed with 99.71% accuracy. Also, we achieved a categorization precision that fits the CAD systems. We attained an overall speedup of 800 × over the sequential model. This speedup is significant compared to the speedups of the benchmark GPGPU-based medical image reconstruction models. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item A secure fingerprint template generation mechanism using visual secret sharing with inverse halftoning(Academic Press Inc., 2023) Muhammed, A.; Pais, A.R.Fingerprints are the most popular and widely practiced biometric trait for human recognition and authentication. Due to the wide approval, reliable fingerprint template generation and secure saving of the generated templates are highly vital. Since fingers are permanently connected to the human body, loss of fingerprint data is irreversible. Cancelable fingerprint templates are used to overcome this problem. This paper introduces a novel cancelable fingerprint template generation mechanism using Visual Secret Sharing (VSS), data embedding, inverse halftoning, and super-resolution. During the fingerprint template generation, VSS shares with some hidden information are formulated as the secure cancelable template. Before authentication, the secret fingerprint image is reconstructed back from the VSS shares. The experimental results show that the proposed cancelable templates are simple, secure, and fulfill all the properties of the ideal cancelable templates, such as security, accuracy, non-invertibility, diversity, and revocability. The experimental analysis shows that the reconstructed fingerprint images are similar to the original fingerprints in terms of visual parameters and matching error rates. © 2023 Elsevier Inc.Item Enhanced Malicious Traffic Detection in Encrypted Communication Using TLS Features and a Multi-class Classifier Ensemble(Springer, 2024) Kondaiah, C.; Pais, A.R.; Rao, R.S.The use of encryption for network communication leads to a significant challenge in identifying malicious traffic. The existing malicious traffic detection techniques fail to identify malicious traffic from the encrypted traffic without decryption. The current research focuses on feature extraction and malicious traffic classification from the encrypted network traffic without decryption. In this paper, we propose an ensemble model using Deep Learning (DL), Machine Learning (ML), and self-attention-based methods. Also, we propose novel TLS features extracted from the network and perform experimentation on the ensemble model. The experimental results demonstrated that the ML-based (RF, LGBM, XGB) ensemble model achieved a significant accuracy of 94.85% whereas the other ensemble model using RF, LSTM, and Bi-LSTM with self-attention technique achieved an accuracy of 96.71%. To evaluate the efficacy of our proposed models, we curated datasets encompassing both phishing, legitimate and malware websites, leveraging features extracted from TLS 1.2 and 1.3 traffic without decryption. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.Item GraPhish: A graph-based approach for phishing detection from encrypted TLS traffic(Elsevier Ltd, 2025) Manguli, K.; Kondaiah, C.; Pais, A.R.; Rao, R.S.Phishing has increased substantially over the last few years, with cybercriminals deceiving users via spurious websites or confusing mails to steal confidential data like username and password. Even with browser-integrated security indicators like HTTPS prefixes and padlock symbols, new phishing strategies have circumvented these security features. This paper proposes GraPhish, a novel graph-based phishing detection framework that leverages encrypted TLS traffic features. We constructed an in-house dataset and proposed an effective method for graph generation based solely on TLS-based features. Our model performs better than traditional machine learning algorithms. GraPhish achieved an accuracy of 94.82%, a precision of 96.28%, a recall of 92.11%, and an improved AUC-ROC score of 98.29%. © 2025 Elsevier Ltd