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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Indonesian Journal of Electrical Engineering and Computer Science
Gupta, Arpita
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Computer Science & IT

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Enhancing credit card security using RSA encryption and tokenization: a multi-module approach Saha, Mainak; Basu, M. Trinath; Gupta, Arpita; Ashrith, K.; Vardhan Reddy, Chevella Vamshi; Reddy, Shashanth; Reddy, Rohith
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i1.pp132-140

Abstract

The security of credit card information remains a critical challenge, with existing methods often falling short in safeguarding data integrity, confidentiality, and privacy. Traditional approaches frequently transmit sensitive information in unencrypted formats, exposing it to significant risks of unauthorized access and breaches. This study introduces a robust security framework that leverages Rivest-Shamir-Adleman (RSA) encryption and tokenization to protect credit card information during transactions. The proposed solution is structured into three key modules: merchant, tokenization, and token vault. The merchant module works in tandem with the tokenization module to generate transaction validation tokens and securely transmit credit card data. The token vault, maintained on a secure cloud storage platform, acts as a restricted-access database, ensuring that sensitive information is encrypted and inaccessible to unauthorized entities. Through this multi-layered approach, the study demonstrates a significant enhancement in the security of credit card transactions, effectively mitigating the risks of data breaches and unauthorized disclosures. The findings indicate that the proposed method not only addresses existing security vulnerabilities but also offers a scalable and efficient solution for protecting financial transactions.
A survey on ransomware detection using AI models Badrinath, Goteti; Gupta, Arpita
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp1085-1094

Abstract

Data centers and cloud environments are compromised as they are at great risk from ransomware attacks, which attack data integrity and security. Through this survey, we explore how AI, especially machine learning and deep learning (DL), is being used to improve ransomware detection capabilities. It classifies ransomware types, highlights active groups such as Akira, and evaluates new DL techniques effective at real-time data analysis and encryption handling. Feature extraction, selection methods, and essential parameters for effective detection, including accuracy, precision, recall, F1-score and receiver operating characteristic (ROC) curve, are identified. The findings point to the state of the art and the state of the art in AI based ransomware detection and underscore the need for robust, real-time models and collaborative research. The statistical and graphical analyses help researchers and practitioners understand existing trends and directions for future development of efficient ransomware detection systems to strengthen cybersecurity in data centers and cloud infrastructures.
UniMSE: a unified approach for multimodal sentiment analysis leveraging the CMU-MOSI Dataset Basu, Miriyala Trinath; Saha, Mainak; Gupta, Arpita; Hazra, Sumit; Fatima, Shahin; Sumalakshmi, Chundakath House; Shanvi, Nallagopu; Reddy, Nyalapatla Anush; Abhinav, Nallamalli Venkat; Hemanth, Koganti
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp2032-2042

Abstract

This paper explores multimodal sentiment analysis using the CMU-MOSI dataset to enhance emotion detection through a unified approach called UniMSE. Traditional sentiment analysis, often reliant on single modalities such as text, faces limitations in capturing complex emotional nuances. UniMSE overcomes these challenges by integrating text, audio, and visual cues, significantly improving sentiment classification accuracy. The study reviews key datasets and compares leading models, showcasing the strengths of multimodal approaches. UniMSE leverages task formalization, pre-trained modality fusion, and multimodal contrastive learning, achieving superior performance on widely used benchmarks like MOSI and MOSEI. Additionally, the paper addresses the difficulties in effectively fusing diverse modalities and interpreting non-verbal signals, including sarcasm and tone. Future research directions are proposed to further advance multimodal sentiment analysis, with potential applications in areas like social media monitoring and mental health assessment. This work highlights UniMSE's contribution to developing more empathetic artificial intelligence (AI) systems capable of understanding complex emotional expressions.
Co-Authors Abhinav, Nallamalli Venkat Ashrith, K. Badrinath, Goteti Basu, M. Trinath Basu, Miriyala Trinath Fatima, Shahin Hazra, Sumit Hemanth, Koganti Reddy, Nyalapatla Anush Reddy, Rohith Reddy, Shashanth Saha, Mainak Shanvi, Nallagopu Sumalakshmi, Chundakath House Vardhan Reddy, Chevella Vamshi