https://matjournals.net/engineering/index.php/JCSCS <p><strong>JCSCS</strong> is a peer reviewed journal of Computer Science domain published by MAT Journals Pvt. Ltd. It is a print and e-journal focused towards the rapid publication of research and review papers based on Protecting Systems, Networks, and Programs from Digital Attacks. This journal focuses on all aspects of Cyber Security including IoT Security, AI Security, Machine Learning in Security, Security and Crime Science, Cryptography and its Applications, Security Economics, Human Factors and Psychology, Legal Aspects of Information Security, Privacy, Hardware Security, Software Security and System Security, Network and Critical Infrastructure Security, Data-Driven Security and Measurement Studies, Adversarial Reasoning, Malware Analysis, Privacy-Enhancing Technologies and Anonymity, Big Data Security and Privacy, Cloud Security, Digital and Information Forensics, Quantum Security, Cryptography and Cryptology, Authentication and Access Control and Biometrics.</p> en-US Journal of Cyber Security in Computer System https://matjournals.net/engineering/index.php/JCSCS/article/view/4066 <p><em>Artificial Intelligence (AI) technology plays a significant role in the development of various solutions to address complex challenges in various sectors such as healthcare, banking, logistics, transport, and mobile computing, among others. Current artificial intelligence technologies rely on extensive amounts of data for the effective training of machine learning algorithms. Usually, data for training machine learning algorithms has been collected on a central server that performs the training of machine learning algorithms. However, collecting and storing data on the central server poses major threats to data privacy, security breaches, and legal obligations, especially for personal and organizational data that require protection. As such, in order to mitigate the above challenges, a new paradigm in machine learning algorithms referred to as federated learning has been developed. Under federated learning, machine learning models are usually trained at the client-side, which could include a smartphone, edge devices, or an organizational server. In this case, the client device would not transmit actual data to the central server but the parameters of the machine learning algorithms instead. Here, the central server would use different aggregation algorithms in the combination of the parameters of the machine learning models from multiple client devices. Communication cost would also be lowered because model parameters would only be communicated to the central server instead of actual data. More importantly, federated learning enables organizations to collaborate in building intelligent machines without violating any privacy and data protection rules.</em></p> Jagu Varalakshmi Madem Meghana P. Devi Sravanthi T. Jagadeesh Copyright (c) 2026 Journal of Cyber Security in Computer System 2026-09-03 2026-09-03 1 13 https://matjournals.net/engineering/index.php/JCSCS/article/view/4079 <p><em>Prompt engineering has emerged as a critical discipline for effectively utilizing Large Language Models (LLMs) across software engineering tasks, including code generation, debugging, documentation, and domain-specific language development. However, the rapid proliferation of prompting techniques has created a fragmented landscape, making it challenging for practitioners to identify suitable approaches for their specific requirements. This survey provides a comprehensive review and comparative analysis of contemporary prompt engineering methodologies in software engineering, examining eight representative studies across three key dimensions: prompt optimization techniques, evaluation frameworks, and domain-specific applications. They systematically analyze each approach based on its methodology, key contributions, and inherent limitations. Our analysis reveals several persistent challenges, including domain specificity, lack of interpretable evaluation feedback, reliance on single datasets, and fragmentation of complementary techniques. Based on these findings, critical research gaps were identified, and an integrated framework was proposed that combines intent extraction, few-shot prompting, and prompt chaining in a unified modular pipeline designed to overcome the limitations identified in existing approaches. This paper serves as both a comprehensive reference for researchers and practitioners in prompt engineering and a foundation for developing more accessible, interpretable, and generalizable LLM interaction tools for software engineering applications.</em></p> Praphthi H. P Ramya B. P Ranjan Prajwal Sanjana B Janaki K. B Copyright (c) 2026 Journal of Cyber Security in Computer System 2026-09-07 2026-09-07 14 26