https://matjournals.net/engineering/index.php/JoCNSDC/issue/feed Journal of Cryptography and Network Security, Design and Codes 2024-12-23T10:31:09+00:00 Open Journal Systems <p><strong>JoCNSDC</strong> is a peer-reviewed journal in the field of Computer Science published by MAT Journals Pvt. Ltd. This is a print and e-journal dedicated to rapid publication of research papers based on all aspects of Cryptography and Coding, Privacy and Authenticity, Untraceability, Quantum Cryptography, Computational Intelligence in Security, Artificial Immune Systems, Biological and Evolutionary Computing, Reinforcement and Unsupervised Learning. It includes Autonomous Computing, Co-evolutionary Algorithms, Fuzzy Systems, Biometric Security, Trust Models and Metrics, Regulation, and Trust Mechanisms. Data Base Security, Network Security, Internet Security, Mobile Security, Security Agents, Protocols, Software Security Measures against Viruses and Hackers, Security and Privacy in Mobile Systems, Security and Privacy in Web Services, Service and Systems Design, and QOS Network Security are some areas that are covered under this journal title.</p> https://matjournals.net/engineering/index.php/JoCNSDC/article/view/868 Ransomware Attack Mitigation: Strategies and Best Practices 2024-08-27T11:44:15+00:00 Saladi Rudra Naga Prasanna Lakshmi prasannasaladi2004@gmail.com Manas Kumar Yogi manas.yogi@gmail.com <p>Ransomware attacks lock up your files and ask for money to unlock them. These attacks have become a significant problem for both people and companies. This study examines how we can effectively handle, stop, and prevent these attacks. We begin by explaining ransomware and how it impacts individuals and organizations. Then, we examine the current methods to detect and stop ransomware, such as checking for known malware patterns and monitoring unusual system behavior. We begin by explaining the ransomware and how it affects people. We then review current methods for spotting and preventing these attacks, such as checking for known threats and watching unusual activities. We propose a new method that uses real-time monitoring and automatic responses to detect and manage ransomware quickly. This method detects suspicious behavior early and isolates the affected parts of the system to reduce damage. Our results show that this new method works better than the existing methods, with fewer false alerts and faster responses. We also suggest areas for future research, such as new technologies and information-sharing, to improve security. In short, our method provides a practical way to handle ransomware attacks and improve overall security.</p> 2024-08-27T00:00:00+00:00 Copyright (c) 2024 Journal of Cryptography and Network Security, Design and Codes https://matjournals.net/engineering/index.php/JoCNSDC/article/view/940 Deep Insight: Enhancing Internet of Things (IoT) Security with Intrusive Deduction Systems (IDS) 2024-09-18T09:34:18+00:00 C. Kumuthini kumuthini@drngpasc.ac.in S. Latika kumuthini@drngpasc.ac.in S. Vasundraa kumuthini@drngpasc.ac.in <p>The Internet of Things (IoT) plays a vital role in recent technology by interconnecting various network devices that communicate with each other across the world via internet connections. These interconnected devices are embedded with multiple technologies, including hardware sensors, actuators, and software. Such devices allow for greater efficiency, convenience, and new possibilities in various aspects of daily life and industries like healthcare, transportation, and agriculture. Thus, IoT security is essential for protecting interconnected devices from cyber threats. As many devices are connected to the IoT system, the lack of security remains a primary task in preventing unauthorized access and data breaches. This paper uses Intrusion Detection Systems (IDS) powered by machine learning and deep learning to enhance IoT security. These systems monitor network traffic and device behavior to detect suspicious activities and threats, helping protect IoT systems from various attacks and unauthorized access. This work explores how IDS technology emphasizes the importance of security measures in safeguarding sensitive data and maintaining trust in IoT environments across different sectors.</p> 2024-09-18T00:00:00+00:00 Copyright (c) 2024 Journal of Cryptography and Network Security, Design and Codes https://matjournals.net/engineering/index.php/JoCNSDC/article/view/1252 Advancing Code Generation: Insights into Large Language Models 2024-12-23T10:31:09+00:00 Chetan Sandip Gadekar chetangadekar67@gmail.com Piyush Dnyaneshwar Ghanghav piyushghanghav@gmail.com Vaishnavi Fulchand Chemte vaishnavichemte30@gmail.com Anant Prabhakar Gagare anantgagare2002@gmail.com T Bhaskar bhaskarcomp@sanjivani.org.in <p>Large Language Models (LLMs) have demonstrated significant potential in automated code generation, aiding in tasks ranging from writing software prototypes to industrial control systems programming. These models offer unprecedented capabilities in generating and refining code. Yet, they face challenges like limited explainability, lack of execution guarantees, and the need for specialized support in niche domains such as Programmable Logic Controllers (PLCs). This paper comprehensively explores LLMbased tools across various fields, including scientific research and industrial applications. We propose novel frameworks like LLM4PLC, which integrates user feedback, external verification tools, and fine-tuning techniques to ensure the correctness and safety of generated code. Additionally, we investigate the role of LLMs in enhancing code understanding, demonstrating that embedding these models in developer environments can significantly improve productivity and task completion rates. Our findings highlight the promise and limitations of LLMs in code generation, with results showing substantial improvements when leveraging structured verification pipelines and context-aware tools.</p> 2024-12-23T00:00:00+00:00 Copyright (c) 2024 Journal of Cryptography and Network Security, Design and Codes https://matjournals.net/engineering/index.php/JoCNSDC/article/view/1240 Secure and Seamless: The Role of QoS in Safeguarding Modern Networks 2024-12-23T08:20:38+00:00 Ch Manikanta Kalyan manikantacm016@gmail.com Manas Kumar Yogi manas.yogi@gmail.com <p><em>Quality of Service (QoS) is a pivotal factor in modern network security, ensuring connected systems' seamless and reliable performance while safeguarding data integrity and confidentiality. With the proliferation of smart devices and complex infrastructures, maintaining QoS has become increasingly vital for secure communication across diverse network environments. This paper explores the role of QoS in mitigating cybersecurity threats, highlights its application in critical areas like IoT, cloud computing, and industrial networks, and discusses the challenges of balancing performance, scalability, and privacy. Emerging trends in AI-enhanced QoS, integration with next-generation networks such as 5G and 6G, and the potential impact of quantum computing are also examined, providing a comprehensive outlook on QoS’s future in network security.</em></p> 2024-12-23T00:00:00+00:00 Copyright (c) 2024 Journal of Cryptography and Network Security, Design and Codes https://matjournals.net/engineering/index.php/JoCNSDC/article/view/1241 Securing Networks: Machine Learning Models for Cyber Attack Identification 2024-12-23T08:22:43+00:00 Raghu Ram Chowdary Velevela vraghuram2021@gmail.com <p><em>The modern world increasingly depends on digital platforms for everyday life. As online activities grow, the digital space is being utilized more extensively. This expansion has led to a rise in cyber threats and crimes. "cyber hazard" refers to illegal activities carried out over the Internet. Over time, cybercriminals have devised more sophisticated techniques to breach security systems, and traditional methods often fail to detect complex or zero-day attacks. Machine learning has become a crucial tool for identifying and mitigating cyber risks. This paper examines several machine learning techniques to address some of the most significant cyber threats. Approaches such as deep belief networks, decision trees, and support vector machines are assessed for effectiveness. The study focuses on their performance in detecting spam, identifying intrusions, and recognizing malware using well-established benchmark datasets. While advancements in computing and communication technologies offer substantial benefits to individuals and organizations, they also introduce new challenges, particularly in securing sensitive data and ensuring its availability. Tackling these challenges is vital to minimizing digital threats. Cybersecurity concerns have prompted collaborative efforts among governments, organizations, and cybersecurity experts to address the rising digital threat. These threats can potentially undermine national security, leading to the development of Intrusion Detection Systems (IDS) to strategically combat and neutralize such attacks.</em></p> 2024-12-23T00:00:00+00:00 Copyright (c) 2024 Journal of Cryptography and Network Security, Design and Codes