Journal of Cryptography and Network Security, Design and Codes

An Investigative Study of Role of Blockchain in Restriction of Virus in Medical IoT: A Comprehensive Review

S. Kavya Sree, M. Pravalika, P. Devi Sravanthi, Manas Kumar Yogi — Sat, 15 Nov 2025 00:00:00 +0000

The rapid adoption of the Internet of Things (IoT) in healthcare has transformed medical monitoring, diagnostics, and patient care. However, the interconnected nature of Medical IoT (MIoT) systems exposes them to significant risks, including malware infiltration, data breaches, and virus propagation across devices. Traditional centralized security models often fail to provide adequate resilience against these threats due to single points of failure, scalability challenges, and privacy concerns. Blockchain technology, with its decentralized architecture, immutability, and cryptographic trust mechanisms, has emerged as a promising solution to mitigate these vulnerabilities. This study investigates the role of blockchain in restricting virus transmission within MIoT environments by integrating smart contracts, decentralized consensus mechanisms, and secure data-sharing protocols. A blockchain-enabled framework is proposed to enhance device authentication, ensure tamper-proof health records, and automate quarantine protocols in case of infectious outbreaks. This study demonstrates that integrating blockchain with MIoT enhances virus control efficiency by approximately 35% in simulated outbreak scenarios through automated quarantine and consensus-based authentication. Comparative analysis reveals a 47% improvement in tamper detection and a 28% reduction in unauthorized data access compared to centralized IoT frameworks. The proposed blockchain-enabled MIoT architecture featuring smart contracts, zero-knowledge proofs, and decentralized identity management achieves a scalable, privacy-preserving healthcare model. Furthermore, case studies from Estonia and South Korea illustrate the practical feasibility of blockchain-supported epidemiological systems. The study concludes that blockchain’s decentralization and cryptographic trust mechanisms are key to building resilient, virus-resistant healthcare infrastructures.

SQL Injection Attack Vulnerability Mitigation Method Using Keyword Classification and ANN-based Query Reconstruction Scheme

Kwang Ok Pak, Kwang Min Myong, Jin Sim Kim — Mon, 22 Dec 2025 00:00:00 +0000

Today, in the digital information era, the Internet and AI technology have changed the lives of people greatly. Traditional cyber-attack detection is no longer available in today’s complex network environment, and machine learning technology using artificial intelligence has started to play an important role in the network security field. In order to enhance the security of the web system, it is very urgent to establish a strong security policy to prevent the web-front-end script attack, such as cross-site scripting attack (XSS), and to prevent the web-server script attack, i.e., SQL injection attack. SQL injection attack is one of the most common application-layer attack techniques used today. Various methods to prevent modern SQL injection attacks have been investigated and introduced to ensure the safety and reliability of the system. SQL injection attacks occur due to insufficient testing of user queries at the client, and to prevent this, we have to apply a method that performs an accurate check of the input fields. The main challenge in preventing SQL injection attacks is to reduce the execution overhead and increase the detection rate for different attack types. We propose an attack detection method based on an ANN that enhances the detection rate against SQL injection attacks and provides maximum service for authenticated users, and we evaluate the security and performance of the system.

AeroSecure: Blockchain-federated Reinforcement Learning Framework for Autonomous Drone Swarm Coordination

V. Raghu Ram Chowdary — Mon, 22 Dec 2025 00:00:00 +0000

Autonomous aerial swarms are revolutionizing sectors such as defense, agriculture, and disaster response by enabling distributed and intelligent coordination. However, ensuring secure collaboration, real-time adaptability, and explainability among autonomous drones remains a significant challenge. This paper introduces AeroSecure, a novel Blockchain-Federated Reinforcement Learning (BFRL) framework for trustworthy drone swarm coordination. The proposed model employs Multi-Agent Reinforcement Learning (MARL) to optimize collective drone behaviors while preserving local autonomy. A permissioned blockchain ledger records mission data, model updates, and trust metrics, ensuring integrity and accountability. To enhance human trust and operational transparency, Explainable Reinforcement Learning (XRL) modules using SHAP and policy visualization provide interpretable insights into swarm decision logic. Simulation results on benchmark drone coordination tasks demonstrate that AeroSecure achieves superior performance in navigation efficiency, security, and interpretability compared to traditional centralized control systems. The framework paves the way for transparent, secure, and cooperative autonomous drone ecosystems.

Disrupting the Scam Cycle: AI for Safer Communications

Tue, 23 Dec 2025 00:00:00 +0000

Fraudulent phone calls are among the fastest-growing cybersecurity threats worldwide. In India alone, losses crossed 11,333 crore rupees in 2024. To address this, the paper introduces Live Scam Call Shield, a hybrid system that combines on-device machine learning with optional cloud support to deliver real-time scam detection while preserving user privacy. The system integrates directly into Android’s default dialer via native CallReceiver mechanisms, ensuring seamless operation without requiring users to switch apps or rely on external APIs during active calls. In testing, our model achieved 95.7% accuracy and a 94.8% F1-score on a dataset of 12,847 calls (85 hours of audio), with a median detection latency of just 4.2 seconds. A secure REST API backend supports encrypted model updates and crowdsourced feedback, while keeping privacy at the core. Field trials with 75 beta users across fraud-prone regions (Maharashtra, Delhi, and Karnataka) confirmed 96.2% precision with only 1.4 false positives, significantly reducing unnecessary alerts compared to existing solutions. Our findings show that privacy-first, on-device detection can rival cloud-based systems (95.7% vs. 99% accuracy) while offering offline resilience and greater user control—critical for regions with limited or unstable internet access. This work delivers the first production-ready solution that blends local intelligence with optional remote enhancement, setting a new benchmark for secure, privacy-preserving fraud detection in telephony.

Adaptation of Network-based Data for Intrusion Detection System

Fri, 26 Dec 2025 00:00:00 +0000

The increasing complexity of cyber threats requires robust intrusion detection systems (IDS) that leverage network-based data to detect and reduce malicious activities. With the growth in the size of computer networks and applications being developed, there is also an increase in the threat and damage that can be done as a result of these malicious activities. This research work looks at findings from scholarly journals to enable the adaptation and evaluation of network-based data for IDS. Based on a review of over 50 peer-reviewed research works, key methodologies, datasets (e.g., NSL-KDD, CICIDS2017, UNSW-NB15), and performance metrics (accuracy, precision, recall, F1-score) are analyzed, highlighting the dominance of machine learning and deep learning techniques in IDS development. In addition, research gaps were uncovered, particularly the difficulty in finding comprehensive and valid datasets that can be tested and evaluated for intrusion detection, limited focus on zero-day attacks, scalability in high-speed networks, and a proper explanation of models. A very critical gap is the scarcity of comprehensive, context-aware datasets, particularly for regions where traffic is dominated by mobile technology and localized threats (e.g., phishing, mobile banking fraud). This gap hinders the accurate deployment and evaluation of IDS. To address this, the study proposes generating benign and attack-specific network flows using synthetic data generated in a network environment and evaluating their performance for IDS. Recommendations include developing lightweight preprocessing frameworks and incorporating specific attack patterns to enhance IDS effectiveness.