https://matjournals.net/engineering/index.php/JoSCNDS/issue/feed 2025-09-23T07:13:59+00:00 Open Journal Systems <p><strong>JoSCNDS</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 fundamental research papers on all areas of Security in Computer Networks and Distributed Systems. It is focused on the overall Network Securities such as-Firewall, System Intrusion Detection and Prevention, Access Control and Authorization, Authentication, Computer and Network Forensics, Cryptography, Emergency Management, Virus and Content Filtering, Identification, Authentication, Malware Detection, Encryption, File Type Filtering, URL Filtering, Data Loss Prevention (DLP), Intrusion Prevention Systems (IPS), Remote Access VPN, Hyperscale Network Security, Email Security, Cloud Security, IoT Security, Mobile Security. The main aim of JoSCNDS is to focus on Security Issues in Computer Networks and Distributed Systems, ranging from attacks to all kinds of solutions from prevention to detection approaches.</p> https://matjournals.net/engineering/index.php/JoSCNDS/article/view/2470 2025-09-23T07:13:59+00:00 Pandu Jayaram chandrasekhar.koppireddy@gmail.com Chandra Sekhar Koppireddy chandrasekhar.koppireddy@gmail.com K V V Subba Rao chandrasekhar.koppireddy@gmail.com

<p><em>Anomaly detection in health data streams is critical for early diagnosis, timely intervention, and effective healthcare management. This study presents a hybrid deep learning framework that combines Autoencoders (AEs) and Recurrent Neural Networks (RNNs) for robust and real-time anomaly detection in health-related time-series data. Autoencoders are employed to learn compact representations and reconstruct the input data, effectively capturing normal patterns while minimizing noise. RNNs, particularly Long Short-Term Memory (LSTM) networks, are integrated to model temporal dependencies and enhance sensitivity to irregularities across time. The reconstruction error from the autoencoder and prediction deviations from the RNN are jointly analyzed to detect anomalies. The proposed model is validated on benchmark physiological datasets, including heart rate, blood pressure, and ECG signals, demonstrating superior performance in identifying rare and subtle health events compared to traditional methods. This approach not only improves detection accuracy but also adapts dynamically to evolving patient conditions, making it suitable for continuous health monitoring in real-world settings. The model’s scalability and low false alarm rate highlight its potential for deployment in wearable devices and telemedicine applications. </em></p>

2025-09-23T00:00:00+00:00 Copyright (c) 2025 Journal of Security in Computer Networks and Distributed Systems