Laila M. Halman, Mohammed J. F. Alenazi^*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1469-1483, 2024, DOI:10.32604/cmes.2023.028077 - 17 November 2023

Abstract The healthcare sector holds valuable and sensitive data. The amount of this data and the need to handle, exchange, and protect it, has been increasing at a fast pace. Due to their nature, software-defined networks (SDNs) are widely used in healthcare systems, as they ensure effective resource utilization, safety, great network management, and monitoring. In this sector, due to the value of the data, SDNs face a major challenge posed by a wide range of attacks, such as distributed denial of service (DDoS) and probe attacks. These attacks reduce network performance, causing the degradation of… More > Graphic Abstract

M. Manikandan^1,*, S. Sakthivel², V. Vivekanandhan¹

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3169-3185, 2023, DOI:10.32604/iasc.2023.029232 - 17 August 2022

Abstract Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed network. This closed network is intended to share sensitive location-centric information from a source node to the base station through efficient routing mechanisms. The efficiency of the sensor node is energy bounded, acts as a concentrated area for most researchers to offer a solution for the early draining power of sensors. Network management plays a significant role in wireless sensor networks, which was obsessed with the factors like the reliability of the network, resource management, energy-efficient routing, and scalability… More >

Haiwen Chen¹, Guang Yu¹, Fang Liu², Zhiping Cai^{1, *}, Anfeng Liu³, Shuhui Chen¹, Hongbin Huang¹, Chak Fong Cheang⁴

CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 917-927, 2020, DOI:10.32604/cmc.2020.05981

Abstract For many Internet companies, a huge amount of KPIs (e.g., server CPU usage, network usage, business monitoring data) will be generated every day. How to closely monitor various KPIs, and then quickly and accurately detect anomalies in such huge data for troubleshooting and recovering business is a great challenge, especially for unlabeled data. The generated KPIs can be detected by supervised learning with labeled data, but the current problem is that most KPIs are unlabeled. That is a time-consuming and laborious work to label anomaly for company engineers. Build an unsupervised model to detect unlabeled More >