Saad Al-Ahmadi

Jikang Deng, Fizza Hassan, Hui Zhou et al.

Non-terrestrial network (NTN) is expected to be a critical component of Sixth Generation (6G) networks, providing ubiquitous services and enhancing the system resilience. However, the high-altitude operation and inherent mobility of NTN introduce significant challenges across the development and operations (DevOps) lifecycle. Apart from that, how to achieve artificial intelligence native (AI-Native) capabilities in NTN for intelligent network management and orchestration remains an important challenge. To solve the challenges above, we propose integrating the Open Radio Access Network (ORAN) with NTN as a promising solution, leveraging its principles of disaggregation, openness, virtualization, and embedded intelligence. Despite extensive technical literature on ORAN and NTN, respectively, there is a lack of a holistic view of the integration of ORAN and NTN architectures, particularly in terms of how intelligent ORAN can address the scalability challenge in NTN management. To address this gap, this paper provides a comprehensive and structured overview of an AI-native ORAN-based NTN framework to support dynamic configuration, scalability, and intelligent orchestration. The paper commences with an in-depth review of the existing literature from leading industry and academic institutions, subsequently providing the necessary background knowledge related to ORAN, NTN, and AI-Native for communication. Furthermore, the paper analyzes the unique DevOps challenges for NTN and proposes the orchestrated AI-Native ORAN-based NTN framework, with a detailed discussion on the key technological enablers within the framework. Finally, this paper presents various use cases and outlines the prospective research directions of this study in detail.

Lama Alsulaiman, Saad Al-Ahmadi

The nature of Wireless Sensor Networks (WSN) and the widespread of using WSN introduce many security threats and attacks. An effective Intrusion Detection System (IDS) should be used to detect attacks. Detecting such an attack is challenging, especially the detection of Denial of Service (DoS) attacks. Machine learning classification techniques have been used as an approach for DoS detection. This paper conducted an experiment using Waikato Environment for Knowledge Analysis (WEKA)to evaluate the efficiency of five machine learning algorithms for detecting flooding, grayhole, blackhole, and scheduling at DoS attacks in WSNs. The evaluation is based on a dataset, called WSN-DS. The results showed that the random forest classifier outperforms the other classifiers with an accuracy of 99.72%.