In service-oriented environments and distributed systems, service composition allows simple services to be dynamically combined into new, more complex services. Service composition techniques are usually designed as an extension to service discovery. Traditional techniques try to match a user’s requirements, often complex, with the available services. However, one-to-one matching is inefficient; it is preferable to meet the request from available services even when one of the basic services is not present. Separating composition and discovery has also led to inefficiency, especially in a highly dynamic environment. With the heterogeneity of networks, users, and applications having multiple sources, constructing service-specific overlays in large distributed networks is challenging. In this article, we propose a new service composition algorithm to deal with the problem of composing multiple autonomic elements to achieve system-wide goals. Using a self-organizing approach, autonomic entities are dynamically and seamlessly composed into service-specific overlay networks. The algorithm combines composition and service discovery into one step, thereby achieving more efficiency and less latency. The decentralized and self-organizing nature of the algorithm allows it to respond rapidly to system changes. Extensive simulation results validate the effectiveness of the approach when it is compared to other solutions.

Cybersecurity is challenging for security guards because of the rising quantity, variety, and frequency of attacks and malicious activities in cyberspace. Intrusion attacks are among the most common types of cyberspace attacks. Therefore, an intrusion detection system (IDS) is in high demand to accurately detect and mitigate their impact. In this paper, an anomaly IDS using machine learning and information gain-rank (IG-R) is proposed to improve the detection accuracy of intrusions. The network security lab-knowledge discovery dataset (NSL-KDD) is used to train and test the proposed IDS. Initially, the information gain (IG) algorithm and Ranker are used to evaluate, rank and reduce the number of selected instances from 41 instances to only 6 instances. Furthermore, many classifiers have been tested and evaluated; such as adaptive boosting (AdaBoostM1), random forest, J48, and naïve Bayes to choose the best performance classifier to be used in the detection process. After applying the IG-R and testing the suggested classifiers, the results showed that the random forest classifier has the best performance over the tested classifiers with TPR, FPR, and accuracy of 99.7%, 0.3%, and 99.7%, respectively, and is recommended to be used in the detection process.

The Sixth Generation (6G) network will rely on the integration and cooperation of a plethora of communication and networking systems. Space-air-ground integrated networks (SA-GIN) which integrate satellites, aerial networks, and terrestrial communication, can provide seamless and continuous delivery of services and applications with high levels of performance. While SAGIN provides promising solutions for data collection and communication, it poses security concerns given that it may allow untrusted devices to be part of the cooperative system. In this paper we introduce a blockchain-enabled SAGIN framework that authenticates both devices and the data collected by SAGIN participants. Federated Learning (FL) is supported by the framework to enable network distribution and accurate real-time data analysis. As a proof-of-concept, the solution considers a use-case for earthquake event prediction and monitoring. Satellite sensors are used to collect data and identify the presence of thermal anomalies used for earthquake forecasting. Furthermore, a post-earthquake monitoring technique is used which relies on Unmanned Aerial Vehicles (UAVs) to provide access to remote places in order to collect high quality images. The collected images are then processed locally on UAV swarms or through edge devices for real-time decision making. An experimental study is conducted on models and approaches needed to increase prediction accuracy for earthquake events.