Topics from networks management and operations

The IEEE/IFIP Network Operations and Management Symposium (NOMS) was established in 1988 and has been held in every even-numbered year since then; 2020 was the 32th anniversary of NOMS; and it was marked by the finalization of Horizon 2020 efforts—celebrating European innovation. Due to the pandemic, the conference had just virtual participation between 20 and 24 of April 2020. In spite of the challenging situation, we could happily celebrate the long and successful tradition of NOMS with record number of papers coming from five continents and approximately 50 countries. The exiting program of the conference included keynotes, panels, technical sessions, demo sessions, dissertation session, mini-conference sessions, poster sessions, tutorials, and workshops. These events provided rich opportunities for participants to learn, share, exchange, and identify the current and future directions and developments of network operations and management key issues. There was an excellent growth in recent years for the NOMS community. A lot of new and challenging research and developments have been on-going worldwide, including artificial intelligence, 5G networks, software-defined infrastructures, open source networking, IoT, and not to mention the fourth industrial revolution. We have set the main theme to “Management in the Age of Softwarization and Artificial Intelligence” for this year to emphasize the strong demands for softwarization, digitalization, and AI-driven management of the more and more complex network and service environments. The purpose of the special issue is to introduce some of the most interesting topics presented during the conference including highest ranked papers in terms of quality, impact, and their applicability to IJNM and wider audience. Finally, six papers were selected for publication presenting novel theoretical or experimentation results. The authors of these papers were given the time to extend and update their papers based on the review comments and suggestions provided. The first paper, “A Privacy-Focused Approach for Anomaly Detection in IoT Networks,” by Petro et al. analyzes anomaly detection mechanisms that focus on physical layer data and that rely on characterizing radio activity periods. Both classical and neural network proposed approaches were able to produce very good results in anomaly detection. Some models obtained a 99% detection rate, namely, density-based models like kernel density estimation (KDE). The best performing neural network approach was the combination of autoencoder (AE) and generative adversarial network (GAN). Utilizing these results, a framework is proposed to deploy the trained machine learning models into a production environment. The framework was designed to be scalable, modular, and able to handle retraining of the models. The second paper, “A Model-Driven Approach for Deployment Descriptor Design in NFV,” by Atoui et al. presents a configurable deployment descriptor model for network function virtualization (NFV). The model capitalizes on a catalog of deployment descriptors and helps service providers designing and generating NFV deployment descriptors. This model has better complexity than using directly the descriptors from the catalog with separate representations. Furthermore, a machine learning-based algorithm is proposed to automatically cluster similar descriptor elements based on a defined similarity distance metric. In sum, such an approach for supporting the design of deployment descriptor has a good accuracy of generating configuration guidelines. The third paper, “Analyzing Group Behavior Patterns in a Cellular Mobile Network for 5G Use-Cases,” by Soós et al. provides insights into user traffic patterns in mobile networks. The need for mobile broadband, machine-to-machine, and low-latency communication scenarios have already been there for legacy pre-5G networks, although their end-to-end servicing is usually executed as best effort. These scenarios will become the eMBB, mMTC, and URLLC use-cases when standalone 5G networks get configured to serve the associated service slices. This paper presents the features and traffic demand characteristics of the current end-devices from live mobile network including signaling patterns like mobility management, data patterns like sessions, and daily profiles. The fourth paper, “Industrial Control Protocols in the Internet Core: Dismantling Operational Practices,” by Nawrocki et al. analyzes the unprotected traffic of protocols that interconnect industrial control systems (ICS). The focus of the paper is on interdomain ICS traffic at an IXP and an ISP. The authors show how to create precise filters for potentially harmful nonindustrial ICS traffic and how to detect ICS sending unprotected interdomain ICS traffic, being vulnerable to eavesdropping and traffic manipulation attacks. The deployment status for ICS security protocols was analyzed in the Internet core. The first results suggest that (transport layer) ports registered for the security extensions experience substantially less traffic than other ports. That suggests a limited deployment of these security extensions. The fifth paper, “Characterising Attacks Targeting Low-Cost Routers: A MikroTik Case Study,” by Ceron et al. analyzes the problem of compromising low-cost routers including distributed denial-of-service (DDoS) attacks to ransomeware deployments. The particular focus is on MikroTik devices which are widely deployed in the Internet including Internet Exchange Infrastructures. Six realistic honeypots were deployed for 120 days around the world mimicking the characteristics of a MikroTik RouterOS discoverable over the Internet. In total, the authors identified 3441 attempts to exploit well-known vulnerabilities and about 200,000 IPs performing brute-force attacks on the honeypots. The protocol most commonly used for attacks detected was Point-to-Point Tunneling Protocol (PPTP). The sixth paper, “A Value-of-Information Based Management Framework for Fog Services,” by Poltronieri et al. presents a theoretical optimization framework for value-base service fabric management for Fog Computing. The paper shows that approaches leveraging value-of-information (VoI) concepts and tools are particularly interesting in management. Both genetic algorithms (GA) and quantum particle swarm optimization (QPSO) meta-heuristics can be used as optimization techniques for such a model. To overcome the complexity of the VoI model in experiments, a simplified user-specific VoI model, a distance-based heuristic, and their hybrid combination were compared by the authors showing that the hybrid model can be a good compromise in speed and performance, making the solution a suitable approach for online optimization of resources in Fog Computing.