Graph Neural Networks (GNNs) have gained significant attention for multivariate time series analysis in recent years. However, applying them to real-world networking data introduces several key challenges, particularly in designing meaningful and effective graph structures. In this paper, we propose a novel method for constructing initial graphs tailored for time series anomaly detection in complex network environments. Our approach, called COSI (COrrelation SImilarity), leverages two fundamental properties of real-world data: feature name semantics and statistical correlation. By combining natural language processing (NLP) with correlation analysis, COSI produces graph structures that significantly enhance GNN model performance for anomaly detection tasks, outperforming conventional graph construction methods in almost all evaluation scores across all datasets. We extensively evaluate COSI on three datasets, including two real-world networking datasets and one widely used benchmark dataset, and we open source the implementation to encourage reproducibility, further research, and practical adoption by the community.

Applying Federated Learning (FL) to real-world network environments, such as telecommunication networks, presents significant challenges. In this paper, we investigate the intrinsic problem of non-IID data training across distributed innetwork FL clients. More specifically, we focus on the feature distribution discrepancy in the use of in-network FL for infrastructure monitoring. While previous works have made notable progress in the design of aggregation functions that compensate strong polarization in data distributions, limited attention was directed toward data load-balancing from sources to processing nodes, which leverages the role and characteristics of the data itself. Our goal is to address feature heterogeneity in in-network federated learning by piloting how data is forwarded in the network on the way to its consumers. To this end, we design and evaluate several scenarios for dynamic data load balancing between FL clients within the network topology, subject to latency constraints.

In this paper, we address the challenge of anomaly detection in multivariate time series data related to environments requiring stringent guarantees, notably in 5G and beyond 5G systems promising high reliability and quality. Traditional approaches to anomaly detection, including supervised, unsuper-vised, and self-supervised methods often struggle with the diver-sity and unpredictability of real-world anomalies. To overcome these limitations, we propose a novel dual forecasting model approach, DREAM (Dual foREcAsting Model Anomaly Detection), which leverages both normal and unlabeled data to enhance detection accuracy. Our approach involves training two distinct models: one on normal behavior and the other on mixed behavior, and then comparing their outputs to identify anomalies. We also introduce new evaluation method, addressing the shortcomings of traditional point-wise evaluation. Our experiments, with multiple networked system datasets, demonstrate that DREAM outper-forms traditional forecasting-based approaches, including when used in a hybrid manner with traditional forecasting algorithms.

The rise of xG networks has brought unprecedented capabilities in wireless communication, but the complexity of 5G and beyond 5G networks introduces challenges in ensuring reliability and performance. In this article, we propose a new approach to network anomaly detection by leveraging Graph Neural Networks (GNNs) and graph structure learning. GNNs are well-suited for capturing relationships in graph-structured data, making them an effective tool for detecting complex patterns in network behavior. We introduce a novel graph structure extracting feature semantics and demonstrate the effectiveness of GNNs in network anomaly detection. We show we can improve the accuracy up to 4% thanks to the proposed graph structure. The source code is available at https://github.com/Killian-cressant/graph4_anomaly_detection.

Virtual Machine (VM) migration presents several advantages for both cloud operators and cloud users. The benefits of VM migration are amplified in the context of Joint-clouds, in which multiple clouds owned by distinct operators exchange VMs. However, inter-cloud VM migrations might require the usage of the Internet, which creates the possibility of external threats and therefore, a risk to the VMs' integrity. Thus, we propose a novel approach to secure VM migration for Joint-clouds. We consider both local migration and inter-cloud migration. Our solution is based on a multi-blockchain system, in which each cloud provider participates in the maintenance of a consortium blockchain to store relevant information about the migrations. In addition, each cloud operator might choose to keep a local blockchain to secure its local VM migrations. Therefore, inter-blockchain communication plays an important role in our work.

The paradigm of Internet of Vehicles (IoV) as an extension to the Internet of Things (IoT) concept can assist the development of smart cities. IoV relies on vehicular communications to allow vehicles to communicate in real time with other vehicles, roadside infrastructure, and pedestrians. The main goal is to enable not only road safety services but also time-constrained IoT applications. In this context, Cellular Vehicle-to-Everything (C-V2X) communication is a prominent technology to accomplish IoV goals. Currently, there are two versions of C-V2X available, long-term evolution-V2X (LTE-V2X) and new radio-V2X (NR-V2X), and in both, mechanisms of resource allocation (RA) play an important role in their performance. Therefore, our goal in this article is to present an extensive state of the art of RA on C-V2X technology. Thus, we first present the main technical aspect related to LTE-V2X and NR-V2X technologies. Afterward, we present and categorize the related works for both technologies, pointing out their main differences, advantages, and drawbacks. Finally, we present the challenges, open issues, and future technical trends related to RA in C-V2X.

In recent years many changes have taken place, such as increasingly powerful smartphones and cellular network allowing broadband access, and is expected in the coming years an increase in live streaming data traffic for mobile devices. On the other hand, it is common to find in the literature criticism of the traditional client-server model, that the Internet was not designed to support multimedia applications, or even that mobile network isn't appropriated to streaming, despite the fact that video streaming works and is a very popular Internet application. This work proposes, from the log files of a large CDN, discuss the influence of impact factors in the quality of the users' transmissions using mobile devices in popular live video transmissions. Using association rule, a data mining technique, this paper aims to analyze popular live streaming sessions to understand what factors may impact the broadcasts.

IoT solutions, in order to reduce power consumption, assume severe constraints on the transmission rate, which limit their use to applications with small amounts of collected data per device. Thus, these solutions are not suitable for monitoring wild animals, which requires transmitting large amounts of photos captured by cameras, installed in the middle of the forest. In addition, the forest environment also impairs the transmission capacity. Therefore, this work proposes collecting data from devices in the forest with the aid of unmanned aerial vehicles - drones. Hence, this paper aims at evaluating the performance of a wireless network between the device attached to the drone and the cameras. To achieve our goal, we evaluate traditional network metrics, such as maximum range, transmission rate, and packet loss. To compare and better characterize the transmission in the forest, we also assess the performance of those devices in different scenarios: indoor, outdoor, and inside the forest. The most important result is the feasibility of using low-complexity IoT devices for collection large amounts of data in forest applications.

Media streaming is one of the key applications on the Internet, with mobile devices becoming each day more powerful and popular. Although mobile users have experienced a large improvement in wireless access networks in the last years, live-streaming videos still face several challenges, especially for large-scale popular events. Therefore, understanding live streaming for mobile users becomes imperative nowadays. This paper targets at the characterization of mobile user behaviors, The main goal of this paper is to understand the behavior of mobile users when watching large popular live events in Brazil, such as 2016 former Brazilian president Impeachment and 2016 Opening Ceremony for Rio Olympic Games. We focus our analysis on comprehending the influence of interruptions on the session duration and new attempts to rejoin the video transmission. The results show that a significant number of mobile users experiences low transmission rates and the type of event imply different user behaviors.

In this paper we present a new routing paradigm for wireless multihop networks. In plasma routing, each packet is delivered over the best available path to one of the gateways. The choice of the path and gateway for each packet is not made beforehand by the source node, but rather on-the-fly by the mesh routers as the packet traverses the network. We propose a distributed routing algorithm to jointly optimize the transmission rate and the set of gateways each node should use. A load balancing technique is also proposed to disperse the network traffic among multiple gateways. We validate our proposal with simulations and show that plasma routing outperforms the state-of-the-art multirate anypath routing paradigm, with a 98% throughput gain and a 2.2x delay decrease. Finally, we also show that the load can be evenly distributed among gateways with a similar routing cost, resulting in a further 63% throughput gain.

The Bloom filter is a space-efficient data structure often employed in distributed applications to save bandwidth during data exchange. These savings, however, come at the cost of errors in the shared data, which are usually assumed low enough to not disrupt the application. We argue that this assumption does not hold in a more hostile environment, such as the Internet, where attackers can send a carefully crafted Bloom filter in order to break the application. In this paper, we propose the concatenated Bloom filter (CBF), a robust Bloom filter that prevents the attacker from interfering on the shared information, protecting the application data while still providing space efficiency. Instead of using a single large filter, the CBF concatenates small subfilters to improve both the filter robustness and capacity. We propose three CBF variants and provide analytical results that show the efficacy of the CBF for different scenarios. We also evaluate the performance of our filter in an IP traceback application and simulation results confirm the effectiveness of the proposed mechanism in the face of attackers.

In this paper, we propose a human-based model which builds a trust relationship between nodes in an ad hoc network. The trust is based on previous individual experiences and on the recommendations of others. We present the Recommendation Exchange Protocol (REP) which allows nodes to exchange recommendations about their neighbors. Our proposal does not require disseminating the trust information over the entire network. Instead, nodes only need to keep and exchange trust information about nodes within the radio range. Without the need for a global trust knowledge, our proposal scales well for large networks while still reducing the number of exchanged messages and therefore the energy consumption. In addition, we mitigate the effect of colluding attacks composed of liars in the network. A key concept we introduce is the relationship maturity, which allows nodes to improve the efficiency of the proposed model for mobile scenarios. We show the correctness of our model in a single-hop network through simulations. We also extend the analysis to mobile multihop networks, showing the benefits of the maturity relationship concept. We evaluate the impact of malicious nodes that send false recommendations to degrade the efficiency of the trust model. At last, we analyze the performance of the REP protocol and show its scalability. We show that our implementation of REP can significantly reduce the number messages.

Slander attacks represent a significant danger to distributed reputation systems. Malicious nodes may collude to lie about the reputation of a particular neighbor and cause serious damage to the overall trust evaluation system. This paper presents and analyzes a trust model robust to slander attacks in ad hoc networks. We provide nodes with a mechanism to build a trust relationship with its neighbors. The proposed model considers the recommendation of trustworthy neighbors and the previous experiences of the node itself. The interactions are limited to direct neighbors in order to scale on mobile networks. The results show the impact of slander attacks to our trust model. We analyze how the main parameters affect the trust evaluation process under a lying collusion attack. We show that our trust model tolerate almost 40% of liars.

This paper analyzes a trust model for mobile ad hoc networks. We provide nodes with a mechanism to build a trust relationship with its neighbors. The proposed model considers the recommendation of trustworthy neighbors and the experience of the node itself. The interactions are limited to direct neighbors in order to scale on mobile networks. The results show the efficiency and the trade-off of our model in the presence of mobility. We also analyze the advantages of considering the relationship maturity, i.e. for how long nodes know each other, to evaluate the trust level. The maturity parameter can decrease the trust level error up to 50%.

The current Internet architecture allows malicious nodes to disguise their origin during denial-of-service attacks with IP spoofing. A well-known solution to identify these nodes is IP traceback. In this paper, we introduce and analyze a lightweight single-packet IP traceback system that does not store any data in the network core. The proposed system relies on a novel data structure called Generalized Bloom Filter, which is tamper resistant. In addition, an efficient improved path reconstruction procedure is introduced and evaluated. Analytical and simulation results are presented to show the effectiveness of the proposed scheme. The simulations are performed in an Internet-based scenario and the results show that the proposed system locates the real attack path with high accuracy.