Abstract
Understanding network workload through the characterization of network flows, being essential for assisting network management tasks, can benefit largely from traffic sampling as long as an accurate snapshot of network behavior is captured. This paper is devoted to evaluate the real applicability of using sampling to support flow analysis. Considering both classical and emerging sampling techniques, a comparative performance study is carried out to assess the accuracy of estimating flow parameters through sampling. After identifying the main building blocks of sampled-based measurements, a sampling framework has been implemented to provide a versatile and fair platform for carrying out the testing and comparison process. Through an encompassing coverage of representative sampling techniques, the present study aims to provide useful insights regarding the use of sampling in traffic flow analysis.

Abstract
Monitoring current communication networks and services is an increasingly complex task as a result of a growth in the number and variety of components involved. Moreover, different perspectives on network monitoring and optimisation policies must be considered to meet context-dependent monitoring requirements. To face these demanding expectations, this article proposes a semantic-based approach to support the flexible configuration of context-aware network monitoring, where traffic sampling is used to improve efficiency. Thus, a semantic layer is proposed to provide with a standard and interoperable description of the elements, requirements and relevant features in the monitoring domain. On top of this description, semantic rules are applied to make decisions regarding monitoring and auditing policies in a proactive and context-aware manner. Use cases focusing on traffic accounting and traffic classification as monitoring tasks are also provided, demonstrating the expressiveness of the ontology and the contribution of smart SWRL rules for recommending optimised configuration profiles.

Abstract
In Wireless Sensor Networks (WSN), typically composed of nodes with resource constraints, leveraging efficient processes is crucial to enhance the network lifetime and, consequently, the sustainability in ultra-dense and heterogeneous environments, such as smart cities. Particularly, balancing the energy required to transport data efficiently across such dynamic environments poses significant challenges to routing protocol design and operation, being the trade-off of reducing data redundancy while achieving an acceptable delivery rate a fundamental research topic. In this way, this work proposes a new energy-aware epidemic protocol that uses the current state of the network energy to create a dynamic distribution topology by self-adjusting each node forwarding behavior as eager or lazy according to the local residual battery. Simulated evaluations demonstrate its efficiency in energy consumption, delivery rate, and reduced computational burden when compared with classical gossip protocols as well as with a directional protocol.

Abstract
This work studies Tor, an anonymous overlay network used to browse the Internet. Apart from its main purpose, this open-source project has gained popularity mainly because it does not hide its implementation. In this way, researchers and security experts can fully examine and confirm its security requirements. Its ease of use has attracted all kinds of people, including ordinary citizens who want to avoid being profiled for targeted advertisements or circumvent censorship, corporations who do not want to reveal information to their competitors, and government intelligence agencies who need to do operations on the Internet without being noticed. In opposition, an anonymous system like this represents a good testbed for attackers, because their actions are naturally untraceable. In this work, the characteristics of Tor traffic are studied in detail in order to devise an inspection methodology able to improve Tor detection. In particular, this methodology considers as new inputs the observer position in the network, the portion of traffic it can monitor, and particularities of the Tor browser for helping in the detection process. In addition, a set of Snort rules were developed as a proof-of-concept for the proposed Tor detection approach. © Springer Nature Switzerland AG 2020.

Abstract
The path towards the United Nations objective of providing legal identity for all, including free birth registrations, has been facing several challenges. Particularly, the diversity of social realities, limited ICT infrastructures, inadequate legal frameworks, and unstable political engagement have resulted in solutions highly fitted to a specific scenario, thus hard to be replicated in different regions. Paired with noncomprehensive public services of civil registration, these aspects impact the way identity records are created, stored and used by citizens in their daily interactions. To tackle these impairments, this work introduces IDINA, a non-authoritative approach aiming at a community-oriented identification system underpinned by relations of social trust, inclusiveness, and the use of cutting-edge accessible technologies. © 2021 Owner/Author.

Abstract
Packet sampling plays an important role in keeping storage and processing requirements at a manageable level in network management. However, because it reduces the amount of available information, it can also reduce the performance of some related tasks, such as detecting security events. In this context, this work explores how packet sampling impacts machine learning-based tasks, in particular, flow-based C2 TLS malware traffic detection using a deep neural network. Based on a proposed lightweight sampling scheme, the ongoing results show a small reduction in classification accuracy compared with analysing all the traffic, while reducing in 10 fold the number of packets processed.