Deep learning (DL) has been successfully applied to encrypted network traffic classification in experimental settings. However, in production use, it has been shown that a DL classifier’s performance inevitably decays over time. Re-training the model on newer datasets has been shown to only partially improve its performance. Manually re-tuning the model architecture to meet the performance expectations on newer datasets is time-consuming and requires domain expertise. We propose AutoML4ETC, a novel tool to automatically design efficient and high-performing neural architectures for encrypted traffic classification. We define a novel, powerful search space tailored specifically for the early classification of encrypted traffic using packet header bytes. We show that with different search strategies over our search space, AutoML4ETC generates neural architectures that outperform the state-of-the-art encrypted traffic classifiers on several datasets, including public benchmark datasets and real-world TLS and QUIC traffic collected from the Orange mobile network. In addition to being more accurate, AutoML4ETC’s architectures are significantly more efficient and lighter in terms of the number of parameters. Finally, we make AutoML4ETC publicly available for future research.

Over the past twenty years, a plethora of methods have been proposed for encrypted traffic classification (ETC), while the Server name indication (SNI) is deemed to solve the problem of classification for TLS traffic. However, SNI-based classification has its pitfalls and the SNI will likely be pushed into the encrypted tunnel in the future. In this work, we envision a futuristic scenario in which encrypted SNI is the norm and labeled traffic flows are scarce. In such settings, we tackle the problem of traffic classification at ISP level using few-shot learning. By means of six real-world ISP-level datasets collected between 2019 and 2021 and two publicly available client-side datasets, we study the performance of a few-shot learner on TLS data, including its cross-dataset generalizability. We further investigate the effect of the number of required labeled samples on the learner's performance. Our experiments show that the dataset-specificity of deep learners carries over to few-shot meta-learning, and calls for addressing the problem of generalizability for deep learning architectures.

The Low-Latency Low-Loss Scalable throughput (L4S) architecture has recently been proposed to reduce the network latency of low-latency services and to allow their flows to coexist with classic ones in the same domain. This coexistence implies monitoring and security challenges. However current monitoring methods, primarily based-on sampling and polling, exhibit performance and granularity limitations. This paper describes the challenges for monitoring LL services and details our solution when introducing a fine-grained and real-time monitoring capability in our P4-based L4S implementation using In-band Network Telemetry. The initial experimental evaluation shows that our solution is able to monitor the metrics of an L4S switch with very few networking and processing overhead and without disturbing the L4S behaviour.

With the recent technological evolutions in networks and increased deployment of multi-tier clouds, cloud gaming (CG) is gaining renewed interest and is expected to become a major Internet service in the upcoming years. Many companies have launched powerful platforms such as Google Stadia, Nvidia GeForce Now, Microsoft xCloud, Sony PlayStation Now among others, to attract players. However, for all end-users to fully enjoy their gaming sessions over the wide range of network access qualities, CG platforms must adapt their traffic. In this paper, we present the outcome of real-life measurements performed between April and July 2021 on the four aforementioned CG platforms, configuring different network constraints like packet loss, throughput decrease, latency increase and jitter variation to observe the behavior of these CG platforms under extreme network conditions. Our findings show that the four platforms exhibit different adaptation behaviors. Moreover, many cases result in a degraded QoS, leaving room for further improvements at both application and/or network levels.

Low-Iatency applications, such as cloud gaming or cloud robotics are very demanding in terms of network latency. The IETF defines the L4S (Low Latency Low Loss Scalable throughput) architecture, to enable the delivery of high-bitrate low-latency applications without degrading the quality of other services. To deploy and upgrade L4S in network equipment to follow the transport protocols entering an age of quick evolutions, P4 (Programming Protocol-Independent Packet Processor), a programmable data plane concept, can help since it facilitates the deployment of networking software. In this paper, we propose a P4-based L4S solution and the performed evaluation proves our system behaves as expected. Furthermore, to be largely deployed, L4S should also be efficient under real cellular network conditions, which can vary over time and where the main network bottlenecks are. However, our evaluation shows limitations of L4S in providing high throughput while ensuring low latency delivery with time varying network conditions.

Amongst the appealing features of 5G, ultra low latency is perhaps the most attractive one, as it unleashes a wealth of disruptive services. However, meeting such a challenging goal requires a thorough understanding of latency in the 5G prevailing bottleneck, that is, the Base Station. For this purpose, we propose LatSeq, an open-source tool for fine-grained analysis of latency inside a software Base Station (BS), and implement it on the OpenAirInterface platform. LatSeq tackles each packet's sequence of successive delays across the layers of a Base Station, which reveals enlightening causal links.This paper discusses LatSeq's design choices and evaluates its fitness for purpose in a first baseline usage scenario. We demonstrate the low impact of LatSeq on the observed system, and the relevance of statistics based on individual packet tracing inside the base station.

The fifth generation of mobile cellular networks (5G) is designed to support a set of new requirements and use cases, including connectivity for the IoT (Internet of Things). Due to the industry and the user's expectation of having connectivity embedded into IoT devices, the “wholesale wireless connectivity” concept is gaining more and more attention. According to this concept, connectivity providers sell connectivity to 3rdparties, which in turn provide it to their own devices. However, this concept brings also new architecture and security requirements that are not fully addressed by the state of the art. Taking advantage of the flexibility provided by virtualization technologies (including network slicing), we propose in this paper a new 5G-SSAAC (5G Slice Specific Authentication and Access Control) mechanism that delegates authentication and access control of the devices to the 3rdparties providing these devices, thereby decreasing the load of the connectivity provider's CN (core network), while increasing flexibility and modularity of the whole 5G network.