With the increasing prevalence of encrypted communication protocols such as TLS 1.3 and QUIC, the volume of encrypted traffic has risen sharply, leading to significant challenges for traditional rule-based traffic detection systems. In particular, cryptomining malware has become more sophisticated, leveraging encryption to evade detection. While machine learning with manually crafted features has been explored for this problem, such methods depend heavily on expert knowledge and break the task into sub-problems that may not yield optimal solutions. In this paper, we propose GVTMC (Graph-Vision-Time Miner Classifier), a novel end-to-end approach for detecting encrypted cryptocurrency mining traffic. GVTMC integrates complementary features through a multi-branch deep neural architecture: a time-series branch models packet timing and length patterns, a packet-vision transformer branch captures content features from packet payloads, and a TLS graph branch analyzes handshake behaviors using graph neural networks. These features are fused using an adaptive weighting mechanism to classify traffic in a unified model. Extensive experiments on real-world encrypted traffic datasets demonstrate that GVTMC outperforms state-of-the-art methods, achieving over 94% accu-racy, more than a 5 percentage point improvement over prior solutions, and high detection rates even under VPN and proxy obfuscation. Our approach provides an effective, scalable, and privacy-preserving solution for encrypted cryptomining traffic detection with strong potential for real-world deployment.

Vehicular ad hoc networks (VANETs) have the characteristics of high mobility, frequently changing topology and uneven distribution, which made it a challenge to design an efficient and robust routing protocol with low latency and high packet delivery rate. Currently, intersection-based routing method and full-path based routing method are two popular solutions for the packet routing in VANETs. Although the intersection-based routing method has better real-time performance, it has the problem of local optimization, making the routing results not global optimal. Although the full-path based method can obtain the global optimal solution, it is weak in dealing with the dynamically changing topological network. Aiming at solving the above problems, this paper designs an intersection-based QoS routing (IQRRL) algorithm, which mainly includes two crutial steps: the next intersection selection and the next hop vehicle selection. In the selection of the next intersection, this paper uses an improved intersection-based routing protocol. In addition to considering connectivity and delay, IQRRL also considers the communication quality from the neighbor’s road to the destination node while evaluating the quality of the neighbor’s road, which minimizes the problem of local optimization. When the next intersection is determined, a road is then determined, and then the next hop vehicle within the road should be chosen to relay the packet forward. In the next-hop vehicle selection step, this paper adopts multi-hop evaluation technology based on reinforcement learning. In addition to using “greedy decision-making” to select the next-hop vehicle, it also comprehensively evaluates whether the next-hop vehicle is still optimal in the future, so that the stability and reliability of data forwarding are improved and local optimal problems are avoided. Besides, this article uses a simulation system to compare IQRRL with other routing algorithms. The result reveals that IQRRL outperforms in terms of packet delivery ratio and transmission delay.

In the industrial scenario, the requirements for network quality and data security of industrial Time Sensitive Network (TSN) services are becoming more and more stringent. 5G technology has the characteristics of wide bandwidth, low latency, and massive connections. Mobile Edge Computing (MEC) can improve the utilization of resources and the security of data, while future reducing the transmission delay. However, for industrial TSN services, a new network deployment architecture is still needed to provide reliable, flexible, and secure services for the industrial Internet. This paper proposes a local deployment architecture and adopts a deployment optimization scheme of Distributed Unit/ Centralized Unit/ User Plane Function/ TSN Translator (DU/CU/UPF/TT) convergence integration, highly integrated network elements, build DU/CU/UPF/TT compact integrated equipment, realize the idea of the separation of control plane and user plane, and realize efficient data forwarding. Meanwhile, some technologies such as Multiple-transmission/Reception Point (Multi-TRP) and New Radio Dual Connectivity (NR-DC) are used to meet the requirements of the low latency of industrial TSN services, and ensure data security and improve network quality.

In cyber-physical power system (CPPS), the communication transmission links carry key services. However, some existing routing approaches cause service routes to be too centralized in the links, which leads to increase the risk of service and network. In order to reduce the risk impact of communication transmission links interruption on the service, this paper proposes a service routing reconstruction approach based on risk balance. Firstly, we analyze the risk transmission of cross-space in CPPS. Then this paper establishes a risk assessment model by characterizing the node risks with the station load pressure, expressing the link risks with the service average communication delay and the service risk balance degree. Further, the improved genetic algorithm is adopted to solve the service routing reconstruction. Finally, based on part of power grid topology from a Chinese province, the simulation results show that the proposed approach can find a route allocation scheme with lower risk value than the original Dijkstra algorithm and genetic algorithm using "roulette wheel" selection strategy, as well as ensuring the stable operation of the power system.

Light-emitting diodes (LEDs) have become quite a high-performance device of late and are revolutionizing the display and illumination sectors of our economy. Due to demands for better performance and reduced energy consumption there is a constant race towards converting every single electron hole pair in the device to photons and extracting them as well while using only the minimum required voltage. This raises the bar on GaN-based LEDs in terms of elimination of nonradiative recombination processes not just at low but just as importantly if not more at very high injection levels needed for high brightness sources, and design of heterostructures for efficient electron and hole recombination without carrier loss and voltage/photon energy conversion loss. The haunting efficiency droop observed in GaN-based LEDs at high injection levels has been attributed to nonradiative Auger recombination, but can be simply explained by electron spillover. Investigations of quantum well (QW) InGaN LED structures with different barrier heights, widths, and doping suggest that limited hole transport in the active region and the resulting electron spillover is responsible for efficiency droop at high injection levels. In this paper, highly critical, demanding, and challenging nature of high-efficiency high-brightness LEDs, in particular the basics surrounding the internal quantum efficiency of LED structures and the ongoing research/development, will be discussed.