Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation, which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units (RSU) or unmanned aerial vehicles (UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning (MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization (MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers (e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.

In the studies of cybersecurity, malicious traffic detection is attracting more and more attention for its capability of detecting attacks. Almost all of the intrusion detection methods based on deep learning have poor data processing capacity with the increase of the data length. Most intrusion detection methods can only handle the header part of the traffic and omit valuable information from the payload, so they can not detect the malicious traffic when the hacker hides attack behavior in the payload. In this article, we propose an attention model that can process network traffic flow with adjustable length to detect payload-based attacks. Furthermore, we design a Flow Wasserstein GAN model to generate new network traffic data from the original data sets to enhance network packet data and protect user privacy. Our model has a hierarchical structure to build representations of bytes and packets on two levels. Moreover, two levels of attention mechanisms enable the model to pay attention to more important content when constructing the flow representation. The experiments based on the ISCX-2012 and ISCX-2017 datasets prove that the proposed model has higher performance in accuracy and true positive rate (TPR) than four state-of-the-art deep learning methods. The experiment shows that our model outperforms the existing HSAT-IDS in detection of the generated packets.

In this paper, we design and implement a full-packet capture and network measurement (FPC-NM) system in a 20 Gbit/s network. The system has multiple functions, such as packet receiving, nanosecond timestamp, load balancing, data packet preprocessing, application layer protocol analysis, data packet storage, and log management. We design a timestamp module in FPGA that can add a timestamp to each captured packet, and the retrospective packet analysis can be done with 4 ns accuracy. As far as we know, this is the first time that nanosecond timestamp has been applied to FPC-NM system, which greatly improves the accuracy of security incident retrospective analysis. The experimental results show that the FPC-NM system can achieve 17 Gbit/s throughput and 160,000 connections per second under the premise of zero packet loss. By using LZ4 compression, the performance of our system for real-time compression and storage of traffic can achieve 10 Gbit/s. The system performance is improved by about 40% after software optimization including direct memory access buffer size adjustment and single instruction multiple data instruction set, which validates the practicability and effectiveness of our optimization method.

This article proposes a domain name caching method for the multicore network-traffic capture system, which significantly improves insert latency, throughput and hit rate. The caching method is composed of caching replacement algorithm, cache set method. The method is easy to implement, low in deployment cost, and suitable for various multicore caching systems. Moreover, it can reduce the use of locks by changing data structures and algorithms. Experimental results show that compared with other caching system, our proposed method reaches the highest throughput under multiple cores, which indicates that the cache method we proposed is best suited for domain name caching.