Unmanned aerial vehicles (UAV) swarm network (USNET) is a promising solution for diverse applications and usually works in harsh environments. However, it is challenging to rebuild the communication connectivity in USNETs with low time complexity under unpredictable UAV damages. In this paper, we present a graph attention network (GAT)-based deep reinforcement learning topology self-healing (GDR-TS) framework to minimize the topology self-healing (TSH) time of remaining UAVs. Specifically, we decompose the TSH problem into a neighbor selection problem and a trajectory planning problem. For the former, we present an edge update GAT-based actor-critic structure to find an optimal adjacency matrix; for the latter, we present a node update GAT-based position fine-tuning module to compute an optimal position matrix. Simulation results show that under various disruption setups, our GDR-TS framework outperforms the other four baselines in terms of the average TSH time and the response time.

Anomaly detection of Key Performance Indicators (KPIs) e.g. response latency, network throughput, etc., is one of the key techniques to ensure the quality and security of network services. However, state-of-the-art unsupervised deep learning algorithms present limitations: they are sensitive to noise and demand extensive KPI data for training, complicating the detection process. This paper proposes an Unsupervised Deep Transfer Learning (UDTL) approach. First, UDTL uses self-training preprocessing that generates reliable, high-quality samples for model training. Then, UDTL calculates the correlation based on the shape of the KPIs and the deviation scores of the KPIs, and clusters these KPIs into different clusters via correlation. At last, UDTL selects the KPI closest to each cluster’s centroid to train a base anomaly detection model for this cluster. The anomaly detection model of other KPIs adaptively choose the parameters to transfer based on correlation. Our experiments conducted on several public datasets highlight UDTL’s effectiveness. UDTL enhances the F1 score by 15.24% and reduces the training time by 22.17 times compared to baselines.

Abstract In generating adversarial examples, the conventional black-box attack methods rely on sufficient feedback from the to-be-attacked models by repeatedly querying until the attack is successful, which usually results in thousands of trials during an attack. This may be unacceptable in real applications since Machine Learning as a Service Platform (MLaaS) usually only returns the final result (i.e., hard-label) to the client and a system equipped with certain defense mechanisms could easily detect malicious queries. By contrast, a feasible way is a hard-label attack that simulates an attacked action being permitted to conduct a limited number of queries. To implement this idea, in this paper, we bypass the dependency on the to-be-attacked model and benefit from the characteristics of the distributions of adversarial examples to reformulate the attack problem in a distribution transform manner and propose a distribution transform-based attack (DTA). DTA builds a statistical mapping from the benign example to its adversarial counterparts by tackling the conditional likelihood under the hard-label black-box settings. In this way, it is no longer necessary to query the target model frequently. A well-trained DTA model can directly and efficiently generate a batch of adversarial examples for a certain input, which can be used to attack un-seen models based on the assumed transferability. Furthermore, we surprisingly find that the well-trained DTA model is not sensitive to the semantic spaces of the training dataset, meaning that the model yields acceptable attack performance on other datasets. Extensive experiments validate the effectiveness of the proposed idea and the superiority of DTA over the state-of-the-art.

Due to the highly dynamic network topology in Flying Ad hoc Networks (FANETs), topology-based routing protocols are impractical, while known geographic routing protocols suffer from routing holes. In this paper, a Q-learning-based adaptive geographic routing protocol is presented, in which the impact of delay, mobility, and energy consumption on routing is comprehensively considered. To overcome the dynamic changes in routing caused by the high mobility of Unmanned Aerial Vehicles (UAVs), reinforcement learning parameters and HELLO message interval are adaptively adjusted by sensing local topology changes. Meanwhile, a new routing hole avoidance mechanism is proposed, which involves a scheme of broadcasting routing hole information and an approach of data forwarding route selection based on the node degree of UAVs and the distance to the destination node. To enable the routing protocol to adapt to highly dynamic changes in FANETs topology, link pre-learning and multi-Q learning methods are used to speed up the learning process. We use NS-3 to evaluate the proposed routing protocol. The results show that our protocol improves throughput by 6% and reduces delay by 20% compared to Q-learning-based Multi-objective Optimization Routing (QMR). It is also far superior to Q-learning-based Geographic Routing (QGeo) and Greedy Perimeter Stateless Routing (GPSR).

Mobile Edge Computing (MEC) enables vehicles to flexibly obtain computing, content storage and other services by deploying at the network edge, which is of great research significance. The application tasks in vehicular networks can be distributed to MEC or task vehicle for collaborative processing. Aiming at the situation that the task dependency graph with multiple corresponding relationships is represented by Directed Acyclic Graph (DAG), this paper considers caching the program data of the corresponding node, and explores the problem of task scheduling and resource allocation in the cache enhancement scenario. Firstly, the task scheduling and cache placement decision are modeled as the optimization problem of minimizing the completion time. Then, through the analysis of the optimization problem, the problem is divided into two sub problems: task scheduling and cache decision. For the two sub problems, the task scheduling algorithm based on the latest start time and the cache decision algorithm based on dynamic programming are designed respectively. Simulation results show that the proposed algorithms and scheme can significantly reduce the completion time and task failure rate compared with the benchmark scheme.