While software defined network (SDN) brings more innovation to the development of future networks, it also faces a more severe threat from DDoS attacks. In order to deal with the single point of failure on SDN controller caused by DDoS attacks, we propose a framework for detection and defense of DDoS attacks in the SDN environment. Firstly, we deploy a trigger mechanism of DDoS attack detection on data plane to screen for abnormal flows in the network. Then, we use a combined machine learning algorithm based on K-Means and KNN to exploit the rate characteristics and asymmetry characteristics of the flows and to detect the suspicious flows determined by the detection trigger mechanism. Finally, the controller will take corresponding actions to defense against the attacks. In this paper, we propose a new framework of cooperative detection methods of control plane and data plane, which effectively improve the detection accuracy and efficiency, and prevent DDoS attacks on SDN.

The rapid development of internet of vehicles (IoV) has recently led to the emergence of diverse intelligent vehicular applications such as automatic driving, auto navigation, and advanced driver assistance, etc. However, the current vehicular communication framework, such as vehicle-to-vehicle (V2V), vehicle-to-cloud (V2C), and vehicle-to-roadside infrastructure (V2I), still remain challenging in supporting these intelligent and delay-sensitive applications, due to its long communication latency or low computational capability. Besides that, the traditional vehicle network is prone to be unavailable because of the mobility with high-speed of the vehicles. To address these issues, this paper proposes a vehicle-to-multi-edges (V2Es) communication framework in vehicular networks. By utilizing the resource of edge nodes in the proximity, the emergency information or services of vehicles can be timely processed and completed, which improves the service quality of vehicles. Furthermore, we define a joint task offloading and edge caching problem, targeting optimizing both the latency of services and energy consumption of vehicles. Based on this, we propose a multi-agent reinforcement learning (RL) method to learn the dynamic communication status between vehicles and edge nodes, and make decisions on task offloading and edge caching. Finally, results of the simulation show that our proposal is able to learn the scheduling policy more quickly and effectively, and reduce the service latency by more than 10% on average.

Enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC) are two major expected services in the fifth-generation mobile communication systems (5G). Specifically, eMBB applications support extremely high data rate communications, while URLLC services aim to provide stringent latency with high reliability communications. Due to their differentiated quality-of-service (QoS) requirements, the spectrum sharing between URLLC and eMBB services becomes a challenging scheduling issue. In this paper, we aim to investigate the URLLC and eMBB co-scheduling/coexistence problem under a puncturing technique in multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) systems. The objective function is formulated to maximize the data rate of eMBB users while satisfying the latency requirements of URLLC users through joint user selection and power allocation scheduling. To solve this problem, we first introduce an eMBB user clustering mechanism to balance the system throughput and computational complexity. Thereafter, we decompose the original problem into two subproblems, namely the scheduling problem of user selection and power allocation. We introduce a Gale-Shapley (GS) theory to solve the user selection problem, and a successive convex approximation (SCA) and a difference of convex (DC) programming to deal with the power allocation problem. Finally, an iterative algorithm is utilized to find the global solution with low computational complexity. Numerical results show the effectiveness of the proposed algorithms, and verify the proposed approach outperforms other baseline methods.

Vehicular ad hoc network (VANET) has become an accessible technology for improving road safety and driving experience, the problems of heterogeneity and lack of resources it faces have also attracted widespread attention. With the development of software-defined networking (SDN) and multiaccess edge computing (MEC), a variety of resource allocation strategies in MEC-enabled software-defined networking-based VANET (SDVN) have been proposed to solve these problems. However, we note that few of these work involves the situation where SDVN is under Distributed Denial of Service (DDoS) attacks. Actually, Internet of Things (IoT) devices are extremely easy to be compromised by malicious users, and compromised IoT devices may be used to launch edge DDoS attacks against the MEC servers in MEC-enabled SDVN at any time. In this article, we propose a graph neural network (GNN)-based collaborative deep reinforcement learning (GCDRL) model to generate the resource provisioning and mitigating strategy. The model evaluates the trust value of the vehicles, formulates mitigation of edge DDoS attacks and resource provisioning strategies to ensure that the MEC servers can work normally under edge DDoS attacks. In addition, GNN is adopted in the DRL model to extract the structure feature of the graph composed of MEC servers, and help transfer computing tasks between MEC servers to alleviate the problem of resources imbalance between them. Experimental results show that the method of estimating the vehicular trust value is effective, and our method can make the average throughput of edge nodes more stable and lower down the average delay and the average energy consumption under the edge DDoS attack. Also, a real-world case study is conducted to verify our conclusion.

With the rapid growth of Internet of Things (IoT), smart home develops rapidly in these years, which could assist people who need family medical support. It could integrate healthcare with ambient assisted living (AAL) technologies and provide activities of daily life (ADLs) to the people who need care. This article proposes a smart home and cross-cloud-and-edge computing-based nursing system (NS). In general, a good NS requires low latency, high stability, and the real-time analysis and response, whereas the conventional centralized cloud computing-based approaches cannot meet those requirements very well. To this end, we introduce a novel distributed joint edge-cloud structure to better satisfy these requirements. Moreover, to deal with the security and privacy issues, we introduce the blockchain to verify the identity of data exchanging and differential privacy (DP) in the NS to protect the healthcare takers’ data privacy. In a word, we propose a privacy-preserving context-aware multiarmed bandit-based online learning approach for edge-cloud-enabled NS via blockchain in IoT. Additionally, our system with a novel top-down expanding tree-based structure can support dynamically increasing healthcare data sets. Extensive experimental and numerical results demonstrate that our solution can achieve accurate recommendation results with sublinear regret performance.

Mobile edge network has been recognized as a promising technology for future wireless communications. However, mobile edge networks usually gathering large amounts of data, which makes it difficult to explore data science efficiently. Currently, federated learning has been proposed as an appealing approach to allow users to cooperatively reap the benefits from trained participants. In this paper, we propose a novel Semi-Asynchronous Hierarchical Federated Learning (SAHFL) framework for mobile edge networks that enables elastic edge to cloud model aggregation from data sensing. We further formulate a joint edge node association and resource allocation problem under the proposed SAHFL framework to prevent personalities of heterogeneous devices and achieve communication-efficiency. To deal with our proposed Mixed integer nonlinear programming (MINLP) problem, we introduce a distributed Alternating Direction Method of Multipliers (ADMM)- Block Coordinate Update (BCU) algorithm. With this algorithm, a tradeoff between training accuracy and transmission latency has been derived. Numerical results demonstrate the advantages of the proposed algorithm in terms of training overhead and model performance.