This study focuses on the problem of attack quantification in distribution automation systems (DASs) and proposes a quantitative model of attacks based on the common vulnerability scoring system (CVSS) and attack trees (ATs) to conduct a quantitative and systematic evaluation of attacks on a DAS. In the DAS security architecture, AT nodes are traversed and used to represent the attack path. The CVSS is used to quantify the attack sequence, which is the leaf node in an AT. This paper proposes a method to calculate each attack path probability and find the maximum attack path probability in DASs based on attacker behavior. The AT model is suitable for DAS hierarchical features in architecture. The experimental results show that the proposed model can reduce the influence of subjective factors on attack quantification, improve the probability of predicting attacks on the DASs, generate attack paths, better identify attack characteristics, and determine the attack path and quantification probability. The quantitative results of the model's evaluation can find the most vulnerable component of a DAS and provide an important reference for developing targeted defensive measures in DASs. these attack quantification results can also provide an important reference for security technicians to implement the DAS defense system.Quantification of the probability of an attack on a DAS directly affects the in-depth analysis of the system's security. Wang et al. [9] proposed a multilevel analysis and modeling method for a power system's communication network. Their case study showed that this method can be used to evaluate the static and dynamic relationships among power networks. Kateb et al. [10] developed an optimal structure tree method for risk assessment in a wide-area power system that can minimize the spread of network attacks. The authors in [9,10] provided a well-optimized evaluation of a specific power network. However, these evaluation neither reflected the attacker's behavior in terms of quantification of the probability of an attack nor provided suggestions for the protection of specific parts of the power system. The authors in [11] and the authors in [12] presented an attack assessment framework based on Bayes attributes-a stochastic game model and a fast modeling method for input data, respectively-which included network connection relationship and vulnerability information. However, the proposed methods were found to be inefficient when applied in DASs due to DAS architecture complexity and expansibility, and they could not generate attack path. The authors in [13] proposed a method for modeling network attacks with a multilevel-layered attack tree (MLL-AT), presented a description language based on the MLL-AT for attacks, and quantified the leaf nodes. This attack tree (AT) was found to be able to accurately model the attacks, especially multilevel network attacks, and can be used to assess system risks. However, the research is mainly based on cyberattacks, and there is no physical attacks involved. Besides, th...
Mobile edge computing (MEC) effectively integrates wireless network and Internet technologies and adds computing, storage, and processing functions to the edge of cellular networks. This new network architecture model can deliver services directly from the cloud to the very edge of the network while providing the best efficiency in mobile networks. However, due to the dynamic, open, and collaborative nature of MEC network environments, network security issues have become increasingly complex. Devices cannot easily ensure obtaining satisfactory and safe services because of the numerous, dynamic, and collaborative character of MEC devices and the lack of trust between devices. The trusted cooperative mechanism can help solve this problem. In this paper, we analyze the MEC network structure and device-to-device (D2D) trusted cooperative mechanism and their challenging issues and then discuss and compare different ways to establish the D2D trusted cooperative relationship in MEC, such as social trust, reputation, authentication techniques, and intrusion detection. All these ways focus on enhancing the efficiency, stability, and security of MEC services in presenting trustworthy services.
Software-defined networking (SDN) is a modern network architecture, which separates the network control plane from the data plane. Considering the gradual migration from traditional networks to SDNs, the hybrid SDN, which consists of SDN-enabled devices and legacy devices, is an intermediate state. For wide-area hybrid SDNs, to guarantee the control performance, such as low latency, multi SDN controllers are usually needed to be deployed at different places. How to assign them to switches and partition the network into several control domains is a critical problem. For this problem, the control latency and the packet loss rate of control messages are important metrics, which have been considered in a lot of previous works. However, hybrid SDNs have their unique characters, which can affect the assignment scheme and have been ignored by previous studies. For example, control messages pass through Legacy Forwarding Devices (LFDs) in hybrid SDNs and cause more latency and packet loss rate for queuing compared with SDN-enabled Forwarding Devices (SFDs). In this paper, we propose a dynamic controller assignment scheme in hybrid SDNs, which is called the Legacy Based Assignment (LBA). This scheme can dynamically delegate each controller with a subset of SFDs in the hybrid SDNs, whose objective is to minimize average SFD-to-control latency. We performed some experiments compared with other schemes, which show that our scheme has a better performance in terms of the latency and the packet loss rate.
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