Real-Time Lateral Movement Detection Based on Evidence Reasoning Network for Edge Computing Environment

Tian, Zhihong; Shi, Wei; Wang, Yuhang; Zhu, Chunsheng; Du, Xiaojiang; Su, Shen; Sun, Yanbin; Guizani, Nadra

doi:10.1109/tii.2019.2907754

Cited by 192 publications

(90 citation statements)

References 28 publications

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“…With the communication capacity provided by the smart grid and VANET, the charging pile can negotiate with an EV based on these running data. It can also sense the distribution of nearby EVs and their charging demands [24][25][26]. Charging pile may advertise the quality and availability of service to the EVs which can make decision of charging choice to eliminate the waste of time and energy in case of choosing another pile due to capacity limit and profile mismatch.…”

Section: Modeling Of Charging Pilementioning

confidence: 99%

Time series behavior modeling with digital twin for Internet of Vehicles

Zhang¹,

Liu²,

Luo

et al. 2019

J Wireless Com Network

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Electric vehicle (EV) is considered eco-friendly with low carbon emission and maintenance costs. Given the current battery and charging technology, driving experience of EVs relies heavily on the availability and reachability of EV charging infrastructure. As the number of charging piles increases, carefully designed arrangement of resources and efficient utilization of the infrastructure is essential to the future development of EV industry. The mobility and distribution of EVs determine the charging demand and the load of power distribution grid. Then, dynamic traffic pattern of numerous interconnected EVs poses great impact on charging plans and charging infrastructure. In this paper, we introduce the digital twin of a real-world EV by modeling the mobility based on a time series behaviors of EVs to evaluate the charging algorithm and pile arrangement policy. The introduced digital twin EV is a virtually simulated equivalence with same traffic behaviors and charging activities as the EV in real world. The behavior and route choice of EVs is dynamically simulated base on the time-varying driving operations, travel intent, and charging plan in a simulated large-scale charging scenario composed of concurrently moving EVs and correspondingly equipped charging piles. Different EV navigation algorithms and charging algorithms of Internet of Vehicle can be exactly evaluated in the dynamic simulation of the digital twins of the moving EVs and charging infrastructure. Then we analyze the collected data such as energy consumption, charging capacity, charging frequency, and waiting time in queue on both the EV side and the charging pile side to evaluate the charging efficiency. The simulation is used to study the relations between the scheduled charging operation of EVs and the deployment of piles. The proposed model helps evaluate and validate the design of the charging recommendation and the deployment plan regarding to the arrangement and distribution of charging piles.

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Section: Modeling Of Charging Pilementioning

confidence: 99%

Time series behavior modeling with digital twin for Internet of Vehicles

Zhang¹,

Liu²,

Luo

et al. 2019

J Wireless Com Network

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show abstract

“…As shown above, the rise of new technologies (e.g. Internet of Vehicles, [34][35][36][37] Heterogeneous Sensor Networks, [38][39][40][41][42][43] Edge Computing, 44,45 Smart City, 46 Blockchain) [47][48][49] makes CIA unsuitable. In this section, we will answer two fundamental problems: which security properties are fundamental?…”

Section: Connotation Of Information Securitymentioning

confidence: 99%

Hierarchically defining Internet of Things security: From CIA to CACA

Liu

Fang

et al. 2020

International Journal of Distributed Sensor Networks

Self Cite

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With the rapid development of Internet of Things technology (e.g. wireless sensor networks), security has become a global issue. Confidentiality, integrity, and availability (known as the CIA triangle) is widely used to define and model information security. However, this CIA triangle is insufficient to address rapidly changing security requirements. In this article, we divide information systems into four layers: physical layer, operational layer, data layer, and content layers (PODC). Corresponding, hierarchy of information security is proposed. Furthermore, we define the basic security properties for each layer and show that the four properties (i.e. confidentiality, availability, controllability, and authentication, called CACA) are minimally complete and independent for information security. Based on PODC and CACA, a new definition of information security is proposed, which acts as a secure foundation for information systems.

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“…In order to achieve better results, the defense decisionmaking approach based on WoLF-PHC needs to set four parameters α, δ, λ, and c reasonably. (1) e range of the payoff learning rate is 0 < α < 1. e bigger the representative α is, the more important the cumulative reward is. e faster the learning speed is, the smaller α is and the better the stability of the algorithm is.…”

Section: Defense Decision-making Algorithm Based On Improvedmentioning

confidence: 99%

“…With the continuous strengthening of social informatization, cyber attacks are becoming more frequent, causing tremendous losses to defenders [1]. Because of the complexity of the network itself and the limitation of the defender's ability, the network cannot achieve absolute security.…”

Section: Introductionmentioning

confidence: 99%

Optimal Decision-Making Approach for Cyber Security Defense Using Game Theory and Intelligent Learning

Zhang

Liu

2019

Security and Communication Networks

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Existing approaches of cyber attack-defense analysis based on stochastic game adopts the assumption of complete rationality, but in the actual cyber attack-defense, it is difficult for both sides of attacker and defender to meet the high requirement of complete rationality. For this aim, the influence of bounded rationality on attack-defense stochastic game is analyzed. We construct a stochastic game model. Aiming at the problem of state explosion when the number of network nodes increases, we design the attack-defense graph to compress the state space and extract network states and defense strategies. On this basis, the intelligent learning algorithm WoLF-PHC is introduced to carry out strategy learning and improvement. Then, the defense decision-making algorithm with online learning ability is designed, which helps to select the optimal defense strategy with the maximum payoff from the candidate strategy set. The obtained strategy is superior to previous evolutionary equilibrium strategy because it does not rely on prior data. By introducing eligibility trace to improve WoLF-PHC, the learning speed is further improved and the defense timeliness is significantly promoted.

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Real-Time Lateral Movement Detection Based on Evidence Reasoning Network for Edge Computing Environment

Cited by 192 publications

References 28 publications

Time series behavior modeling with digital twin for Internet of Vehicles

Time series behavior modeling with digital twin for Internet of Vehicles

Hierarchically defining Internet of Things security: From CIA to CACA

Optimal Decision-Making Approach for Cyber Security Defense Using Game Theory and Intelligent Learning

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