A Novel Anonymous Authentication Scheme Based on Edge Computing in Internet of Vehicles

Wang, Xiaoliang; She, Xinhui; Bai, Liang; Qing, Yang; Jiang, Frank

doi:10.32604/cmc.2021.012454

Cited by 11 publications

(3 citation statements)

References 40 publications

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“…We found that the attention mechanism improves the model results during our experiments. In the future work, we focus on applying the attention mechanism further to enhance the effects of threat behavior entity extraction and explore the few-shot learning method to improve the classification ability of a few categories and strengthen the generalization ability of the model minor sample problems, and added authentication to enhance the security of the usage process [42].…”

Section: Experimental Results Analysismentioning

confidence: 99%

Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks

Tang¹,

Wang²,

Qiu³

et al. 2023

Computers, Materials &Amp; Continua

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The continuous improvement of the cyber threat intelligence sharing mechanism provides new ideas to deal with Advanced Persistent Threats (APT). Extracting attack behaviors, i.e., Tactics, Techniques, Procedures (TTP) from Cyber Threat Intelligence (CTI) can facilitate APT actors' profiling for an immediate response. However, it is difficult for traditional manual methods to analyze attack behaviors from cyber threat intelligence due to its heterogeneous nature. Based on the Adversarial Tactics, Techniques and Common Knowledge (ATT&CK) of threat behavior description, this paper proposes a threat behavioral knowledge extraction framework that integrates Heterogeneous Text Network (HTN) and Graph Convolutional Network (GCN) to solve this issue. It leverages the hierarchical correlation relationships of attack techniques and tactics in the ATT&CK to construct a text network of heterogeneous cyber threat intelligence. With the help of the Bidirectional Encoder Representation from Transformers (BERT) pretraining model to analyze the contextual semantics of cyber threat intelligence, the task of threat behavior identification is transformed into a text classification task, which automatically extracts attack behavior in CTI, then identifies the malware and advanced threat actors. The experimental results show that F1 achieve 94.86% and 92.15% for the multi-label classification tasks of tactics and techniques. Extend the experiment to verify the method's effectiveness in identifying the malware and threat actors in APT attacks. The F1 for malware and advanced threat actors identification task reached 98.45% and 99.48%, which are better than the benchmark model in the experiment and achieve state of the art. The model can effectively model threat intelligence text data and acquire knowledge and experience migration by correlating implied features with a priori knowledge to compensate for insufficient sample data and improve the classification performance and recognition ability of threat behavior in text.

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Section: Experimental Results Analysismentioning

confidence: 99%

Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks

Tang¹,

Wang²,

Qiu³

et al. 2023

Computers, Materials &Amp; Continua

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“…To guarantee the safety of data, several works [10,11] built blockchain systems for IoV. Bai et al [12] proposed a driving behavior warning scheme based on blockchain.…”

Section: Application Of Blockchain In the Vehicular Networkmentioning

confidence: 99%

A Trusted Edge Resource Allocation Framework for Internet of Vehicles

Zhong,

Xu,

Liao

et al. 2023

CMC

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With the continuous progress of information technique, assisted driving technology has become an effective technique to avoid traffic accidents. Due to the complex road conditions and the threat of vehicle information being attacked and tampered with, it is difficult to ensure information security. This paper uses blockchain to ensure the safety of driving information and introduces mobile edge computing technology to monitor vehicle information and road condition information in real time, calculate the appropriate speed, and plan a reasonable driving route for the driver. To solve these problems, this paper proposes a trusted edge resource allocation framework for assisted driving service, which includes two stages: the blockchain generation stage (the first stage) and assisted driving service stage (the second stage). Furthermore, in the first stage, a delay-and-throughput-oriented block generation model for the mobile terminal is designed. In the second stage, a balanced offloading algorithm for assisted driving service based on edge collaboration is proposed to solve the problems of unbalanced load of cluster mobile edge computing (MEC) servers and low resource utilization of the system. And this paper optimizes the throughput of blockchain and delay of the transportation network through deep reinforcement learning (DRL) algorithm. Finally, compared with joint computation and communication resources' allocation (JCCR) and resource allocation method based on binary offloading (RAB), our proposed scheme can optimize the delay by 7.4% and 26.7%, and support various application services of the vehicular networks more effectively.

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“…The implementation of UAV-assisted MEC systems has some challenges, such as limited onboard energy of the UAV [17], security of aerial data, security of the UAV, data privacy [18], and data management and storage. This study focuses on preserving privacy in UAV-assisted systems.…”

Section: Introductionmentioning

confidence: 99%

Efficient Authentication Scheme for UAV-Assisted Mobile Edge Computing

Alhassan¹,

Khan²

2023

Computers, Materials &Amp; Continua

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Preserving privacy is imperative in the new unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) architecture to ensure that sensitive information is protected and kept secure throughout the communication. Simultaneously, efficiency must be considered while developing such a privacy-preserving scheme because the devices involved in these architectures are resource constrained. This study proposes a lightweight and efficient authentication scheme for the UAV-assisted MEC environment. The proposed scheme is a hardware-based password-less authentication mechanism that is based on the fact that temporal and memory-related efficiency can be significantly improved while maintaining the data security by adopting a hardwarebased solution with a simple implementation. The proposed scheme works in four stages: system initialization, EU registration, EU authentication, and session establishment. It is implemented as a single hardware chip comprising registers and XOR gates, and it can run the entire process in one clock cycle. Consequently, the proposed scheme has significantly higher efficiency in terms of runtime and memory consumption compared to other prevalent methods in the area. Simulations are conducted to evaluate the proposed authentication algorithm. The results show that the scheme has an average execution time of 0.986 ms and consumes average memory of 34 KB. The hardware execution time is approximately 0.39 ns, which is a significantly less than the prevalent schemes, whose execution times range in milliseconds. Furthermore, the security of the proposed scheme is examined, and it is resistant to brute-force attacks. Around 1.158 × 10 77 trials are required to overcome the system's security, which is not feasible using fastest available processors.

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A Novel Anonymous Authentication Scheme Based on Edge Computing in Internet of Vehicles

Cited by 11 publications

References 40 publications

Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks

Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks

A Trusted Edge Resource Allocation Framework for Internet of Vehicles

Efficient Authentication Scheme for UAV-Assisted Mobile Edge Computing

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