Honeypot Identification in Softwarized Industrial Cyber–Physical Systems

Sun, Yanbin; Tian, Zhihong; Li, Mohan; Su, Shen; Du, Xiaojiang; Guizani, Mohsen

doi:10.1109/tii.2020.3044576

Cited by 48 publications

(25 citation statements)

References 21 publications

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“…If some IoT data involves the user's private information, once it is leaked, it will cause property and life safety issues. More and more solutions [31][32][33][34] are proposed to solve data security issues, which are implemented by not directly processing data. In addition, people can also protect their privacy by processing data.…”

Section: Related Workmentioning

confidence: 99%

A Privacy Protection Scheme for IoT Big Data Based on Time and Frequency Limitation

Zhang

Huo

et al. 2021

Wireless Communications and Mobile Computing

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Various applications of the Internet of Things assisted by deep learning such as autonomous driving and smart furniture have gradually penetrated people’s social life. These applications not only provide people with great convenience but also promote the progress and development of society. However, how to ensure that the important personal privacy information in the big data of the Internet of Things will not be leaked when it is stored and shared on the cloud is a challenging issue. The main challenges include (1) the changes in access rights caused by the flow of manufacturers or company personnel while sharing and (2) the lack of limitation on time and frequency. We propose a data privacy protection scheme based on time and decryption frequency limitation that can be applied in the Internet of Things. Legitimate users can obtain the original data, while users without a homomorphic encryption key can perform operation training on the homomorphic ciphertext. On the one hand, this scheme does not affect the training of the neural network model, on the other hand, it improves the confidentiality of data. Besides that, this scheme introduces a secure two-party agreement to improve security while generating keys. While revoking, each attribute is specified for the validity period in advance. Once the validity period expires, the attribute will be revoked. By using storage lists and setting tokens to limit the number of user accesses, it effectively solves the problem of data leakage that may be caused by multiple accesses in a long time. The theoretical analysis demonstrates that the proposed scheme can not only ensure safety but also improve efficiency.

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Section: Related Workmentioning

confidence: 99%

A Privacy Protection Scheme for IoT Big Data Based on Time and Frequency Limitation

Zhang

Huo

et al. 2021

Wireless Communications and Mobile Computing

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“…There are various protocols mixed together on the network, and malicious programs use private protocols to avoid being tracked and analyzed, which brings great challenges to network security. Especially in the industrial cyber-physical system, which faces various security threats, these programs can jeopardize the system's stability [6,7]. In addition, well-known network protocols may have different formats due to different application requirements.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Binary Protocol Clustering Based on Maximum Sequential Patterns

Shi¹,

Ye²,

Li³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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With the rapid development of the Internet, a large number of private protocols emerge on the network. However, some of them are constructed by attackers to avoid being analyzed, posing a threat to computer network security. The blockchain uses the P2P protocol to implement various functions across the network. Furthermore, the P2P protocol format of blockchain may differ from the standard format specification, which leads to sniffing tools such as Wireshark and Fiddler not being able to recognize them. Therefore, the ability to distinguish different types of unknown network protocols is vital for network security. In this paper, we propose an unsupervised clustering algorithm based on maximum frequent sequences for binary protocols, which can distinguish various unknown protocols to provide support for analyzing unknown protocol formats. We mine the maximum frequent sequences of protocol message sets in bytes. And we calculate the fuzzy membership of the protocol message to each maximum frequent sequence, which is based on fuzzy set theory. Then we construct the fuzzy membership vector for each protocol message. Finally, we adopt K-means++ to split different types of protocol messages into several clusters and evaluate the performance by calculating homogeneity, integrity, and Fowlkes and Mallows Index (FMI). Besides, the clustering algorithms based on Needleman-Wunsch and the fixed-length prefix are compared with the algorithm presented in this paper. Compared with these traditional clustering methods, we demonstrate a certain improvement in the clustering performance of our work.

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“…Although the above CNN-based methods have an outstanding performance for RSI scene classification, they also face various security risks [15], e.g., data poisoning attacks [16] and adversarial sample attacks. To defend against these attacks, researchers also proposed corresponding malicious detection methods [17][18][19]. Among these attacks, the classifiers can easily be fooled by the designed adversarial samples and get unexpected results.…”

Section: Introductionmentioning

confidence: 99%

Project Gradient Descent Adversarial Attack against Multisource Remote Sensing Image Scene Classification

Jiang

Yin

Yuan

et al. 2021

Security and Communication Networks

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Deep learning technology (a deeper and optimized network structure) and remote sensing imaging (i.e., the more multisource and the more multicategory remote sensing data) have developed rapidly. Although the deep convolutional neural network (CNN) has achieved state-of-the-art performance on remote sensing image (RSI) scene classification, the existence of adversarial attacks poses a potential security threat to the RSI scene classification task based on CNN. The corresponding adversarial samples can be generated by adding a small perturbation to the original images. Feeding the CNN-based classifier with the adversarial samples leads to the classifier misclassify with high confidence. To achieve a higher attack success rate against scene classification based on CNN, we introduce the projected gradient descent method to generate adversarial remote sensing images. Then, we select several mainstream CNN-based classifiers as the attacked models to demonstrate the effectiveness of our method. The experimental results show that our proposed method can dramatically reduce the classification accuracy under untargeted and targeted attacks. Furthermore, we also evaluate the quality of the generated adversarial images by visual and quantitative comparisons. The results show that our method can generate the imperceptible adversarial samples and has a stronger attack ability for the RSI scene classification.

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Honeypot Identification in Softwarized Industrial Cyber–Physical Systems

Cited by 48 publications

References 21 publications

A Privacy Protection Scheme for IoT Big Data Based on Time and Frequency Limitation

A Privacy Protection Scheme for IoT Big Data Based on Time and Frequency Limitation

Unsupervised Binary Protocol Clustering Based on Maximum Sequential Patterns

Project Gradient Descent Adversarial Attack against Multisource Remote Sensing Image Scene Classification

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