Verifiable Chebyshev maps‐based chaotic encryption schemes with outsourcing computations in the cloud/fog scenarios

Li, Jing; Wang, Licheng; Wang, Lihua; Wang, Xianmin; Huang, Zhengan

doi:10.1002/cpe.4523

Cited by 16 publications

(16 citation statements)

References 35 publications

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“…However, how to securely outsource the computation to the cloud is a major challenge. Different solutions have been proposed which address this issue [77][78][79][80]. In the research of ML, Li et al [76] discussed theoretical aspects of privacy in the sense of data privacy and privacy in the training model using deep learning.…”

Section: Privacy Preservationmentioning

confidence: 99%

Applications in Security and Evasions in Machine Learning: A Survey

2020

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In recent years, machine learning (ML) has become an important part to yield security and privacy in various applications. ML is used to address serious issues such as real-time attack detection, data leakage vulnerability assessments and many more. ML extensively supports the demanding requirements of the current scenario of security and privacy across a range of areas such as real-time decision-making, big data processing, reduced cycle time for learning, cost-efficiency and error-free processing. Therefore, in this paper, we review the state of the art approaches where ML is applicable more effectively to fulfill current real-world requirements in security. We examine different security applications’ perspectives where ML models play an essential role and compare, with different possible dimensions, their accuracy results. By analyzing ML algorithms in security application it provides a blueprint for an interdisciplinary research area. Even with the use of current sophisticated technology and tools, attackers can evade the ML models by committing adversarial attacks. Therefore, requirements rise to assess the vulnerability in the ML models to cope up with the adversarial attacks at the time of development. Accordingly, as a supplement to this point, we also analyze the different types of adversarial attacks on the ML models. To give proper visualization of security properties, we have represented the threat model and defense strategies against adversarial attack methods. Moreover, we illustrate the adversarial attacks based on the attackers’ knowledge about the model and addressed the point of the model at which possible attacks may be committed. Finally, we also investigate different types of properties of the adversarial attacks.

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Section: Privacy Preservationmentioning

confidence: 99%

Applications in Security and Evasions in Machine Learning: A Survey

2020

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“…6 For privacy protection, users often do not want to expose their personal information. 7,8 For example, some users do not want to reveal that they have been to the hospital, or some users deliberately hide their experience of having been to the bar. Users act in this way to protect their privacy and avoid check-in, which leads to the sparse check-in.…”

Section: Introductionmentioning

confidence: 99%

“…Privacy protection 6 . For privacy protection, users often do not want to expose their personal information 7,8 . For example, some users do not want to reveal that they have been to the hospital, or some users deliberately hide their experience of having been to the bar.…”

Section: Introductionmentioning

confidence: 99%

Bidirectional GRU networks‐based next POI category prediction for healthcare

Liu

Song

et al. 2021

Int J of Intelligent Sys

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The Corona Virus Disease 2019 has a great impact on public health and public psychology. People stay at home for a long time and rarely go out. With the improvement of the epidemic situation, people began to go to different places to check in. To maintain public mental health, it is necessary to propose a point‐of‐interest (POI) prediction model which can mine users' interests. However, the current techniques suffer from lower precision during prediction and the practical value is poor, which is due to the sparse data of users' check‐in. Faced with this challenge, we propose an attention‐based bidirectional gated recurrent unit (GRU) model for POI category prediction (ABG_poic). We regard the user's POI category as the user's interest preference because the fuzzy POI category is easier to reflect the user's interest than the POI. This method can alleviate the data sparsity, and protect users' location privacy. Since users' preferences are variable, we utilize a bidirectional GRU to capture the dynamic dependence of users' check‐ins. Furthermore, since the neural network is similar to a “black box” in feature learning, the decision‐making stage is opaque. Thus, we combine the attention mechanism with bidirectional GRU to selectively focus on historical check‐in records, which can improve the interpretability of the model. Considering the time impact on users' check‐in, we utilize the time sliding window in the ABG_poic model. Experiments on two data sets demonstrate that our ABG_poic outperforms the comparison models for POI category prediction on sparse check‐in data.

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“…Jing Li et al, they proposed a verifiable chebyshev maps-based chaotic encryption schemes with outsourcing computations in the cloud/fog scenarios [32]. Jin Li, Xiaofeng Chen and Sherman S. M. Chow proposed multi-authority fine-grained access control with accountability approach for cloud computing [33]. A privacy-preserving naive bayesian classifier, secure against the substitution-then-comparison attack, was presented by Chong-zhi Gao, Qiong Cheng, and Pei He [34]; we could adapt the privacy-preserving technique into our method in the future.…”

Section: Introductionmentioning

confidence: 99%

A DRDoS Detection and Defense Method Based on Deep Forest in the Big Data Environment

Cheng

Wang

et al. 2019

Symmetry

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Distributed Denial of Service (DDoS) has developed multiple variants, one of which is Distributed Reflective Denial of Service (DRDoS). With the increasing number of Internet of Things (IoT) devices, the threat of DRDoS attack is growing, and the damage of a DRDoS attack is more destructive than other types. The existing DDoS detection methods cannot be generalized in DRDoS early detection, which leads to heavy load or degradation of service when deployed at the final point. In this paper, we propose a DRDoS detection and defense method based on deep forest model (DDDF), and then we integrate differentiated service into defense model to filter out DRDoS attack flow. Firstly, from the statistics perspective on different stages of DRDoS attack flow in the big data environment, we extract a host-based DRDoS threat index (HDTI) from the network flows. Secondly, using the HDTI feature we build a DRDoS detection and defense model based on the deep forest, which consists of 1 extreme gradient boost (XGBoost) forest estimator, 2 random forest estimators, and 2 extra random forest estimators in each layer. Lastly, the differentiated service procedure applies the detection result from DDDF to drop the traffic identified in different stages and different detection points. Theoretical analysis and experiments show that the method we proposed can effectively identify DRDoS attack with higher detection rate and a lower false alarm rate, the defense model also shows distinguishing ability to effectively eliminate the DRDoS attack flows, and dramatically mitigate the damage of a DRDoS attack.Symmetry 2019, 11, 78 2 of 22 access requests, and the internal server or devices reflecting more packets, the DRDoS attack brings the danger of congestion effectively, and the high accessibility of conducting such attacks is achieved due to the easy-use tools and relatively low cost.In 26 September 2016, and 21 October 2016, the network in the United States was also attacked by DRDoS, the attackers unitized tens of millions of webcams and digital video recorders (DVR), sending packets to the Internet service provider (ISP), causing over 1200 websites, including Twitter, Amazon, Reddit, and Netflix to be inaccessible for millions of Internet users. Moreover, the DRDoS attacks witnessed this year have the trend to be more dedicated and sophisticated with higher diversities, as a consequence, a swift, smart and solid cyber-attack detection mechanism is needed for security control for the increasingly vulnerable network.The rest of the paper is organized as follows. Related work is discussed in Section 2. In Section 3, we analyze the feature of general type DRDoS attack as the base theorem for the method we proposed in Section 4, where we will introduce the algorithm of the DRDoS detection method and characterized each part of it. We will then introduce the deep forest model for classification and apply differentiated service in the defense method. And we define differentiated service as a procedure that implements a simple and scalable mechani...

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Verifiable Chebyshev maps‐based chaotic encryption schemes with outsourcing computations in the cloud/fog scenarios

Cited by 16 publications

References 35 publications

Applications in Security and Evasions in Machine Learning: A Survey

Applications in Security and Evasions in Machine Learning: A Survey

Bidirectional GRU networks‐based next POI category prediction for healthcare

A DRDoS Detection and Defense Method Based on Deep Forest in the Big Data Environment

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