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学者姓名：何泾沙
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Abstract ：
Privacy leakage resulting from information sharing in online social networks (OSNs) is a serious concern for individuals. One of the culprits behind the problem is that existing privacy policies developed for OSNs are not fine-grained or flexible enough, resulting in privacy settings that could hardly meet the privacy requirements of individuals. Neither would such privacy settings allow individuals to control where the information could go. In addition, there are hardly any effective mechanisms for measuring potential threats to privacy during information propagation. To alleviate the situation, in this paper, we propose a novel privacy-preserving information sharing scheme for OSNs in which information flow can be controlled according to the privacy requirements of the information owner and the context of the information flow. Specifically, we first formally define the privacy-dependent condition (PDC) for information sharing in OSNs and then design a PDC-based privacy-preserving information sharing scheme (PDC-InfoSharing) to protect the privacy of individuals according to the heterogeneous privacy requirements of individuals as well as the potential threats that they may face. Furthermore, to balance information sharing and privacy protection, the techniques of reinforcement learning is utilized to help individuals reach a trade-off. PDC-InfoSharing would allow the privacy policies for specific information audience to be derived based on PDC to achieve dynamical control of the flow of information. Theoretical analysis proves that the proposed scheme can assist individuals in adopting fine-grained privacy policies and experiment shows that it can adapt to different situations to help individuals achieve the trade-off between information sharing and privacy protection.
Keyword ：
Multi-armed bandit Multi-armed bandit Prospect theory Prospect theory Privacy protection Privacy protection Information sharing Information sharing Online social networks Online social networks
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| GB/T 7714 | Yi, Yuzi , Zhu, Nafei , He, Jingsha et al. A privacy-dependent condition-based privacy-preserving information sharing scheme in online social networks [J]. | COMPUTER COMMUNICATIONS , 2023 , 200 : 149-160 . |
| MLA | Yi, Yuzi et al. "A privacy-dependent condition-based privacy-preserving information sharing scheme in online social networks" . | COMPUTER COMMUNICATIONS 200 (2023) : 149-160 . |
| APA | Yi, Yuzi , Zhu, Nafei , He, Jingsha , Jurcut, Anca Delia , Ma, Xiangjun , Luo, Yehong . A privacy-dependent condition-based privacy-preserving information sharing scheme in online social networks . | COMPUTER COMMUNICATIONS , 2023 , 200 , 149-160 . |
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Abstract ：
Positive developments in smartphone usage have led to an increase in malicious attacks, particularly targeting Android mobile devices. Android has been a primary target for malware exploiting security vulnerabilities due to the presence of critical applications, such as banking applications. Several machine learning-based models for mobile malware detection have been developed recently, but significant research is needed to achieve optimal efficiency and performance. The proliferation of Android devices and the increasing threat of mobile malware have made it imperative to develop effective methods for detecting malicious apps. This study proposes a robust hybrid deep learning-based approach for detecting and predicting Android malware that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). It also presents a creative machine learning-based strategy for dealing with unbalanced datasets, which can mislead the training algorithm during classification. The proposed strategy helps to improve method performance and mitigate over- and under-fitting concerns. The proposed model effectively detects Android malware. It extracts both temporal and spatial features from the dataset. A well-known Drebin dataset was used to train and evaluate the efficacy of all creative frameworks regarding the accuracy, sensitivity, MAE, RMSE, and AUC. The empirical finding proclaims the projected hybrid ConvLSTM model achieved remarkable performance with an accuracy of 0.99, a sensitivity of 0.99, and an AUC of 0.99. The proposed model outperforms standard machine learning-based algorithms in detecting malicious apps and provides a promising framework for real-time Android malware detection.
Keyword ：
LSTM LSTM Android malware detection Android malware detection CNN CNN Drebin dataset Drebin dataset deep learning deep learning
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| GB/T 7714 | Wajahat, Ahsan , He, Jingsha , Zhu, Nafei et al. An adaptive semi-supervised deep learning-based framework for the detection of Android malware [J]. | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS , 2023 , 45 (3) : 5141-5157 . |
| MLA | Wajahat, Ahsan et al. "An adaptive semi-supervised deep learning-based framework for the detection of Android malware" . | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 45 . 3 (2023) : 5141-5157 . |
| APA | Wajahat, Ahsan , He, Jingsha , Zhu, Nafei , Mahmood, Tariq , Nazir, Ahsan , Pathan, Muhammad Salman et al. An adaptive semi-supervised deep learning-based framework for the detection of Android malware . | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS , 2023 , 45 (3) , 5141-5157 . |
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Abstract ：
The Internet of Things (IoT) has transformed many aspects of modern life, from healthcare and transportation to home automation and industrial control systems. However, the increasing number of connected devices has also led to an increase in security threats, particularly from botnets. To mitigate these threats, various machine learning (ML) and deep learning (DL) techniques have been proposed for IoT botnet attack detection. This systematic review aims to identify the most effective ML and DL techniques for detecting IoT botnets by delving into benchmark datasets, evaluation metrics, and data pre-processing techniques in detail. A comprehensive search was conducted in multiple databases for primary studies published between 2018 and 2023. Studies were included if they reported the use of ML or DL techniques for IoT botnet detection. After screening 1,567 records, 25 studies were included in the final review. The findings suggest that ML and DL techniques show promising results in detecting IoT botnet attacks, outperforming traditional signature-based methods. However, the effectiveness of the techniques varied depending on the dataset, features, and evaluation metrics used. Based on the synthesis of the findings, this review proposes a taxonomy for ML and DL techniques in IoT botnet attack detection and provides recommendations for future research in this area. This review illuminates the considerable potential of ML and DL approaches in bolstering the detection of IoT botnet attacks, thereby offering valuable insights to researchers involved in the domain of IoT security.
Keyword ：
Deep learning Deep learning Machine learning Machine learning Systematic review Systematic review IoT security IoT security IoT botnet detection IoT botnet detection Internet of things Internet of things
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| GB/T 7714 | Nazir, Ahsan , He, Jingsha , Zhu, Nafei et al. Advancing IoT security: A systematic review of machine learning approaches for the detection of IoT botnets [J]. | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES , 2023 , 35 (10) . |
| MLA | Nazir, Ahsan et al. "Advancing IoT security: A systematic review of machine learning approaches for the detection of IoT botnets" . | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 35 . 10 (2023) . |
| APA | Nazir, Ahsan , He, Jingsha , Zhu, Nafei , Wajahat, Ahsan , Ma, Xiangjun , Ullah, Faheem et al. Advancing IoT security: A systematic review of machine learning approaches for the detection of IoT botnets . | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES , 2023 , 35 (10) . |
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Abstract ：
The integration of information systems and physical systems is the development trend of today's manufacturing industry. Intelligent manufacturing is a new model of manufacturing, based on advanced manufacturing technology with human-machine-material collaboration. Internet of Things technology is the core technology of intelligent manufacturing, and access control technology is one of the main measures to ensure the security of the IoT. In view of the problem that the existing IoT access control model does not support distributed and fine-grained dynamic access control, this paper uses the characteristics of blockchain, such as decentralization and non-tampering, combined with the attribute-based access control (ABAC) method, to propose a distributed access control method, applicable to the IoT environment in the process of intelligent manufacturing. This paper describes a fine-grained access control policy by defining the access control attribute values in a formal language, which supports complex logic operations in the policy and enhances the expressiveness of the model. Distributed access control decision making, using smart contracts for blockchain, improves the decision-making efficiency of the access control model, increases the post-facto audit of the access control behavior, and improves the overall security of IoT data protection. The paper concludes with proof of security and a performance analysis, and the experimental results, such as storage and computing overheads, show that this method can provide fine-grained, dynamic, and distributed access control for devices in intelligent manufacturing, ensuring the security and reliability of access control for IoT devices.
Keyword ：
blockchain blockchain access control access control IoT IoT intelligent manufacturing intelligent manufacturing
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| GB/T 7714 | Zhai, Peng , He, Jingsha , Zhu, Nafei . Blockchain-Based Internet of Things Access Control Technology in Intelligent Manufacturing [J]. | APPLIED SCIENCES-BASEL , 2022 , 12 (7) . |
| MLA | Zhai, Peng et al. "Blockchain-Based Internet of Things Access Control Technology in Intelligent Manufacturing" . | APPLIED SCIENCES-BASEL 12 . 7 (2022) . |
| APA | Zhai, Peng , He, Jingsha , Zhu, Nafei . Blockchain-Based Internet of Things Access Control Technology in Intelligent Manufacturing . | APPLIED SCIENCES-BASEL , 2022 , 12 (7) . |
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Abstract ：
K-anonymity privacy protection model demonstrates good performance in privacy pro-tection and, has been widely applied in such scenarios as data publishing, location-based services, and social networks. With the aim of ensuring k-anonymity to conform to the requirements of pri-vacy protection with improved data utilization, this study proposes a k-anonymity algorithm based on central point clustering, so as to improve the quality of clustering through optimizing the selection of cluster centroids, leading to the improvement in effectiveness and efficiency of k-anonymity. After clustering, the quasi-identifier attributes are aligned for classification and generalization, which is evaluated using appropriate information loss metrics. To measure the distance between records and between records and clusters, this study also establishes a definition of such distance that is positively correlated to the amount of information that is lost by combining the characteristics of the depth and width of the generalization hierarchy, in an effort to improve of the utility of the algorithm. The exper-imental results show that the proposed algorithm not only meets the basic anonymity requirements, but also improves data utilization compared with some prevailing algorithms.
Keyword ：
Microaggregation Microaggregation Data privacy Data privacy Privacy protection Privacy protection Information security Information security Clustering-based k-anonymity Clustering-based k-anonymity
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| GB/T 7714 | Wang, Hewen , He, Jingsha , Zhu, Nafei . Improving Data Utilization of K-anonymity through Clustering Optimization [J]. | TRANSACTIONS ON DATA PRIVACY , 2022 , 15 (3) : 177-192 . |
| MLA | Wang, Hewen et al. "Improving Data Utilization of K-anonymity through Clustering Optimization" . | TRANSACTIONS ON DATA PRIVACY 15 . 3 (2022) : 177-192 . |
| APA | Wang, Hewen , He, Jingsha , Zhu, Nafei . Improving Data Utilization of K-anonymity through Clustering Optimization . | TRANSACTIONS ON DATA PRIVACY , 2022 , 15 (3) , 177-192 . |
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Abstract ：
The growing popularity of online social networks (OSNs) in recent years has generated a lot of concern on personal privacy. One approach of protecting privacy in OSNs is to intervene in the flow of privacy information, making the study of the dynamics of privacy information propagation necessary for the design of effective privacy protection mechanisms. Although previous work on information propagation has produced some models, these models are not adequate for privacy information since they do not reflect the main characteristics of privacy information. In this paper, we propose a model for privacy information propagation. We first analyze the structural characteristics of privacy information and then design the model by incorporating these characteris-tics. A unique feature of the model is that it infers the privacy attitudes of the information recipients to the privacy concerning subject implicated in the privacy information to determine the forwarding decisions of the recipients. Thus, by mapping the heterogeneous tendency of information forwarding by the recipients to a limited number of privacy attitudes, the model can predict the decisions on forwarding privacy information and thus describe the macroscopic process of privacy information propagation. Results of the experiment based on real OSN datasets show that the proposed model can be used to learn both the scope and the trend of privacy information propagation in OSNs, demonstrating the importance of the privacy attitudes of recipients on privacy information propagation. The properties of the model are also studied through experiment to examine the impact of various factors on privacy information propagation in OSNs.
Keyword ：
Privacy attitude Privacy attitude Privacy Privacy Online social networks Online social networks Information propagation Information propagation
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| GB/T 7714 | Yi, Yuzi , Zhu, Nafei , He, Jingsha et al. Toward pragmatic modeling of privacy information propagation in online social networks [J]. | COMPUTER NETWORKS , 2022 , 219 . |
| MLA | Yi, Yuzi et al. "Toward pragmatic modeling of privacy information propagation in online social networks" . | COMPUTER NETWORKS 219 (2022) . |
| APA | Yi, Yuzi , Zhu, Nafei , He, Jingsha , Jurcut, Anca Delia , Zhao, Bin . Toward pragmatic modeling of privacy information propagation in online social networks . | COMPUTER NETWORKS , 2022 , 219 . |
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Abstract ：
基于贡献值证明(PoC)的区块链共识机制是面向知识产权保护与交易应用场景提出的一种区块链共识机制,通过计算节点用户的贡献值,由贡献值最大的节点获得新区块的记账权.然而,由于PoC会造成记账节点具有很强的确定性,一旦该节点未能正常完成记账出块,网络中其它节点将始终保持在挂起等待状态,系统将陷于停滞状态,无法继续运行.为了使PoC区块链共识机制能够适用于公有链应用场景,本文提出基于贡献值和难度值(PoC+PoW)的区块链共识机制,使选择新区块记账权的节点具备一定的不确定性,能够有效解决PoC共识机制中存在的系统运行挂起缺陷.在PoC+PoW共识机制中,节点在工作量证明(PoW)竞争中所对应数学难题的难度值根据节点的贡献值(PoC)进行动态确定,是一种对单纯基于PoC共识机制的灾备方案,以确保系统运行的可靠性.本文提出的PoC+PoW方案根据节点的贡献值排名为节点分配相应的PoW难度值,节点再通过PoW共识机制竞争记账权.引入PoW后的共识机制最大程度地尊重PoC贡献值排名,使节点的记账出块率与其贡献值成高度正比,在系统运行层面则保证记账出块率达到或无限趋近100%,有效解决PoC带来的系统运行挂起问题.本文从节点贡献值排名、相邻贡献值节点间值差以及分组方式三个角度设计PoW难度值分配算法,并通过实验验证难度值分配算法的合理性和有效性.同时,通过实验与传统PoC共识机制在记账出块时延方面进行对比分析,进一步验证了PoC+PoW方案的优越性和可行性.
Keyword ：
工作量证明 工作量证明 区块链 区块链 贡献值 贡献值 共识机制 共识机制 难度值 难度值 贡献值证明 贡献值证明
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| GB/T 7714 | 何泾沙 , 张琨 , 薛瑞昕 et al. 基于贡献值和难度值的高可靠性区块链共识机制 [J]. | 计算机学报 , 2021 , 44 (1) : 162-176 . |
| MLA | 何泾沙 et al. "基于贡献值和难度值的高可靠性区块链共识机制" . | 计算机学报 44 . 1 (2021) : 162-176 . |
| APA | 何泾沙 , 张琨 , 薛瑞昕 , 朱娜斐 , 贺鹏 , 宋洪宇 et al. 基于贡献值和难度值的高可靠性区块链共识机制 . | 计算机学报 , 2021 , 44 (1) , 162-176 . |
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Abstract ：
In the modern digital era, everyone is partially or fully integrated with cloud computing to access numerous cloud models, services, and applications. Multi-cloud is a blend of a well-known cloud model under a single umbrella to accomplish all the distinct nature and realm requirements under one service level agreement (SLA). In current era of cloud paradigm as the flood of services, applications, and data access rise over the Internet, the lack of confidentiality of the end user's credentials is rising to an alarming level. Users typically need to authenticate multiple times to get authority and access the desired services or applications. In this research, we have proposed a completely secure scheme to mitigate multiple authentications usually required from a particular user. In the proposed model, a federated trust is created between two different domains: consumer and provider. All traffic coming towards the service provider is further divided into three phases based on the concerned user's data risks. Single sign-on (SSO) and multifactor authentication (MFA) are deployed to get authentication, authorization, accountability, and availability (AAAA) to ensure the security and confidentiality of the end user's credentials. The proposed solution exploits the finding that MFA achieves a better AAAA pattern as compared to SSO.
Keyword ：
multifactor authentication multifactor authentication single sign on single sign on federated trust in multi-cloud federated trust in multi-cloud multi-cloud security multi-cloud security AAAA in multi-cloud AAAA in multi-cloud
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| GB/T 7714 | Hussain, Muhammad Iftikhar , He, Jingsha , Zhu, Nafei et al. AAAA: SSO and MFA Implementation in Multi-Cloud to Mitigate Rising Threats and Concerns Related to User Metadata [J]. | APPLIED SCIENCES-BASEL , 2021 , 11 (7) . |
| MLA | Hussain, Muhammad Iftikhar et al. "AAAA: SSO and MFA Implementation in Multi-Cloud to Mitigate Rising Threats and Concerns Related to User Metadata" . | APPLIED SCIENCES-BASEL 11 . 7 (2021) . |
| APA | Hussain, Muhammad Iftikhar , He, Jingsha , Zhu, Nafei , Sabah, Fahad , Zardari, Zulficiar Ali , Hussain, Saqib et al. AAAA: SSO and MFA Implementation in Multi-Cloud to Mitigate Rising Threats and Concerns Related to User Metadata . | APPLIED SCIENCES-BASEL , 2021 , 11 (7) . |
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Abstract ：
The rapid development of object oriented programming (OOP) technology has made it one of the mainstream programming technologies that has been widely used in the design and development of object oriented software (OOS). The inheritance, encapsulation and polymorphism properties of object-oriented language can improve the reusability, scalability and interoperability of software while increasing the difficulty of testing OOS. Researchers have proposed a variety of testing methods to test OOS among which random testing (RT) has been widely used due to its simplicity and ease of use. An OMISS-ARTsum algorithm is proposed in this paper that uses improved OMISS random test FSCS-ART with max-sum standard, which is an implementation version of fixed-sized-candidate-set ART. The OMISS-ARTsum algorithm calculates the total distance between a candidate test case and the executed test case set before the next test case is selected from the set of candidate test cases. Unlike the traditional max-sum based FSCS-ART algorithm, OMIS S-ARTsum does not calculate the distance between each executed test case and the candidate case and then sum up the total distance, but uses the method of summing up all the executed test cases and the candidate cases. The information of executing test cases is saved as a whole and the distance between the executed test case set and candidate cases is calculated at the same time. Experiment shows that compared to the OMISS-ART algorithm, the proposed OMISS-ARTsum algorithm can reduce the time overhead.
Keyword ：
test input test input Object oriented software Object oriented software adaptive random testing adaptive random testing time cost time cost
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| GB/T 7714 | Zhou, Yue , Wang, Xiujun , Guo, Shu et al. A cost-effective adaptive random testing algorithm for object-oriented software testing [J]. | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS , 2021 , 41 (3) : 4415-4423 . |
| MLA | Zhou, Yue et al. "A cost-effective adaptive random testing algorithm for object-oriented software testing" . | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 41 . 3 (2021) : 4415-4423 . |
| APA | Zhou, Yue , Wang, Xiujun , Guo, Shu , Wen, Yi , He, Jingsha . A cost-effective adaptive random testing algorithm for object-oriented software testing . | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS , 2021 , 41 (3) , 4415-4423 . |
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Abstract ：
Privacy protection has received a lot of attention in recent years since in the era of big data, abundant information about individuals can be easily acquired. Meanwhile, as a prerequisite for effective privacy protection, the measurement of privacy disclosure is essential. Although some work has been done on the evaluation of privacy disclosure via quantification for the protection of privacy, not much attention has been placed on exploring the relationships between privacy information, resulting in underestimation, if not ill-formed reasoning, of privacy disclosure. In this paper, we propose an ontology-based approach to measure privacy disclosure by exploring the relationships between privacy information based on the WordNet. We first propose an algorithm for deriving or measuring privacy disclosure based on a set of words or concepts from text data related to individuals to ensure that the disclosure of certain user privacy can still be deduced and measured even if the set of words or concepts don't seem to be much related to it. We then perform a set of experiment by applying the proposed algorithm to some public information of ten public figures from different walks of life to evaluate the effectiveness of the algorithm and to shed some light on the characteristics of privacy disclosure in the real world in the era of big data. The work can thus serve as the foundation for the development of mechanisms for limiting or reducing privacy disclosure to achieve better protection of individual privacy.
Keyword ：
privacy quantification privacy quantification Ontology Ontology Privacy Privacy Privacy disclosure Privacy disclosure
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| GB/T 7714 | Zhu, Nafei , Chen, Baocun , Wang, Siyu et al. Ontology-Based Approach for the Measurement of Privacy Disclosure [J]. | INFORMATION SYSTEMS FRONTIERS , 2021 , 24 (5) : 1689-1707 . |
| MLA | Zhu, Nafei et al. "Ontology-Based Approach for the Measurement of Privacy Disclosure" . | INFORMATION SYSTEMS FRONTIERS 24 . 5 (2021) : 1689-1707 . |
| APA | Zhu, Nafei , Chen, Baocun , Wang, Siyu , Teng, Da , He, Jingsha . Ontology-Based Approach for the Measurement of Privacy Disclosure . | INFORMATION SYSTEMS FRONTIERS , 2021 , 24 (5) , 1689-1707 . |
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