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学者姓名：何泾沙
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Abstract ：
The swift proliferation of Internet of Things (IoT) devices has presented considerable challenges in maintaining cybersecurity. As IoT ecosystems expand, they increasingly attract malware attacks, necessitating advanced detection and forensic analysis methods. This systematic review explores the application of deep learning techniques for malware detection and forensic analysis within IoT environments. The literature is organized into four distinct categories: IoT Security, Malware Forensics, Deep Learning, and Anti-Forensics. Each group was analyzed individually to identify common methodologies, techniques, and outcomes. Conducted a combined analysis to synthesize the findings across these categories, highlighting overarching trends and insights.This systematic review identifies several research gaps, including the need for comprehensive IoTspecific datasets, the integration of interdisciplinary methods, scalable real-time detection solutions, and advanced countermeasures against anti-forensic techniques. The primary issue addressed is the complexity of IoT malware and the limitations of current forensic methodologies. Through a robust methodological framework, this review synthesizes findings across these categories, highlighting common methodologies and outcomes. Identifying critical areas for future investigation, this review contributes to the advancement of cybersecurity in IoT environments, offering a comprehensive framework to guide future research and practice in developing more robust and effective security solutions.
Keyword ：
Anti-forensics Anti-forensics IoT malware detection IoT malware detection Forensics techniques Forensics techniques Malware forensic analysis Malware forensic analysis
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| GB/T 7714 | Qureshi, Siraj Uddin , He, Jingsha , Tunio, Saima et al. Systematic review of deep learning solutions for malware detection and forensic analysis in IoT [J]. | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES , 2024 , 36 (8) . |
| MLA | Qureshi, Siraj Uddin et al. "Systematic review of deep learning solutions for malware detection and forensic analysis in IoT" . | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 36 . 8 (2024) . |
| APA | Qureshi, Siraj Uddin , He, Jingsha , Tunio, Saima , Zhu, Nafei , Nazir, Ahsan , Wajahat, Ahsan et al. Systematic review of deep learning solutions for malware detection and forensic analysis in IoT . | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES , 2024 , 36 (8) . |
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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 ：
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 ：
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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Smart contract has shown its potential in cutting down the cost of administration through reshaping conventional business processes and in expanding the application of blockchain to areas that are beyond the cryptocurrency. However, with the rapid development and wide application of smart contracts, security issues have become a serious concern and have thus attracted widespread attention. As the result, a great deal of effort has been spent on improving and supporting secure development and on the application of smart contracts by introducing new and advanced vulnerability detection and privacy protection techniques in recent years. There is therefore the need for a comprehensive review of the new development on security enhancement technologies of smart contracts for the blockchain. This paper provides a review of the current research status and advances in smart contract security based on related literature published in recent years. Our review is divided into six categories along the line of the technology, which includes symbolic execution, abstract interpretation, fuzz testing, formal verification, deep learning, and privacy enhancement. A comparison of the various tools and methods developed to tackle security issues is provided. Challenges in the research of smart contract security as well as future directions are discussed. This paper intends to provide the inspiration and reference for follow-up research on the security issues of smart contracts in the blockchain.
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| GB/T 7714 | Wang, Yajing , He, Jingsha , Zhu, Nafei et al. Security enhancement technologies for smart contracts in the blockchain: A survey [J]. | TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES , 2021 , 32 (12) . |
| MLA | Wang, Yajing et al. "Security enhancement technologies for smart contracts in the blockchain: A survey" . | TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 32 . 12 (2021) . |
| APA | Wang, Yajing , He, Jingsha , Zhu, Nafei , Yi, Yuzi , Zhang, Qingqing , Song, Hongyu et al. Security enhancement technologies for smart contracts in the blockchain: A survey . | TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES , 2021 , 32 (12) . |
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Abstract ：
本发明提供一种区块链存储容量优化方法及系统，涉及区块链技术领域，包括预设区块链网络的分片数N；将散列函数的值域对应分片数N平均划分为N个区间；对区块链网络中所有节点的公钥均进行散列函数计算，得到散列值；根据散列值将所有节点对应分配到不同分片；各分片内运行片内共识机制选取锚节点加入区块链网络的主链；将区块链账本以区块为单位分割成多个区块；各分片内锚节点均将每个区块分发给多个节点进行存储。本发明通过将区块链的全体节点划分为若干个独立自治的分片，使位于同一分片内的全体节点共同存储完整区块链账本的若干数量的副本，降低区块链系统对单节点存储能力的需求，进而实现区块链存储容量的优化。
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| GB/T 7714 | 何泾沙 , 宋洪宇 , 朱娜斐 et al. 一种区块链存储容量优化方法及系统 : CN202110037238.1[P]. | 2021-01-12 . |
| MLA | 何泾沙 et al. "一种区块链存储容量优化方法及系统" : CN202110037238.1. | 2021-01-12 . |
| APA | 何泾沙 , 宋洪宇 , 朱娜斐 , 薛瑞昕 , 王雅静 , 杜伟东 . 一种区块链存储容量优化方法及系统 : CN202110037238.1. | 2021-01-12 . |
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Abstract ：
本发明公开了一种基于贡献值的高可靠性区块链共识方法及系统，包括：收集区块链系统中节点的信息，为各节点分配贡献值并进行降序排序；采用“均值迁移”的动态分组方法对降序列表进行动态分组；计算分组后各组的组间难度和每组内节点的组内难度，确定各节点的PoW计算难度值；各节点根据各自的难度值开始PoW计算，将率先完成计算的节点作为本轮生成新区块的出块节点；基于出块节点外的其他节点，验证区块链系统选取的出块节点是否符合创建新区块的要求。本发明可解决现有共识机制中节点贡献值相差不大但节点获得出块权利的概率相差很大的问题，加强节点出块概率与节点贡献值的拟合程度，以及提高系统的出块效率和备灾效果。
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| GB/T 7714 | 何泾沙 , 杜伟东 , 朱娜斐 et al. 一种基于贡献值的高可靠性区块链共识方法及系统 : CN202110081985.5[P]. | 2021-01-21 . |
| MLA | 何泾沙 et al. "一种基于贡献值的高可靠性区块链共识方法及系统" : CN202110081985.5. | 2021-01-21 . |
| APA | 何泾沙 , 杜伟东 , 朱娜斐 , 甘宇 , 宋洪宇 , 薛瑞昕 et al. 一种基于贡献值的高可靠性区块链共识方法及系统 : CN202110081985.5. | 2021-01-21 . |
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