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学者姓名：何泾沙
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摘要 ：
The rapid expansion of Internet of Things (IoT) devices has led to an escalation in security vulnerabilities, particularly concerning botnet attacks. Securing IoT networks against botnet threats is paramount to preserving network integrity and safeguarding sensitive data. Despite advancements in security measures, traditional methods often fall short in effectively detecting and mitigating botnet activity in IoT environments. There is a pressing need for robust and adaptive detection mechanisms capable of accurately identifying botnet behavior amidst the complexity of IoT network traffic. This research addresses the challenge of botnet detection in IoT networks, aiming to develop an effective and scalable solution that can accurately discern between benign and IoT botnets. To address this problem, we propose the use of ensemble learning techniques for the IoT botnet detection. Leveraging the N-BaIoT dataset, which offers real-world IoT traffic data, we apply the Voting Classifier to nine distinct IoT devices and evaluate its performance against key metrics such as accuracy, precision, recall, and F1 score. Our experiments demonstrate the effectiveness of the proposed ensemble approach, achieving high accuracy with an average accuracy rate of 99.3%. Furthermore, the ensemble method exhibits strong precision, recall, and F1 scores across various IoT device types, underscoring its efficacy in accurately discerning botnet activity. This research contributes to the advancement of botnet detection in IoT networks by introducing an ensemble-based approach that offers robust and adaptive detection capabilities. Our findings highlight the potential of ensemble learning techniques in enhancing security measures in IoT environments.
关键词 ：
Machine Learning Machine Learning Threat Detection Threat Detection Artificial Intelligence Artificial Intelligence Ensemble Learning Ensemble Learning IoT Security IoT Security
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| GB/T 7714 | Nazir, Ahsan , He, Jingsha , Zhu, Nafei et al. Ensemble Learning Techniques for the Detection of IoT Botnets [J]. | PROCEEDINGS OF 2024 3RD INTERNATIONAL CONFERENCE ON CYBER SECURITY, ARTIFICIAL INTELLIGENCE AND DIGITAL ECONOMY, CSAIDE 2024 , 2024 : 80-85 . |
| MLA | Nazir, Ahsan et al. "Ensemble Learning Techniques for the Detection of IoT Botnets" . | PROCEEDINGS OF 2024 3RD INTERNATIONAL CONFERENCE ON CYBER SECURITY, ARTIFICIAL INTELLIGENCE AND DIGITAL ECONOMY, CSAIDE 2024 (2024) : 80-85 . |
| APA | Nazir, Ahsan , He, Jingsha , Zhu, Nafei , Ma, Xiangjun , Ullah, Faheem , Qureshi, Siraj Uddin et al. Ensemble Learning Techniques for the Detection of IoT Botnets . | PROCEEDINGS OF 2024 3RD INTERNATIONAL CONFERENCE ON CYBER SECURITY, ARTIFICIAL INTELLIGENCE AND DIGITAL ECONOMY, CSAIDE 2024 , 2024 , 80-85 . |
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摘要 ：
Privacy inference poses a significant threat to users of online social networks (OSNs). To deal with this issue, a number of privacy-enhancing technologies have been proposed with the goal of achieving a balance between the protection of privacy and the utility of data. Previous studies, however, failed to take into consideration the impact of the interdependency of privacy (IoP), which dictates that privacy decisions made by some users may affect the privacy of some other users. The implication of IoP is that too much privacy may be disclosed when multiple individuals share data with the same data accessor because privacy conflicts resulting from independent privacy decisions would make it possible for adversaries to infer the privacy of the target user. Ideally, cooperation that preserves privacy should allow OSN users to respect each other's privacy specifications so as to resolve such privacy conflicts caused by independent privacy decisions of individuals. To facilitate the design, we propose a privacy-preserving cooperation framework based on the evolutionary game theory to facilitate such cooperation. Based on the framework, the dynamics of user strategies regarding whether to participate in the cooperation are analyzed and an evolutionary stable state is derived to serve as the basis for incentivizing users to participate in cooperative privacy protection. Experiments based on real OSN data show that the proposed cooperation framework is effective in modeling the behaviors of users and that the proposed incentive allocation method can incentivize users to participate in the cooperation. The proposed cooperation framework can not only helps lower the threat to user privacy resulting from privacy inference by data accessors but also allows OSN service providers to design effective privacy protection policies.
关键词 ：
Game theory Game theory Privacy Privacy online social network (OSN) online social network (OSN) privacy protection privacy protection Evolutionary game theory Evolutionary game theory privacy inferences privacy inferences Behavioral sciences Behavioral sciences Data privacy Data privacy Games Games Sociology Sociology Data models Data models
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| GB/T 7714 | Yi, Yuzi , Zhu, Nafei , He, Jingsha et al. An Evolutionary Game Theory-Based Cooperation Framework for Countering Privacy Inference Attacks [J]. | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2024 , 11 (3) : 4367-4384 . |
| MLA | Yi, Yuzi et al. "An Evolutionary Game Theory-Based Cooperation Framework for Countering Privacy Inference Attacks" . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 11 . 3 (2024) : 4367-4384 . |
| APA | Yi, Yuzi , Zhu, Nafei , He, Jingsha , Jurcut, Anca Delia , Ma, Xiangjun , Luo, Yehong . An Evolutionary Game Theory-Based Cooperation Framework for Countering Privacy Inference Attacks . | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS , 2024 , 11 (3) , 4367-4384 . |
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摘要 ：
Ensuring robust security in the Internet of Things (IoT) landscape is of paramount importance. This research article presents a novel approach to enhance IoT security by leveraging collaborative threat intelligence and integrating blockchain technology with machine learning (ML) models. The iOS application acts as a central control centre, facilitating the reporting and sharing of detected threats. The shared threat data is securely stored on a blockchain network, enabling ML models to access and learn from a diverse range of threat scenarios. The research focuses on implementing Random Forest, Decision Tree classifier, Ensemble, LSTM, and CNN models on the IoT23 dataset within the context of a Collaborative Threat Intelligence Framework for IoT Security. Through an iterative process, the models' accuracy is improved by reducing false negatives through the collaborative threat intelligence system. The article investigates the implementation details, privacy considerations, and the seamless integration of ML -based techniques for continuous model improvement. Experimental evaluations on the IoT23 dataset demonstrate the effectiveness of the proposed system in enhancing IoT security and mitigating potential threats. The research contributes to the advancement of collaborative threat intelligence and blockchain technology in the context of IoT security, paving the way for more secure and reliable IoT deployments.
关键词 ：
Internet of Things Internet of Things IoT security IoT security iOS iOS Machine learning Machine learning Ensemble learning Ensemble learning BlockChain BlockChain
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| GB/T 7714 | Nazir, Ahsan , He, Jingsha , Zhu, Nafei et al. Collaborative threat intelligence: Enhancing IoT security through blockchain and machine learning integration [J]. | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES , 2024 , 36 (2) . |
| MLA | Nazir, Ahsan et al. "Collaborative threat intelligence: Enhancing IoT security through blockchain and machine learning integration" . | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 36 . 2 (2024) . |
| APA | Nazir, Ahsan , He, Jingsha , Zhu, Nafei , Wajahat, Ahsan , Ullah, Faheem , Qureshi, Sirajuddin et al. Collaborative threat intelligence: Enhancing IoT security through blockchain and machine learning integration . | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES , 2024 , 36 (2) . |
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摘要 ：
In the realm of modern healthcare, Electronic Health Records EHR serve as invaluable assets, yet they also pose significant security challenges. The absence of EHR access auditing mechanisms, which includes the EHR audit trails, results in accountability gaps and magnifies security vulnerabilities. This situation effectively paves the way for unauthorized data alterations to occur without detection or consequences. Inadequate EHR compliance auditing procedures, particularly in verifying and validating access control policies, expose healthcare organizations to risks such as data breaches, and unauthorized data usage. These vulnerabilities result from unchecked unauthorized access activities. Additionally, the absence of EHR audit logs complicates investigations, weakens proactive security measures, and raises concerns to put healthcare institutions at risk. This study addresses the pressing need for robust EHR auditing systems designed to scrutinize access to EHR data, encompassing who accesses it, when, and for what purpose. Our research delves into the complex field of EHR auditing, which includes establishing an immutable audit trail to enhance data security through blockchain technology. We also integrate Purpose-Based Access Control ( PBAC ) alongside smart contracts to strengthen compliance auditing by validating access legitimacy and reducing unauthorized entries. Our contributions encompass the creation of audit trail of EHR access, compliance auditing via PBAC policy verification, the generation of audit logs, and the derivation of data-driven insights, fortifying EHR access security.
关键词 ：
EHR access control policy verification EHR access control policy verification EHR audit logs EHR audit logs Access control pattern Access control pattern Smart contract Smart contract EHR audit trail EHR audit trail Purpose-based access control Purpose-based access control
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| GB/T 7714 | Ullah, Faheem , He, Jingsha , Zhu, Nafei et al. Blockchain-enabled EHR access auditing: Enhancing healthcare data security [J]. | HELIYON , 2024 , 10 (16) . |
| MLA | Ullah, Faheem et al. "Blockchain-enabled EHR access auditing: Enhancing healthcare data security" . | HELIYON 10 . 16 (2024) . |
| APA | Ullah, Faheem , He, Jingsha , Zhu, Nafei , Nazir, Ahsan , Qureshi, Sirajuddin , Pathan, Muhammad Salman et al. Blockchain-enabled EHR access auditing: Enhancing healthcare data security . | HELIYON , 2024 , 10 (16) . |
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摘要 ：
Driven by the rapid development of information technology, online social networks (OSNs) have experienced a fast development in recent years, allowing increasingly more people to share and spread information over OSNs. The rapid rise of OSN platforms such as Facebook and Twitter is sufficient evidence of such development. As one type of information, privacy information can also be created and disseminated over an OSN, posing a severe threat to individual privacy. This article attempts to construct a model for disseminating privacy information in OSNs and to analyze the model by simulating the dissemination process of privacy information in OSNs. First, we establish network models that exhibit the main characteristics of OSNs. Second, by considering the factors related to social relationships, especially intimacy between users and the attention of users to the privacy subject, we derive the parameters for privacy information dissemination models in OSNs. Third, based on the theory of information dissemination dynamics, we construct a model for information dissemination that conforms to the properties of privacy information. We also present some experimental results based on the constructed model and analyze the characteristics of privacy information dissemination. Fourth, we study and verify the various properties of the model through a set of experiments. The proposed model provides the opportunity to better understand the dynamics of privacy information dissemination in OSNs and the effect of user behavior on dissemination. In this paper, we constructed a privacy information dissemination model for OSNs by considering factors that affect the dissemination. Our experiment and analysis indicated that network structures would affect the dissemination of private information and privacy information can often be disseminated very fast and spread very widely. image
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| GB/T 7714 | Zhu, Nafei , Li, Wenhui , Pan, Shijia et al. Modeling the dissemination of privacy information in online social networks [J]. | TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES , 2024 , 35 (6) . |
| MLA | Zhu, Nafei et al. "Modeling the dissemination of privacy information in online social networks" . | TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 35 . 6 (2024) . |
| APA | Zhu, Nafei , Li, Wenhui , Pan, Shijia , Jin, Shuting , He, Jingsha . Modeling the dissemination of privacy information in online social networks . | TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES , 2024 , 35 (6) . |
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摘要 ：
Intelligent transportation systems (ITSs) make it possible for applications such as autonomous driving, active safety systems and smart cities. As the infrastructure of ITSs, vehicular ad-hoc network (VANET) plays a key role in ensuring traffic safety while improving driving experience and comfort. However, VANET faces many challenges as the network is exposed to the public and involves sensitive information such as vehicle control commands and driving records. Although blockchain technology can provide such features as decentralization, immutability, non-reliance on trust and traceability, most of the existing blockchain systems impose high storage and computing capacity requirements for participating nodes while terminals nodes in VANET, such as road-side units (RSUs), on-board units (OBUs) and sensors, usually have very limited storage and computing capacity. To solve this problem, this paper proposes a blockchain system that provides scalable storage capacity for VANETs. The proposed scheme uses network sharding and multi-consensus strategy to improve the topology and consensus process of the blockchain. Aimed at lowering the storage requirement on the RSU nodes that participate in the blockchain, a collaborative storage mechanism and a dynamic copy number strategy for the blockchain ledger is designed. Experimental results show that compared to existing schemes, the proposed scalable blockchain storage scheme can significantly lower the storage requirement for the blockchain nodes, thus making it possible for the nodes with limited storage capacity in VANET to participate in the maintenance of the blockchain to prevent the blockchain from drifting towards centralization, and support maximal sharing of road traffic information while ensuring the security, privacy and trustworthiness of information.
关键词 ：
Dynamic copy number Dynamic copy number Blockchain Blockchain Sharding Sharding Collaborative ledger storage Collaborative ledger storage VANET VANET
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| GB/T 7714 | Wei, Wenxiang , Zhu, Nafei , Wang, Jian et al. A scalable blockchain storage scheme for VANET [J]. | CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS , 2024 , 27 (4) : 3957-3981 . |
| MLA | Wei, Wenxiang et al. "A scalable blockchain storage scheme for VANET" . | CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 27 . 4 (2024) : 3957-3981 . |
| APA | Wei, Wenxiang , Zhu, Nafei , Wang, Jian , Song, Hongyu , He, Jingsha . A scalable blockchain storage scheme for VANET . | CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS , 2024 , 27 (4) , 3957-3981 . |
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摘要 ：
Consensus mechanism (CM) is the heart and soul of blockchain, for it allows nodes in a blockchain network to reach an agreement on the state of system execution. Among the CMs, the right for constructing the next new block is regarded as the most important one since it is required for the blockchain to grow so that transaction data can be recorded in it. Proof-of-work (PoW) is an efficient CM since it allows all the nodes to participate equally in the competition for the right. However, high energy consumption makes PoW less desirable for applications. Moreover, application-oriented blockchains should employ CMs that could reflect some main characteristics of the applications to facilitate the development of other mechanisms, such as the incentive mechanisms. Proof-of-contribution (PoC) is an effective, application-oriented CM since the right for constructing the next new block is determined by contributions made by the nodes and the node that has accumulated the highest contribution value (CV) gets the right. PoC is a general-purpose CM since the behavior of nodes can be characterized in the form of contributions. However, the deterministic nature of PoC as the result of ranking nodes based on CVs may lower efficiency since nodes could fail to function due to network delay, node failure or node's simply dropping out of the network. This paper proposes to design highly effective and efficient CMs by integrating PoC with PoW, which we refer to as PoCW. In PoCW, nodes compete for the right for constructing the next new block based on PoW after being assigned different difficulty values (DVs) based on the ranking of their CVs. Since assigning DVs strictly according to the ranking of CVs would make PoCW resemble PoC while assigning the same DV to all the nodes would make PoCW the same as PoW, PoCW could be designed as a class of CMs through applying different DV assignment strategies to meet the effectiveness and efficiency requirements of a variety of applications. We can further apply the same principle at finer levels of granularity by dynamically grouping nodes along the ranking of CVs and then applying different DV assignment strategies to different groups. The paper will first describe a generic PoCW without involving node grouping and then present the design of a general PoCW through applying an example node grouping method to demonstrate the feasibility of PoCW as a general-purpose CM for blockchain-based applications. Experiments were also conducted to demonstrate the effectiveness and the efficiency of PoCW as well as its advantages over comparable CMs.
关键词 ：
Consensus mechanism Consensus mechanism Contribution value Contribution value Blockchain Blockchain PoC PoC Dynamic grouping Dynamic grouping PoW PoW Difficulty value Difficulty value
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| GB/T 7714 | Zhu, Nafei , Yang, Yue , Du, Weidong et al. Toward designing highly effective and efficient consensus mechanisms for blockchain-based applications [J]. | CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS , 2024 , 27 (5) : 5677-5698 . |
| MLA | Zhu, Nafei et al. "Toward designing highly effective and efficient consensus mechanisms for blockchain-based applications" . | CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 27 . 5 (2024) : 5677-5698 . |
| APA | Zhu, Nafei , Yang, Yue , Du, Weidong , Gan, Yu , He, Jingsha . Toward designing highly effective and efficient consensus mechanisms for blockchain-based applications . | CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS , 2024 , 27 (5) , 5677-5698 . |
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摘要 ：
The Internet of Things (IoT) landscape is witnessing rapid growth, driven by continuous innovation and a simultaneous increase in cybersecurity threats. As these threats become more sophisticated, the imperative to fortify IoT devices against emerging vulnerabilities becomes increasingly pronounced. This research is motivated by the need for comprehensive IoT threat detection solutions that can effectively address the evolving threat landscape. Existing approaches often fall short in adapting to the dynamic nature of IoT environments and the increasing complexity of attacks. The core problem addressed in this research is the development of a novel Hybrid Convolutional Neural Network and Long Short -Term Memory (CNN-LSTM) architecture tailored for precise and efficient IoT threat detection. This architecture aims to overcome the limitations of existing methods and enhance the security of IoT ecosystems. Our study encompasses a detailed analysis of the proposed Hybrid CNN-LSTM model, leveraging data from diverse datasets, including IoT-23, N-BaIoT, and CICIDS2017. The proposed model is tested and validated on more than 14 attack types. We have designed this model to exhibit robust threat detection capabilities by effectively capturing and analyzing IoT security data. The outcomes of our research showcase remarkable accuracy, with the models achieving 95% accuracy on the IoT-23 dataset and an outstanding 99% accuracy on both the N-BaIoT and CICIDS2017 datasets. These findings underscore the model's adaptability to various IoT environments. Our research contributes a comprehensive Hybrid CNNLSTM architecture that significantly enhances IoT threat detection. We introduce Principal Component Analysis (PCA) to optimize data processing and incorporate advanced optimization techniques like model quantization and pruning to improve deployment efficiency in resource -constrained IoT environments. This study lays the foundation for future advancements in bolstering IoT security.
关键词 ：
Convolutional neural network Convolutional neural network Long short-term memory Long short-term memory Machine learning Machine learning Internet of Things Internet of Things Artificial intelligence Artificial intelligence IoT security IoT security
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| GB/T 7714 | Nazir, Ahsan , He, Jingsha , Zhu, Nafei et al. A deep learning-based novel hybrid CNN-LSTM architecture for efficient detection of threats in the IoT ecosystem [J]. | AIN SHAMS ENGINEERING JOURNAL , 2024 , 15 (7) . |
| MLA | Nazir, Ahsan et al. "A deep learning-based novel hybrid CNN-LSTM architecture for efficient detection of threats in the IoT ecosystem" . | AIN SHAMS ENGINEERING JOURNAL 15 . 7 (2024) . |
| APA | Nazir, Ahsan , He, Jingsha , Zhu, Nafei , Qureshi, Saima Siraj , Qureshi, Siraj Uddin , Ullah, Faheem et al. A deep learning-based novel hybrid CNN-LSTM architecture for efficient detection of threats in the IoT ecosystem . | AIN SHAMS ENGINEERING JOURNAL , 2024 , 15 (7) . |
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摘要 ：
The Internet of Things (IoT) has experienced significant growth in recent years and has emerged as a very dynamic sector in the worldwide market. Being an open -source platform with a substantial user base, Android has not only been a driving force in the swift advancement of the IoT but has also garnered attention from malicious actors, leading to malware attacks. Given the rapid proliferation of Android malware in recent times, there is an urgent requirement to introduce practical techniques for the detection of such malware. While current machine learning -based Android malware detection approaches have shown promising results, the majority of these methods demand extensive time and effort from malware analysts to construct dynamic or static features. Consequently, the practical application of these methods becomes challenging. Therefore, this paper presents an Android malware detection system characterized by its lightweight design and reliance on explainable machine -learning techniques. The system uses features extracted from mobile applications (apps) to distinguish between malicious and benign apps. Through extensive testing, it has exhibited exceptional accuracy and an F1 -score surpassing 0.99 while utilizing minimal device resources and presenting negligible false positive and false negative rates. Furthermore, the classifier model's transparency and comprehensibility are significantly augmented through the application of Shapley's additive explanation scores, enhancing the overall interpretability of the system.
关键词 ：
Random forest Random forest Android malware detection Android malware detection RFE RFE IoT IoT Machine learning Machine learning SHAP SHAP
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| GB/T 7714 | Wajahat, Ahsan , He, Jingsha , Zhu, Nafei et al. Securing Android IoT devices with GuardDroid transparent and lightweight malware detection [J]. | AIN SHAMS ENGINEERING JOURNAL , 2024 , 15 (5) . |
| MLA | Wajahat, Ahsan et al. "Securing Android IoT devices with GuardDroid transparent and lightweight malware detection" . | AIN SHAMS ENGINEERING JOURNAL 15 . 5 (2024) . |
| APA | Wajahat, Ahsan , He, Jingsha , Zhu, Nafei , Mahmood, Tariq , Nazir, Ahsan , Ullah, Faheem et al. Securing Android IoT devices with GuardDroid transparent and lightweight malware detection . | AIN SHAMS ENGINEERING JOURNAL , 2024 , 15 (5) . |
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摘要 ：
本发明公开了一种面向联盟链账本数据的节点协作存储方法及系统，方法包括：针对联盟链群组内的节点，计算节点性能并进行强弱排序，由排序靠前的节点轮巡作为主节点；主节点将组内产生交易收集并打包提交至共识网络；主节点同步获取新区块，根据区块编号的hash数据，将区块通过第一次映射确定对应的归置组PG；利用伪随机哈希算法对PG号、节点ID和副本数量各节点的权重得到对应节点的随机数straw值；将区块的主账本和副账本第二次映射存储至每次计算得到的straw值最大的不同节点。通过本发明的技术方案，降低了单个节点的存储压力，提升了联盟链的存储性能，同时提高了区块链的交易查询效率，提高了节点和区块的管理效率。
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| GB/T 7714 | 何泾沙 , 叶子昂 , 朱娜斐 . 一种面向联盟链账本数据的节点协作存储方法及系统 : CN202310179650.6[P]. | 2023-02-28 . |
| MLA | 何泾沙 et al. "一种面向联盟链账本数据的节点协作存储方法及系统" : CN202310179650.6. | 2023-02-28 . |
| APA | 何泾沙 , 叶子昂 , 朱娜斐 . 一种面向联盟链账本数据的节点协作存储方法及系统 : CN202310179650.6. | 2023-02-28 . |
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