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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 ：
In the digital transformation era, the Internet of Things (IoT) has become integral, propelling innovation yet exposing cybersecurity vulnerabilities and compromising system integrity. The complexity and diversity of IoT environments render traditional security measures inadequate against the evolving nature of cyber threats, especially those targeting wireless sensor networks (WSNs). This gap underscores the need for solutions that adapt alongside emerging threats to enhance IoT ecosystem resilience. Our research introduces a pioneering intrusion detection system (IDS) combining reinforcement learning (RL) and artificial neural networks (ANNs). Using a Q-learning agent, this system interacts with various IoT datasets to iteratively select and refine attack features for classification, enabling the identification of key representations that increase detection accuracy. The model can effectively identify known and emerging threats through an automated feature selection process and continuous adaptation. Extensive testing demonstrated robust performance under dynamic network conditions, positioning this approach as a scalable and resilient defense for large-scale IoT infrastructures. When tested on real-world IoT datasets across multiple contexts and threats, the proposed model achieved over 99% accuracy while maintaining a false-positive rate of less than 5 %, surpassing existing solutions and establishing a new standard for adaptive cybersecurity.
Keyword ：
Intrusion detection system Intrusion detection system Artificial neural networks Artificial neural networks Recursive feature elimination Recursive feature elimination Wireless sensor networks Wireless sensor networks Reinforcement learning Reinforcement learning
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| GB/T 7714 | Hussain, Saqib , He, Jingsha , Zhu, Nafei et al. An Adaptive Intrusion Detection System for WSN using Reinforcement Learning and Deep Classification [J]. | ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING , 2024 . |
| MLA | Hussain, Saqib et al. "An Adaptive Intrusion Detection System for WSN using Reinforcement Learning and Deep Classification" . | ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING (2024) . |
| APA | Hussain, Saqib , He, Jingsha , Zhu, Nafei , Mughal, Fahad Razaque , Hussain, Muhammad Iftikhar , Algarni, Abeer D. et al. An Adaptive Intrusion Detection System for WSN using Reinforcement Learning and Deep Classification . | ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING , 2024 . |
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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 ：
本发明公开了一种在时空群聊天场景中建立私聊的方法，包括：用户在时空群中建立私聊的方式主要有三种，第一种是在时空群中创建一级私聊会话，依赖所在时空群的时间属性和空间属性，即当时空群不存在时或者用户退出时空群时，一级私聊会话会自动终止；第二种是在时空群中创建二级私聊会话，不依赖所在的时空群，即在时空群之外，用户依然可以保持私聊通信；第三种方法是在时空群中先创建一级私聊会话，用户根据个人需求，选择申请将一级私聊会话转换为二级私聊会话。若在时空群中成功建立私聊会话，则用户可同时接收群聊消息和私聊消息，其中消息类型包括但不限于文本、语音、图片、视频、文件等。
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| GB/T 7714 | 何泾沙 , 祝明光 , 朱娜斐 et al. 一种在时空群聊天场景中建立私聊的方法 : CN202310051932.8[P]. | 2023-02-02 . |
| MLA | 何泾沙 et al. "一种在时空群聊天场景中建立私聊的方法" : CN202310051932.8. | 2023-02-02 . |
| APA | 何泾沙 , 祝明光 , 朱娜斐 , 王虎 , 蔡锦泓 , 何海洋 et al. 一种在时空群聊天场景中建立私聊的方法 : CN202310051932.8. | 2023-02-02 . |
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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 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 ：
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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本发明公开了一种基于智能合约的区块链贡献证明共识方法及系统，方法包括：基于智能合约，根据节点在区块链系统中的操作行为，为节点计算并分配系统贡献值和应用贡献值；根据参与共识的候选节点在上一周期的贡献值和/或代币的质押数量进行加权计算，根据各候选节点的权值进行离线VRF抽签计算，并根据抽签结果选举得到委员会节点；根据节点的贡献值和/或代币的质押者、质押方式和质押数量的比例进行分配奖励，同时对未积极完成共识出块任务的节点所质押的贡献值和/或代币进行扣押惩罚。通过本发明的技术方案，使区块链系统具有更高的适用性，实现了完整的贡献值积累和兑现过程，能够降低攻击和恶意节点，具有较高的可靠性。
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| GB/T 7714 | 何泾沙 , 甘宇 , 朱娜斐 et al. 基于智能合约的区块链贡献证明共识方法及系统 : CN202210161974.2[P]. | 2022-02-22 . |
| MLA | 何泾沙 et al. "基于智能合约的区块链贡献证明共识方法及系统" : CN202210161974.2. | 2022-02-22 . |
| APA | 何泾沙 , 甘宇 , 朱娜斐 , 王建 . 基于智能合约的区块链贡献证明共识方法及系统 : CN202210161974.2. | 2022-02-22 . |
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Abstract ：
本发明公开了一种基于购物评论的人格特质分析方法及系统，方法包括：获取评论数据构建评论样本数据集，并进行分词及标注，得到实验数据集；将实验数据集向量化后输入LSTM网络模型，得到情感倾向；通过SC‑LIWC词典提取的关键词计算与每个人格维度的皮尔逊相关系数；将实验数据集的特征结合皮尔逊相关系数和情感倾向，输入以KNN、NB和SVM共同构建的人格特质分析融合模型进行训练；针对所需分析的评论数据进行文本特征及情感特征提取，通过LSTM网络模型得到情感倾向量化值，并输入人格特质分析融合模型，输出多标签的人格特质属性。通过本发明的技术方案，实现了对评论数据的情感倾向量化及人格特质分类，为个性推荐提供支持。
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| GB/T 7714 | 何泾沙 , 洪睿 , 朱娜斐 et al. 一种基于购物评论的人格特质分析方法及系统 : CN202210146150.8[P]. | 2022-02-17 . |
| MLA | 何泾沙 et al. "一种基于购物评论的人格特质分析方法及系统" : CN202210146150.8. | 2022-02-17 . |
| APA | 何泾沙 , 洪睿 , 朱娜斐 , 魏巍 , 陈燕博 , 宋国正 . 一种基于购物评论的人格特质分析方法及系统 : CN202210146150.8. | 2022-02-17 . |
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