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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 ：
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.
Keyword ：
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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Abstract ：
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.
Keyword ：
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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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 ：
本发明提供一种基于注意力权重计算的差分隐私加噪方法及系统，涉及隐私保护、信息传播技术领域，包括：将待处理的敏感数据预处理为图形数据；对图形数据进行随机采样；基于该组随机采样数据中各节点的节点数据和属性数据，通过注意力机制得到所有节点的权重矩阵，并计算得到各节点的保护度权重；根据各节点的保护度权重为各节点分配隐私预算；基于随机梯度优化算法计算随机采样数据的最佳梯度，在最佳梯度上根据不同的隐私预算为不同节点的数据加入不同程度的噪声；持续对图形数据中的剩余数据进行随机采样，完成全部敏感数据的噪声的加入。本发明在保证信息的隐私性的同时，通过合理分配噪声，最大化保留数据的效用和研究价值。
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| GB/T 7714 | 何泾沙 , 常瑞天 , 朱娜斐 . 一种基于注意力权重计算的差分隐私加噪方法及系统 : CN202310117847.7[P]. | 2023-02-15 . |
| MLA | 何泾沙 et al. "一种基于注意力权重计算的差分隐私加噪方法及系统" : CN202310117847.7. | 2023-02-15 . |
| APA | 何泾沙 , 常瑞天 , 朱娜斐 . 一种基于注意力权重计算的差分隐私加噪方法及系统 : CN202310117847.7. | 2023-02-15 . |
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Abstract ：
本发明公开了一种基于联盟链的车联网数据存证方法，包括：搭建由多机构构成的联盟链；路侧单元向覆盖范围内的车辆发送数据采集请求，车辆将车辆存证记录发送至路侧单元；路侧单元对车辆存证记录验证通过后对车辆存证记录进行存储；路侧单元对车辆发送的数据进行分析，判断是否有事故发生；若有事故发生，则路侧单元根据事故发生地点以及时间对车辆提交的数据进行检索，使用收集到的数据构建事故数据存证记录树并对其哈希值进行签名后提交至联盟链账本中。本发明利用区块链技术的不可篡改性与可追溯性保证事故现场数据的完整性，实现了全流程的车联网数据自动存证。
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| GB/T 7714 | 朱娜斐 , 蔡锦泓 , 何泾沙 . 一种基于联盟链的车联网数据存证方法 : CN202310143890.0[P]. | 2023-02-21 . |
| MLA | 朱娜斐 et al. "一种基于联盟链的车联网数据存证方法" : CN202310143890.0. | 2023-02-21 . |
| APA | 朱娜斐 , 蔡锦泓 , 何泾沙 . 一种基于联盟链的车联网数据存证方法 : CN202310143890.0. | 2023-02-21 . |
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Abstract ：
本发明公开一种基于区块链的个人数据交易方法，属于区块链、数据交易和数据确权领域；该方法包括：数据持有者对原始数据进行加密，得到加密数据并上传到数据代理节点；数据购买者上传数据征集需求到数据代理节点；数据代理节点生成候选数据列表，并将候选数据列表和数据评分发送给数据购买者；数据购买者根据候选数据列表和数据评分进行数据购买，生成购买信息；数据持有者根据购买信息，生成数据集加密密钥；数据购买者根据数据集加密密钥，下载原始数据。本发明还公开一种基于区块链的个人数据交易系统。本发明能保证数据的保密性、保证数据的完整性、保证数据交易的自主性以及保证数据交易的公平性。
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| GB/T 7714 | 朱娜斐 , 姜尚东 , 何泾沙 . 一种基于区块链的个人数据交易方法及系统 : CN202310056156.0[P]. | 2023-01-17 . |
| MLA | 朱娜斐 et al. "一种基于区块链的个人数据交易方法及系统" : CN202310056156.0. | 2023-01-17 . |
| APA | 朱娜斐 , 姜尚东 , 何泾沙 . 一种基于区块链的个人数据交易方法及系统 : CN202310056156.0. | 2023-01-17 . |
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本发明公开一种面向社交网的网络传销团体检测方法，属于计算机技术、社区发现及复杂网络分析技术领域；该方法包括：获取社交网络中各个用户节点的节点特征信息；将用户节点的节点特征信息与传销文本特征库中的传销特征信息进行匹配，得到可疑用户节点；对可疑用户节点进行中心度测量，得到种子节点；根据种子节点和预设的网络节点扩展规则，得到结果社区集合；对结果社区集合进行对比和合并，得到可能网络传销团体。本发明还公开一种面向社交网的网络传销团体检测系统。本发明能深度结合传销团体和传销事件在社交网中的特点，保证了传销团体检测算法的准确性、有利于提高传销团体检测的完整性、在传销团体检测的效率和准确率上都有了进一步的提高。
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| GB/T 7714 | 何泾沙 , 吴秉权 , 朱娜斐 . 一种面向社交网的网络传销团体检测方法和系统 : CN202310389225.X[P]. | 2023-04-12 . |
| MLA | 何泾沙 et al. "一种面向社交网的网络传销团体检测方法和系统" : CN202310389225.X. | 2023-04-12 . |
| APA | 何泾沙 , 吴秉权 , 朱娜斐 . 一种面向社交网的网络传销团体检测方法和系统 : CN202310389225.X. | 2023-04-12 . |
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本发明公开了一种基于差分隐私保护机制的粒子群模糊C均值聚类方法，包括：首先，对数据集的所有数据做归一化处理，得到归一化的数据集；初始化聚类中心矩阵，并计算对应的隶属度矩阵；设定上下速度范围，并初始化速度矩阵；进入循环后根据c个聚类中心点位置和隐私预算，添加拉普拉斯噪声，并使用指数机制对优质的粒子进行选择，从而更新全局的粒子矩阵，在实现保护隐私的同时向着全局最优的方向进行进化，同时进行多次循环，输出满足差分隐私保护的聚类集合，该聚类集合即能够保护个体隐私。本发明可达到在推荐过程中防范背景知识攻击，并在进行粒子群优化的过程中对粒子群的优质粒子进行隐私化筛选的效果。
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| GB/T 7714 | 朱娜斐 , 谷雨 , 何泾沙 . 一种基于差分隐私保护机制的粒子群模糊C均值聚类方法 : CN202310374419.2[P]. | 2023-04-10 . |
| MLA | 朱娜斐 et al. "一种基于差分隐私保护机制的粒子群模糊C均值聚类方法" : CN202310374419.2. | 2023-04-10 . |
| APA | 朱娜斐 , 谷雨 , 何泾沙 . 一种基于差分隐私保护机制的粒子群模糊C均值聚类方法 : CN202310374419.2. | 2023-04-10 . |
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Abstract ：
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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