Query：
学者姓名：何泾沙
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-Author
Language
Clean All
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
Cite：
Copy from the list or Export to your reference management。
| 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) . |
| Export to | NoteExpress RIS BibTex |
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
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
Cite：
Copy from the list or Export to your reference management。
| 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) . |
| Export to | NoteExpress RIS BibTex |
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
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。
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
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。
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
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。
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
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。
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
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。
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Export
| Results： |
Selected to |
| Format： |
