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学者姓名：杨震
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摘要 ：
The rapid growth of various vehicular apps such as automotive navigation and in-car entertainment has brought the explosion of vehicular data. Such a growth has given rise to a huge challenge of maintaining the quality of cloud storage services for the whole period of storage in vehicular networks. As a result, poor quality of services easily causes data corruption problems and thereby threats vehicular data integrity. Blockchain, a tamper-proofing technique, is considered a promising approach for mitigating data integrity risks in cloud storage. However, existing blockchain-based schemes for auditing long-term cloud data integrity suffer from poor communication performance in a vehicular network. In this study, a blockchain-based offline auditing scheme for cloud storage in the vehicular network is proposed to improve auditing performance. Inspired by the data structure of blockchain, we design an evidence chain to achieve offline auditing, which allows the cloud to spontaneously generate data integrity evidence without communicating with auditors during the evidence generation phase. Furthermore, we extend our scheme to support public and automatic validation based on the smart contract. We prove the security of the proposed scheme under the random oracle model and further provide the performance evaluation by comparing with the state-of-the-art approaches.
关键词 ：
Metadata Metadata Vehicle dynamics Vehicle dynamics Blockchains Blockchains Security Security vehicular network vehicular network Data integrity Data integrity offline auditing offline auditing Cloud storage Cloud storage blockchain blockchain Cloud computing Cloud computing Vehicular ad hoc networks Vehicular ad hoc networks
引用：
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| GB/T 7714 | Yu, Haiyang , Yang, Zhen , Tu, Shanshan et al. Blockchain-Based Offline Auditing for the Cloud in Vehicular Networks [J]. | IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT , 2022 , 19 (3) : 2944-2956 . |
| MLA | Yu, Haiyang et al. "Blockchain-Based Offline Auditing for the Cloud in Vehicular Networks" . | IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 19 . 3 (2022) : 2944-2956 . |
| APA | Yu, Haiyang , Yang, Zhen , Tu, Shanshan , Waqas, Muhammad , Liu, Huan . Blockchain-Based Offline Auditing for the Cloud in Vehicular Networks . | IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT , 2022 , 19 (3) , 2944-2956 . |
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摘要 ：
针对新冠疫情期间"停课不停学"的教学需求,分析计算机类专业课线上教学的平台及工具,结合北京工业大学的课程特点,阐述以学生为中心的创新教学实践过程,通过课程教学数据的分析,说明教学效果.
关键词 ：
自主学习 自主学习 移动互联网应用安全 移动互联网应用安全 线上实验 线上实验 线上教学 线上教学
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| GB/T 7714 | 庄俊玺 , 赖英旭 , 刘静 et al. 移动互联网应用安全课程线上教学创新实践 [J]. | 计算机教育 , 2021 , (3) : 6-9,14 . |
| MLA | 庄俊玺 et al. "移动互联网应用安全课程线上教学创新实践" . | 计算机教育 3 (2021) : 6-9,14 . |
| APA | 庄俊玺 , 赖英旭 , 刘静 , 杨震 . 移动互联网应用安全课程线上教学创新实践 . | 计算机教育 , 2021 , (3) , 6-9,14 . |
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摘要 ：
移动互联网应用安全课程线上教学创新实践
关键词 ：
移动互联网应用安全 移动互联网应用安全 线上实验 线上实验 线上教学 线上教学 自主学习 自主学习
引用：
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| GB/T 7714 | 庄俊玺 , 赖英旭 , 刘静 et al. 移动互联网应用安全课程线上教学创新实践 [J]. | 庄俊玺 , 2021 , (3) : 6-9,14 . |
| MLA | 庄俊玺 et al. "移动互联网应用安全课程线上教学创新实践" . | 庄俊玺 3 (2021) : 6-9,14 . |
| APA | 庄俊玺 , 赖英旭 , 刘静 , 杨震 , 计算机教育 . 移动互联网应用安全课程线上教学创新实践 . | 庄俊玺 , 2021 , (3) , 6-9,14 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Decentralized storage powered by blockchain is becoming a new trend that allows data owners to outsource their data to remote storage resources offered by various storage providers. Unfortunately, unqualified storage providers easily encounter unpredictable downtime due to security threats, such as malicious attacks or system failures, which is unacceptable in many real-time or data-driven applications. As a result, continuous data integrity should be guaranteed in decentralized storage, which ensures that data is intact and available for the entire storage period. However, this requires frequent checking for long time periods and incurs heavy burdens of both communication and computation, especially in a big data scenario. In this paper, we propose an efficient continuous big data integrity checking approach for decentralized storage. We design a data-time sampling strategy that randomly checks the integrity of multiple files at each time slot with high checking probability. Furthermore, to tackle the fairness problem derived from the sampling strategy, we propose a fair approach by designing an arbitration algorithm with the verifiable random function. Security analysis shows the security of our approach under the random oracle model. Evaluation and experiments demonstrate that our approach is more efficient in the big data scenario compared with the state-of-the-arts.
关键词 ：
Big data Big data Big Data Big Data Blockchain Blockchain Cloud computing Cloud computing Data integrity Data integrity data integrity checking data integrity checking decentralized storage decentralized storage Metadata Metadata sampling sampling Security Security Servers Servers verifiable random function verifiable random function
引用：
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| GB/T 7714 | Yu, Haiyang , Hu, Qi , Yang, Zhen et al. Efficient Continuous Big Data Integrity Checking for Decentralized Storage [J]. | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2021 , 8 (2) : 1658-1673 . |
| MLA | Yu, Haiyang et al. "Efficient Continuous Big Data Integrity Checking for Decentralized Storage" . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 8 . 2 (2021) : 1658-1673 . |
| APA | Yu, Haiyang , Hu, Qi , Yang, Zhen , Liu, Huan . Efficient Continuous Big Data Integrity Checking for Decentralized Storage . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2021 , 8 (2) , 1658-1673 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Automatically extracting application features from their descriptions has become an efficient way to understand user requirements and accumulate related domain knowledge. Existing approaches typically extract features based on their syntactic patterns, which may lead to lots of false positives as the sentences to be processed do not contain any features. In this paper, we first propose a POS-weighted sentence classifier based on advanced word embedding techniques to filter non-feature-containing sentences before feature extraction. Specifically, we assign different POS tags with different weights according to their importance in sentences. Secondly, we defined a group of patterns with dual constraints of POS and dependency relationship, then match phrases from each feature-containing sentence to obtain features. To evaluate the performance of our classifier, we rigorously produced a dataset with corresponding annotations. The result shows that our classifier can successfully filter out 79% of non-feature-containing sentences. Applying our method to eight applications, it outperforms the state-of-the-art approach in precision, recall, and f-measure. © 2021 Owner/Author.
关键词 ：
Classification (of information) Classification (of information) Computation theory Computation theory
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| GB/T 7714 | Zhi, Yinghao , Li, Tong , Yang, Zhen . Extracting features from app descriptions based on POS and dependency [C] . 2021 : 1354-1358 . |
| MLA | Zhi, Yinghao et al. "Extracting features from app descriptions based on POS and dependency" . (2021) : 1354-1358 . |
| APA | Zhi, Yinghao , Li, Tong , Yang, Zhen . Extracting features from app descriptions based on POS and dependency . (2021) : 1354-1358 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Sarcasm is common in social media, and people use it to express their opinions with stronger emotions indirectly. Although it belongs to a branch of sentiment analysis, traditional sentiment analysis methods cannot identify the rhetoric of irony as it requires a significant amount of background knowledge. Existing sarcasm detection approaches mainly focus on analyzing the text content of sarcasm using various natural language processing techniques. It is argued herein that the essential issue for detecting sarcasm is examining its context, including sentiments of texts that reply to the target text and user's expression habit. A dual-channel convolutional neural network is proposed that analyzes not only the semantics of the target text, but also its sentimental context. In addition, SenticNet is used to add common sense to the long short-term memory (LSTM) model. The attention mechanism is then applied to take the user's expression habits into account. A series of experiments were carried out on several public datasets, the results of which show that the proposed approach can significantly improve the performance of sarcasm detection tasks.
关键词 ：
Sentimental context Sentimental context Expression habit Expression habit Sarcasm detection Sarcasm detection Convolutional neural network Convolutional neural network Attention mechanism Attention mechanism
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| GB/T 7714 | Du, Yu , Li, Tong , Pathan, Muhammad Salman et al. An Effective Sarcasm Detection Approach Based on Sentimental Context and Individual Expression Habits [J]. | COGNITIVE COMPUTATION , 2021 , 14 (1) : 78-90 . |
| MLA | Du, Yu et al. "An Effective Sarcasm Detection Approach Based on Sentimental Context and Individual Expression Habits" . | COGNITIVE COMPUTATION 14 . 1 (2021) : 78-90 . |
| APA | Du, Yu , Li, Tong , Pathan, Muhammad Salman , Teklehaimanot, Hailay Kidu , Yang, Zhen . An Effective Sarcasm Detection Approach Based on Sentimental Context and Individual Expression Habits . | COGNITIVE COMPUTATION , 2021 , 14 (1) , 78-90 . |
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摘要 ：
With the rapid development of intelligent transportation, massive data are generated by autonomous vehicle systems and shared among vehicles through cloud servers to improve the driving experience and service quality. However, cloud servers cannot be fully trusted and may lead to serious data security challenges.
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| GB/T 7714 | Yu, Haiyang , Ma, Shuai , Hu, Qi et al. Blockchain-Based Continuous Auditing for Dynamic Data Sharing in Autonomous Vehicular Networks [J]. | COMPUTER , 2021 , 54 (8) : 33-45 . |
| MLA | Yu, Haiyang et al. "Blockchain-Based Continuous Auditing for Dynamic Data Sharing in Autonomous Vehicular Networks" . | COMPUTER 54 . 8 (2021) : 33-45 . |
| APA | Yu, Haiyang , Ma, Shuai , Hu, Qi , Yang, Zhen . Blockchain-Based Continuous Auditing for Dynamic Data Sharing in Autonomous Vehicular Networks . | COMPUTER , 2021 , 54 (8) , 33-45 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
With the fast growth of system complexity, it is increasingly difficult to comprehensively analyze security of such large-scale systems, which is a knowledge-intensive task. Although there are various available security knowledge sources, they are not well-connected with each other due to their heterogeneity and unstructured descriptions. In this paper, we propose a systematic approach to construct a comprehensive and reusable knowledge graph in the field of information security. Specifically, we first investigate heterogeneous security knowledge sources and establish a detailed ontology of information security, integrating various security conceptual models. Then, we train a security entity identifier based on active learning to extract security knowledge from unstructured descriptions. Such extracted knowledge is then fused to establish a comprehensive and reusable security knowledge graph based on the unified ontology. Finally, we illustrate the utility of our established knowledge graph with a set of exemplary queries and reasoning rules in the context of a real security scenario.
关键词 ：
Named Entity Recognition Named Entity Recognition Knowledge Graph Knowledge Graph Security Knowledge Extraction Security Knowledge Extraction Security Ontology Security Ontology Active Learning Active Learning
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| GB/T 7714 | Wang, Guodi , Li, Tong , Yue, Hao et al. Integrating Heterogeneous Security Knowledge Sources for Comprehensive Security Analysis [C] . 2021 : 714-724 . |
| MLA | Wang, Guodi et al. "Integrating Heterogeneous Security Knowledge Sources for Comprehensive Security Analysis" . (2021) : 714-724 . |
| APA | Wang, Guodi , Li, Tong , Yue, Hao , Yang, Zhen , Zhang, Runzi . Integrating Heterogeneous Security Knowledge Sources for Comprehensive Security Analysis . (2021) : 714-724 . |
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摘要 ：
Point of interest (POI) recommendation is a fundamental task in location-based social networks (LBSN). The increasing proliferation of LBSNs brings about considerable amounts of user-generated check-in data. Such data can significantly contribute to understanding user behaviors, based on which personalized recommendations can be efficiently derived. Spatial and temporal effects are crucial factors in the user's decision-making for choosing a POI to visit. Most existing methods treat them as two independent features and cannot accurately capture users' interests. We argue that spatial and temporal effects should be analyzed simultaneously in POI recommendations. To this end, we propose a S patioT emporal heterogeneous information Network (HIN)-based PO I RE commendation model (STORE) to model various heterogeneous context features, e.g., the joint spatiotemporal effects, types of POI, and social relations. Specifically, we defined the spatiotemporal effects entity (St) in HIN to model the joint spatiotemporal effects. Instead of modeling the traditional two-way interaction , we further design a four-way neural interaction model . In this way, our model can effectively mine and extract useful information from the meta-path-based context and spatiotemporal effects, thereby improving recommendation performance. We conduct extensive experiments on two real-world datasets, and the results demonstrate that the STORE model outperforms the best baseline by about 12% in NDCG@5 and 11% in Rec@5.
关键词 ：
Heterogeneous information network Heterogeneous information network LBSN LBSN POI recommendation POI recommendation Spatiotemporal effects Spatiotemporal effects
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| GB/T 7714 | Liu, Yongheng , Yang, Zhen , Li, Tong et al. A novel POI recommendation model based on joint spatiotemporal effects and four-way interaction [J]. | APPLIED INTELLIGENCE , 2021 , 52 (5) : 5310-5324 . |
| MLA | Liu, Yongheng et al. "A novel POI recommendation model based on joint spatiotemporal effects and four-way interaction" . | APPLIED INTELLIGENCE 52 . 5 (2021) : 5310-5324 . |
| APA | Liu, Yongheng , Yang, Zhen , Li, Tong , Wu, Di . A novel POI recommendation model based on joint spatiotemporal effects and four-way interaction . | APPLIED INTELLIGENCE , 2021 , 52 (5) , 5310-5324 . |
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摘要 ：
In recent years, there have been numerous cyber security issues that have caused considerable damage to the society. The development of efficient and reliable Intrusion Detection Systems (IDSs) is an effective countermeasure against the growing cyber threats. In modern high-bandwidth, large-scale network environments, traditional IDSs suffer from a high rate of missed and false alarms. Researchers have introduced machine learning techniques into intrusion detection with good results. However, due to the scarcity of attack data, such methods' training sets are usually unbalanced, affecting the analysis performance. In this paper, we survey and analyze the design principles and shortcomings of existing oversampling methods. Based on the findings, we take the perspective of imbalance and high dimensionality of datasets in the field of intrusion detection and propose an oversampling technique based on Generative Adversarial Networks (GAN) and feature selection. Specifically, we model the complex high-dimensional distribution of attacks based on Gradient Penalty Wasserstein GAN (WGAN-GP) to generate additional attack samples. We then select a subset of features representing the entire dataset based on analysis of variance, ultimately generating a rebalanced low-dimensional dataset for machine learning training. To evaluate the effectiveness of our proposal, we conducted experiments based on the NSL-KDD, UNSW-NB15, and CICIDS-2017 datasets. The experimental results show that our method can effectively improve the detection performance of machine learning models and outperform the baselines.
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| GB/T 7714 | Liu, Xiaodong , Li, Tong , Zhang, Runzi et al. A GAN and Feature Selection-Based Oversampling Technique for Intrusion Detection [J]. | SECURITY AND COMMUNICATION NETWORKS , 2021 , 2021 . |
| MLA | Liu, Xiaodong et al. "A GAN and Feature Selection-Based Oversampling Technique for Intrusion Detection" . | SECURITY AND COMMUNICATION NETWORKS 2021 (2021) . |
| APA | Liu, Xiaodong , Li, Tong , Zhang, Runzi , Wu, Di , Liu, Yongheng , Yang, Zhen . A GAN and Feature Selection-Based Oversampling Technique for Intrusion Detection . | SECURITY AND COMMUNICATION NETWORKS , 2021 , 2021 . |
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