2019 IEEE International Conference on Intelligence and Security Informatics (ISI)最新文献_第6页

Privacy Protection in Transformer-based Neural Network 基于变压器的神经网络隐私保护

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823346

Jiaqi Lang, Linjing Li, Weiyun Chen, D. Zeng

引用次数: 1

Pre-trained Contextualized Representation for Chinese Conversation Topic Classification 中文会话主题分类的预训练语境化表示

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823172

Yujun Zhou, Changliang Li, Saike He, Xiaoqi Wang, Yiming Qiu

{"title":"Pre-trained Contextualized Representation for Chinese Conversation Topic Classification","authors":"Yujun Zhou, Changliang Li, Saike He, Xiaoqi Wang, Yiming Qiu","doi":"10.1109/ISI.2019.8823172","DOIUrl":"https://doi.org/10.1109/ISI.2019.8823172","url":null,"abstract":"Topic classification plays an important role in facilitating security-related applications, which can help people reduce data scope and acquire key information quickly. Conversation is one of the important ways of communication between people. The utterances in a conversation may contain vital clues, such as people’s opinions, emotions and political slants. To explore more effective approaches for Chinese conversational topic classification, in this paper, we propose a neural network architecture with pre-trained contextualized representation. We firstly apply pretrained BERT model to fine-tune and generate the conversational embeddings, which are the inputs of our neural network models. Then we design several models based on neural networks to extract task-oriented advanced features for topic classification. Experimental results indicate that the models based on our neural network architecture all outperform the baseline only fine-tuned with the pre-trained BERT model. It demonstrates that the pretrained representations are effective to Chinese conversational topic classification, and the proposed architecture can further capture the salient features from the representations. And we release the code and dataset of this paper that can be obtained from https://github.com/njoe9/pretrained_representation.","PeriodicalId":156130,"journal":{"name":"2019 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115494284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

A Deep Learning Approach to Modeling Temporal Social Networks on Reddit 一种深度学习方法对Reddit上的时间社会网络进行建模

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823399

Wingyan Chung, Cagri Toraman, Yifan Huang, M. Vora, Jinwei Liu

{"title":"A Deep Learning Approach to Modeling Temporal Social Networks on Reddit","authors":"Wingyan Chung, Cagri Toraman, Yifan Huang, M. Vora, Jinwei Liu","doi":"10.1109/ISI.2019.8823399","DOIUrl":"https://doi.org/10.1109/ISI.2019.8823399","url":null,"abstract":"As terrorists are losing against counter-terrorism efforts, they turn to manipulating cryptocurrency prices through online social communities to gain illicit profit to fund their operations. Modeling temporal online social networks (OSNs) of these communities can possibly help to provide useful intelligence about these malicious activities. However, existing techniques do not learn sufficiently from diverse features to enable prediction and simulation of online social behavior. Research on simulating temporal OSN behavior is not widely available. This research developed and validated a deep learning approach, named Temporal Network Model (TNM), to modeling the complex features and dynamic behavior exhibited in the temporal OSNs of online communities. Using extensive features extracted from fine-grained data, TNM consists of weighted time series models, user and link prediction models, and temporal dependency model that predict respectively the macroscopic behavior, microscopic user participation and events, and time stamps of the events. Evaluation was done in comparison with a benchmark approach to examine TNM’s performance on predicting and simulating behavior of 42,627 users in 440,906 events on the Reddit cryptocurrency community during July-August of 2017. Results show that TNM outperformed the benchmark in 5 out of 8 simulation metrics. TNM achieved consistently better performance in user activity prediction, and performed generally better in structural (network-level) prediction. The research provides new findings on simulating temporal OSNs and new predictive analytics for understanding online social behavior.","PeriodicalId":156130,"journal":{"name":"2019 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126616840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

The entropy source of pseudo random number generators: from low entropy to high entropy 伪随机数生成器的熵源:从低熵到高熵

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823457

Jizhi Wang, Jingshan Pan, Xueli Wu

引用次数: 6

Enhancing Rumor Detection in Social Media Using Dynamic Propagation Structures 利用动态传播结构增强社交媒体谣言检测

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823266

Shuai Wang, Qingchao Kong, Yuqi Wang, Lei Wang

{"title":"Enhancing Rumor Detection in Social Media Using Dynamic Propagation Structures","authors":"Shuai Wang, Qingchao Kong, Yuqi Wang, Lei Wang","doi":"10.1109/ISI.2019.8823266","DOIUrl":"https://doi.org/10.1109/ISI.2019.8823266","url":null,"abstract":"Social media, such as Facebook and Twitter, has become one of the most important channels for information dissemination. However, these social media platforms are often misused to spread rumors, which has brought about severe social problems, and consequently, there are urgent needs for automatic rumor detection techniques. Existing work on rumor detection concentrates more on the utilization of textual features, but diffusion structure itself can provide critical propagating information in identifying rumors. Previous works which have considered structural information, only utilize limited propagation structures. Moreover, few related research has considered the dynamic evolution of diffusion structures. To address these issues, in this paper, we propose a Neural Model using Dynamic Propagation Structures (NM-DPS) for rumor detection in social media. Firstly, we propose a partition approach to model the dynamic evolution of propagation structure and then use temporal attention based neural model to learn a representation for the dynamic structure. Finally, we fuse the structure representation and content features into a unified framework for effective rumor detection. Experimental results on two real-world social media datasets demonstrate the salience of dynamic propagation structure information and the effectiveness of our proposed method in capturing the dynamic structure.","PeriodicalId":156130,"journal":{"name":"2019 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122299736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

ISI 2019 Committees ISI 2019委员会

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/isi.2019.8823499

引用次数: 0

Information Hiding in OOXML Format Data based on the Splitting of Text Elements 基于文本元素拆分的OOXML格式数据中的信息隐藏

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823564

Wenjie Guo, Li Yang, Yonggang Lu, Yi Yang, Lian Li, Zongli Liu

{"title":"Information Hiding in OOXML Format Data based on the Splitting of Text Elements","authors":"Wenjie Guo, Li Yang, Yonggang Lu, Yi Yang, Lian Li, Zongli Liu","doi":"10.1109/ISI.2019.8823564","DOIUrl":"https://doi.org/10.1109/ISI.2019.8823564","url":null,"abstract":"In this paper, a novel information hiding method is proposed to embed data in the Word documents that use OOXML format. The 2007 version and more recent versions of MS Word are all based on the OOXML format. The main document body of OOXML document consists of the text elements that correspond to the content of the document. It is found that, in the OOXML format, the printable text in an element can be “split” by separating the element into multiple elements. The digital code of the information determines if the adjacent characters in the text will be “split” or not, so as to achieve the purpose of information hiding. Since the format and all the other properties of the text in the OOXML format document is unchanged after the embedding, the embedded information is imperceptible. The code of the information is embedded circularly to enhance robustness. Experiments show that the proposed method can deal with many kinds of attacks, including “content”, “save as”, “copy” and part of “format”. The proposed method can be used in information security and copyright protection for OOXML format documents.","PeriodicalId":156130,"journal":{"name":"2019 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131918724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

The Corporation Lawsuit Prediction based on Guiding Learning and Collaborative Filtering Recommendation 基于引导学习和协同过滤推荐的公司诉讼预测

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823537

Zhenyu Wu, Guangda Chen, Jingjing Yao

{"title":"The Corporation Lawsuit Prediction based on Guiding Learning and Collaborative Filtering Recommendation","authors":"Zhenyu Wu, Guangda Chen, Jingjing Yao","doi":"10.1109/ISI.2019.8823537","DOIUrl":"https://doi.org/10.1109/ISI.2019.8823537","url":null,"abstract":"It is meaningful to use data mining technology to predict the type of lawsuit which a company may receive so that enterprises can avoid lawsuit risks. So we propose a corporation lawsuit prediction algorithm based on guiding learning and collaborative filtering recommendation. Firstly, we use the adaptive synthetic sampling approach (ADASYN) to generate more synthetic data for different minority classes according to their different level of difficulty in learning, so that the training would focus on these minority classes that are difficulty to learn and reduce the learning bias introduced by the imbalance of data distribution. Secondly, for the sake of solving the problem that the insufficient samples make it difficult for the model to learn enough knowledge resulting in a large fluctuation of final scores during the training and poor model stability, we use guiding learning to integrate the basic knowledge of all types of lawsuit a company may receive in the future obtained by the multi-label classification model into the training process of TOP-1 and TOP-2 predictive models. Finally, in order to further improve the prediction accuracy, we use the collaborative filtering recommendation algorithm (CFRA) to select the most similar sample with each test sample from the training set, and the lawsuit type of the selected sample is directly used as the predicted lawsuit type of the corresponding test sample, thereby improving the total prediction accuracy. The experimental results show that the proposed algorithm can effectively predict the most probable lawsuit types of the Top2 for corporations.","PeriodicalId":156130,"journal":{"name":"2019 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132118052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Forensic Evidence Acquisition Model for Data Leakage Attacks 数据泄露攻击的取证取证模型

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823391

Weifeng Xu, Jie Yan, H. Chi

引用次数: 4

Exploring the Dark Web for Cyber Threat Intelligence using Machine Leaning 利用机器学习探索暗网中的网络威胁情报

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823360

Masashi Kadoguchi, S. Hayashi, Masaki Hashimoto, Akira Otsuka

引用次数: 23