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Phishing detection method based on borderline-smote deep belief network 10th

Phishing detection method based on borderline-smote deep bel...

10th International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, SpaCCS 2017

作者： Zhang, Jiahua Li, Xiaoyong The Key Laboratory of Trustworthy Distributed Computing Service Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9783319723945

With the rapid development of Internet, phishing and other frauds are becoming more and more serious. Criminals posing as banks, electricity providers, social networking sites to send fraudulent information to induce users to log on, steal user information, so that the vast numbers of users and financial institutions suffered property and economic losses. How to accurately and effectively identify phishing related Internet risks has been a major concern of the Internet. This paper analyzes the development history of phishing prevention and control, and presents a Borderline-Smote (Synthetic Minority Over-sampling Technique) DBN (Deeping Belief Network) method to detect phishing. The method uses deep learning phishing detection method based on web documents content and other features to improve 1% on the recognition accuracy. Furthermore the paper uses Borderline-Smote to solve the imbalanced data problem in the training of phishing detection, and further improve 2% on the F-value and recall rate. © Springer International Publishing AG 2017.

关键词： Deep learning

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MTIV: A trustworthiness determination approach for threat intelligence 10th

MTIV: A trustworthiness determination approach for threat in...

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10th International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, SpaCCS 2017

作者： Li, Lei Li, Xiaoyong Gao, Yali Key Laboratory of Trustworthy Distributed Computing Service of the Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9783319723945

With the gradually sharing of threat intelligences, users concern more about their trustworthiness, which is difficult to be judged. Some threat intelligence sharing platforms choose to show the risk or credibility, and inform users the trustworthiness of the threat intelligence. Several researchers have proposed the requirements and techniques for threat intelligence trust assessment. However, they do not present any tool-based or model solutions. In this paper, we present a Trustworthiness Determination Approach for Threat Intelligence (MTIV) to make up these shortcomings. First, we propose a framework to excavate threat intelligence via multiple sharing platforms, and extract multidimensional trustful features of the threat intelligence. Based on these, contributions of dimensional trustful features to the trustworthiness determination can be derived. Then we introduce Deep Belief Network (DBN) to determine the trustworthiness of the threat intelligence. The experimental results verify that MTIV is more effective than traditional methods. Our work will be of benefit to build a more credible threat intelligence sharing platform, and enhance the capability of real-time detection and resisting the cyberspace attacks. © Springer International Publishing AG 2017.

关键词： Network security

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A tensor-based sub-mode coordinate algorithm for stock prediction

arXiv

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arXiv 2018年

作者： Huang, Jieyun Zhang, Yunjia Zhang, Jialai Zhang, Xi Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China School of Electronic Information Sichuan University Chengdu China

The investment on the stock market is prone to be affected by the Internet. For the purpose of improving the prediction accuracy, we propose a multi-task stock prediction model that not only considers the stock correlations but also supports multi-source data fusion. Our proposed model first utilizes tensor to integrate the multi-sourced data, including financial Web news, investors' sentiments extracted from the social network and some quantitative data on stocks. In this way, the intrinsic relationships among different information sources can be captured, and meanwhile, multi-sourced information can be complemented to solve the data sparsity problem. Secondly, we propose an improved sub-mode coordinate algorithm (SMC). SMC is based on the stock similarity, aiming to reduce the variance of their subspace in each dimension produced by the tensor decomposition. The algorithm is able to improve the quality of the input features, and thus improves the prediction accuracy. And the paper utilizes the Long Short-Term Memory (LSTM) neural network model to predict the stock fluctuation trends. Finally, the experiments on 78 A-share stocks in CSI 100 and thirteen popular HK stocks in the year 2015 and 2016 are conducted. The results demonstrate the improvement on the prediction accuracy and the effectiveness of the proposed model. Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

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Comparison of three data mining methods in predicting 5-Year Survival of Colorectal Cancer Patients

Comparison of three data mining methods in predicting 5-Year...

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智能时代的创新、融合与发展——2018全国博士后学术交流会

作者： LUO Yan SUN Ya-wei FU Qun-chao XUE Teng-fei ZHOU Ping School of Software Engineering Beijing University of Posts and Telecommunications Beijing 100876 China Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of Education Beijing 100876 China

The prediction of colorectal cancer (CRC) survivability has always been a challenging research *** the importance of predicting CRC Patient's survival rates,this article compare the performance of three data mining methods: decision trees(DTs),artificial neural networks(ANNs) and support vector machines(SVMs),for predicting 5-year survival of CRC patients to assist clinicians in making treatment *** CRC dataset used to build the prediction model comes from the surveillance,epidemiology,and end results(SEER) *** 5-fold cross-validation and random forest algorithm were respectively utilized for measuring the model predictive accuracy and the importance of *** results show that the predictive accuracy of ANNs (0.73) and SVMs (0.75) were higher than that of decision trees,and they also have the best result in the area under the ROC curve (AUC =0.82).This result may indicate high predictive power of ANNs and SVMs for predicting 5-year survival of CRC patients.

关键词： data mining 5-Year Survival CRC SEER

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An Attack Signatures Generation Sequence Alignment Algorithm Based on Production Rules

An Attack Signatures Generation Sequence Alignment Algorithm...

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International Conference on Communication Software and Networks, ICCSN

作者： Muhan Xue Wen Yu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing China

Aiming at the problem that the result of some attack sequence alignment methods is not necessarily the optimal expression of their characteristics. This paper presents a Production Rule Sequence Alignment Algorithm (PRSA) combining the production rule inference mechanism which improves traditional sequence alignment algorithm. A new accumulation of knowledge is obtained by changing the way of sequence alignment and the transformation of signatures. PRSA overcomes the problem that the extraction results produced by the traditional sequence alignment algorithm cannot express the attack signature accurately. Then, we establish an automatic attack signature generation model based on PRSA. The experimental results show that the matching results obtained by using PRSA can express the signatures of the attack accurately and improve the detection rate of the attacks.

关键词： Production Feature extraction Inference algorithms Classification algorithms Software algorithms Libraries Data models

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Faster-than-Nyquist Signaling for Optical Communications

Faster-than-Nyquist Signaling for Optical Communications

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OptoElectronics and Communications Conference, OECC

作者： Yaojun Qiao Ji Zhou Mengqi Guo Xizi Tang Jia Qi Yueming Lu School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China

In this paper, we will discuss the faster-than-Nyquist (FTN) signaling for optical communications, including the single-carrier and multi-carrier FTN signaling.

ISBN: (纸本)9781538691465

In this paper, we will discuss the faster-than-Nyquist (FTN) signaling for optical communications, including the single-carrier and multi-carrier FTN signaling.

关键词： Bandwidth OFDM MIMO communication Optical fiber communication Optical filters Optical transmitters

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Maximizing Influence Propagation in Networks by User-Relational Iterative Ranking Algorithm

Maximizing Influence Propagation in Networks by User-Relatio...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Wenbin Yao Lijuan Zhang Xiaoyong Li Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781728111421;9781728111414

The issue of maximizing the influence is a hot topic in the research of social network. Many researchers have studied from the perspective of the structure of the network such as the LeaderRank algorithm. However, the algorithm lacks semantic interpretability and explanations for user behavior. Therefore, we propose a novel URI (user-relational iterative) rank to address the above issues. The URI rank is divided into two parts to obtain the values of user influence. The first part is the forwarding probability based on the user relationship. We introduce the relationship between users to the user transition probability and use the random forest to quantify the value of the forwarding probability. The second part is the random transition probability based on the ground node. We optimize the weight assigning of the random transition probability by combining static decentralization and dynamic decentralization. Thus, we more accurately represent the user's random transfer behavior. The experiments performed on the Sina Micro-Blog Dataset show that our algorithm outperforms the existing algorithms.

关键词： Social networking (online) Telecommunications Greedy algorithms Complex networks Software Multimedia communication Multimedia computing

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Neural Network Based Target User Recognition Model for Network Community

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IOP Conference Series: Earth and Environmental Science 2019年第1期234卷

作者： Xu Wu Yulun Dai Xiaqing Xie Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security BUPT Beijing University of Posts and Telecommunications Library Beijing 100876 China

For multi-dimensional feature of user data in network community, available methods mainly use Rank scoring algorithm or user classification algorithm for target user recognition. However, Rank method has a low performance, and the classification algorithm needs high construction cost. Therefore, this paper uses a target user recognition model integrating the user content analysis and the user behavior analysis to improve performance and speed of the target user recognition by a neural network content analysis model with a single-layer neural network and N-gram features for discovering automatically the user feature. The proposed method outperforms the current Rank scoring and classification methods in terms of performance, in which the F value reaches 0.89 and the accuracy reaches 0.91. Moreover, avoiding the cost of manual design dependent on specific tasks shortens the training time. Ten thousand data can be modeled in one minute.

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Exploiting investors social network for stock prediction in China's market

arXiv

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arXiv 2018年

作者： Zhang, Xi Shi, Jiawei Wang, Di Fang, Binxing Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China Institute of Electronic and Information Engineering UESTC in Guangdong Dongguan Guangdong523808 China

Recent works have shown that social media platforms are able to influence the trends of stock price movements. However, existing works have majorly focused on the U.S. stock market and lacked attention to certain emerging countries such as China, where retail investors dominate the market. In this regard, as retail investors are prone to be influenced by news or other social media, psychological and behavioral features extracted from social media platforms are thought to well predict stock price movements in the China's market. Recent advances in the investor social network in China enables the extraction of such features from web-scale data. In this paper, on the basis of tweets from Xueqiu, a popular Chinese Twitter-like social platform specialized for investors, we analyze features with regard to collective sentiment and perception on stock relatedness and predict stock price movements by employing nonlinear models. The features of interest prove to be effective in our experiments. Copyright © 2018, The Authors. All rights reserved.

关键词： Sentiment analysis

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An efficient nonlinear equalizer for 40-Gb/s PAM4-PON systems

An efficient nonlinear equalizer for 40-Gb/s PAM4-PON system...

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Optical Fiber Communication Conference, OFC 2018

作者： Tang, Xizi Zhou, Ji Guo, Mengqi Qi, Jia Zhang, Tiantian Zhang, Zhenshan Lu, Yueming Qiao, Yaojun State Key Laboratory of Information Photonics and Optical Communications School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

We firstly propose a decision feedback equalizer (DFE) based on a novel nonlinear filter structure for 40-Gb/s PAM4-PON systems. The proposed equalizer achieves similar performance with Volterra DFE at only 21.7% tap ... 详细信息

ISBN: (纸本)9781943580385

关键词： Decision feedback equalizers

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