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2017 IEEE International Conference on Intelligence and Security Informatics (ISI)最新文献

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Web-derived Emotional Word Detection in social media using Latent Semantic information 基于潜在语义信息的社交媒体情感词检测
Pub Date : 2017-07-01 DOI: 10.1109/ISI.2017.8004881
C. Cai, Linjing Li, D. Zeng
Public sentiment permeated through social media is usually regarded as an important measure for public opinion monitoring, policy making, and so forth. However, the deluge of user-generated content in web, especially in social platform, causes great challenge to public sentiment analysis tasks. Therefore, Web-derived Emotional Word Detection (WEWD) is proposed as a fundamental tool aims to alleviate this problem. Most previous works on WEWD focus on rules, syntax, and sentence structures, a few utilize semantic information which has the potential to further increase the accuracy and efficiency of WEWD. In this paper, we propose a Global-Local Latent Semantic (GLLS) framework for WEWD to make a full use of latent semantic information with the help of multiple sense word embedding technology. We devise two computational WEWD models, called Ensemble GLLS (EGLLS) and Deep GLLS (DGLLS). EGLLS exploits an ensemble learning way to fuse the global and local latent semantics while DGLLS takes advantage of deep neural network. We also design an old-new corpus enrich technique to help increase the effectiveness of the overall training and detecting process. To the best of our knowledge, this is the first work which applies multiple sense word embedding and deep neural network in WEWD related tasks. Experiments on real datasets demonstrate the effectiveness of the proposed idea and methods.
通过社交媒体渗透的民意通常被视为舆论监测、政策制定等的重要手段。然而,网络尤其是社交平台上大量的用户生成内容给舆情分析任务带来了很大的挑战。因此,基于网络的情感词检测(WEWD)作为一种基本工具被提出,旨在缓解这一问题。以往关于WEWD的研究大多集中在规则、句法和句子结构上,少数利用语义信息的研究有可能进一步提高WEWD的准确性和效率。本文提出了一种基于全局-局部潜在语义(Global-Local Latent Semantic, GLLS)的WEWD框架,利用多义词嵌入技术充分利用潜在语义信息。我们设计了两种计算WEWD模型,称为集成GLLS (EGLLS)和深度GLLS (DGLLS)。EGLLS利用集成学习的方式融合全局和局部潜在语义,而DGLLS则利用深度神经网络。我们还设计了一种新旧语料库丰富技术,以帮助提高整体训练和检测过程的有效性。据我们所知,这是第一个将多义词嵌入和深度神经网络应用于web相关任务的工作。在实际数据集上的实验证明了所提思想和方法的有效性。
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引用次数: 1
Behavior enhanced deep bot detection in social media 行为增强了社交媒体中的深度机器人检测
Pub Date : 2017-07-01 DOI: 10.1109/ISI.2017.8004887
C. Cai, Linjing Li, D. Zeng
Social bots are regarded as the most common kind of malwares in social platform. They can produce fake messages, spread rumours, and even manipulate public opinions. Recently, massive social bots are created and widely spread in social platform, they bring negative effects to public and netizen security. Bot detection aims to distinguish bots from human and it catches more and more attentions in recent years. In this paper, we propose a behavior enhanced deep model (BeDM) for bot detection. The proposed model regards user content as temporal text data instead of plain text to extract latent temporal patterns. Moreover, BeDM fuses content information and behavior information using deep learning method. To the best of our knowledge, this is the first trial that applies deep neural network in bot detection. Experiments on real world dataset collected from Twitter also demonstrate the effectiveness of our proposed model.
社交机器人被认为是社交平台上最常见的一种恶意软件。他们可以制造虚假信息,传播谣言,甚至操纵公众舆论。最近,大量社交机器人在社交平台上被创造和广泛传播,它们给公众和网民的安全带来了负面影响。机器人检测旨在将机器人与人类区分开来,近年来受到越来越多的关注。本文提出了一种用于机器人检测的行为增强深度模型(BeDM)。该模型将用户内容视为时间文本数据,而不是纯文本,以提取潜在的时间模式。此外,BeDM采用深度学习方法融合内容信息和行为信息。据我们所知,这是第一次将深度神经网络应用于机器人检测的试验。从Twitter收集的真实世界数据集的实验也证明了我们提出的模型的有效性。
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引用次数: 89
Impact of replacement policies on static-dynamic query results cache in web search engines 替换策略对web搜索引擎中静态和动态查询结果缓存的影响
Pub Date : 2017-07-01 DOI: 10.1109/ISI.2017.8004890
Hongyuan Ma, Ou Tao, Chunlu Zhao, Pengxiao Li, Lihong Wang
Caching query results is an efficient technique for Web search engines. A state-of-the-art approach named Static-Dynamic Cache (SDC) is widely used in practice. Replacement policy is the key factor on the performance of cache system, and has been widely studied such as LIRS, ARC, CLOCK, SKLRU and RANDOM in different research areas. In this paper, we discussed replacement policies for static-dynamic cache and conducted the experiments on real large scale query logs from two famous commercial Web search engine companies. The experimental results show that ARC replacement policy could work well with static-dynamic cache, especially for large scale query results cache.
缓存查询结果是Web搜索引擎的一种高效技术。静态动态缓存(SDC)是目前应用最为广泛的一种缓存方法。替换策略是影响缓存系统性能的关键因素,在不同的研究领域得到了广泛的研究,如LIRS、ARC、CLOCK、SKLRU和RANDOM。本文讨论了静态动态缓存的替换策略,并在两家著名的商业Web搜索引擎公司的真实大规模查询日志上进行了实验。实验结果表明,ARC替换策略可以很好地处理静态动态缓存,特别是对于大规模查询结果缓存。
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引用次数: 0
Raising flags: Detecting covert storage channels using relative entropy 提升标志:使用相对熵检测隐蔽存储通道
Josephine K. Chow, Xiangyang Li, X. Mountrouidou
This paper focuses on one type of Covert Storage Channel (CSC) that uses the 6-bit TCP flag header in TCP/IP network packets to transmit secret messages between accomplices. We use relative entropy to characterize the irregularity of network flows in comparison to normal traffic. A normal profile is created by the frequency distribution of TCP flags in regular traffic packets. In detection, the TCP flag frequency distribution of network traffic is computed for each unique IP pair. In order to evaluate the accuracy and efficiency of the proposed method, this study uses real regular traffic data sets as well as CSC messages using coding schemes under assumptions of both clear text, composed by a list of keywords common in Unix systems, and encrypted text. Moreover, smart accomplices may use only those TCP flags that are ever appearing in normal traffic. Then, in detection, the relative entropy can reveal the dissimilarity of a different frequency distribution from this normal profile. We have also used different data processing methods in detection: one method summarizes all the packets for a pair of IP addresses into one flow and the other uses a sliding moving window over such a flow to generate multiple frames of packets. The experimentation results, displayed by Receiver Operating Characteristic (ROC) curves, have shown that the method is promising to differentiate normal and CSC traffic packet streams. Furthermore the delay of raising an alert is analyzed for CSC messages to show its efficiency.
本文重点研究了一种隐蔽存储通道(CSC),它使用TCP/IP网络数据包中的6位TCP标志头在共犯之间传输秘密消息。与正常流量相比,我们使用相对熵来表征网络流量的不规则性。正常配置文件是根据常规流量报文中TCP标志的频率分布来创建的。在检测中,为每个唯一的IP对计算网络流量的TCP标志频率分布。为了评估所提出方法的准确性和效率,本研究使用了真实的常规交通数据集以及CSC消息,这些消息使用编码方案,假设是明文(由Unix系统中常见的关键字列表组成)和加密文本。此外,智能助手可能只使用那些在正常通信中出现的TCP标志。然后,在检测中,相对熵可以揭示不同频率分布与该正态分布的不同之处。我们还在检测中使用了不同的数据处理方法:一种方法将一对IP地址的所有数据包汇总到一个流中,另一种方法在这样的流上使用滑动移动窗口来生成多帧数据包。实验结果显示,接收机工作特征(ROC)曲线表明,该方法有望区分正常和CSC流量数据包流。进一步分析了CSC报文的报警延迟,证明了它的有效性。
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引用次数: 6
期刊
2017 IEEE International Conference on Intelligence and Security Informatics (ISI)
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