2019 2nd International Conference on Data Intelligence and Security (ICDIS)最新文献_第4页

An Immersive VR Interactive Learning System for Tenon Structure Training 一种用于榫卯结构训练的沉浸式VR交互学习系统

2019 2nd International Conference on Data Intelligence and Security (ICDIS) Pub Date : 2019-06-01 DOI: 10.1109/ICDIS.2019.00025

Liang Chen, Lingling Wu, Xuwei Li, Jin Xu

引用次数: 0

Malware Detection Using Power Consumption and Network Traffic Data 利用功耗和网络流量数据进行恶意软件检测

2019 2nd International Conference on Data Intelligence and Security (ICDIS) Pub Date : 2019-06-01 DOI: 10.1109/ICDIS.2019.00016

J. Jiménez, K. Goseva-Popstojanova

{"title":"Malware Detection Using Power Consumption and Network Traffic Data","authors":"J. Jiménez, K. Goseva-Popstojanova","doi":"10.1109/ICDIS.2019.00016","DOIUrl":"https://doi.org/10.1109/ICDIS.2019.00016","url":null,"abstract":"Even though malware detection is an active area of research, not many works have used features extracted from physical properties, such as power consumption. This paper is focused on malware detection using power consumption and network traffic data collected using our experimental testbed. Seven power-based and eighteen network traffic-based features were extracted and ten supervised machine learning algorithms were used for classification. The main findings include: (1) Among the best performing learners, Random Forest had the highest F-score and close to the highest G-score. (2) Power data extracted from the +12V CPU rails led to better performance than power data from the other three voltage rails. (3) Using only power-based features provided better performance than using only network traffic-based features; using both types of features had the best performance. (4) Feature selection based on information gain was used to identify the smallest numbers of features sufficient to successfully distinguish malware from non-malicious software. The top eleven features provided the same performance as using all 25 features. Five out of seven power-based features were among the top eleven features.","PeriodicalId":181673,"journal":{"name":"2019 2nd International Conference on Data Intelligence and Security (ICDIS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131161981","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: 16

Concurrency Strategies for Attack Graph Generation 攻击图生成的并发策略

2019 2nd International Conference on Data Intelligence and Security (ICDIS) Pub Date : 2019-06-01 DOI: 10.1109/ICDIS.2019.00033

Ming Li, P. Hawrylak, J. Hale

引用次数: 5

An Accelerated Hierarchical Approach for Object Shape Extraction and Recognition 一种快速分层的物体形状提取与识别方法

2019 2nd International Conference on Data Intelligence and Security (ICDIS) Pub Date : 2019-06-01 DOI: 10.1109/ICDIS.2019.00030

M. Quweider, Bassam Arshad, Hansheng Lei, Liyu Zhang, Fitratullah Khan

{"title":"An Accelerated Hierarchical Approach for Object Shape Extraction and Recognition","authors":"M. Quweider, Bassam Arshad, Hansheng Lei, Liyu Zhang, Fitratullah Khan","doi":"10.1109/ICDIS.2019.00030","DOIUrl":"https://doi.org/10.1109/ICDIS.2019.00030","url":null,"abstract":"We present a novel automatic supervised object recognition algorithm based on a scale and rotation invariant Fourier descriptors algorithm. The algorithm is hierarchical in nature allowing it to capture the inherent intra-contour spatial relationships between the parent and child contours of an object by building a tree-structure of the top-level contours that make the distinctive features of the object to be recognized. A set of distance metrics are combined to measure the similarity between two objects under the hierarchical model. To test the algorithm, a diverse database of shapes is created and used to train standard classification algorithms, for shape-labeling. The implemented algorithm takes advantage of the multi-threaded architecture and GPU efficient image-processing functions present in OpenCV wherever possible, speeding up the running time and making it efficient for use in real-time applications. The technique is successfully tested on common traffic and road signs of real-world images, with excellent overall performance that is robust to low-to-moderate noise levels.","PeriodicalId":181673,"journal":{"name":"2019 2nd International Conference on Data Intelligence and Security (ICDIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128923203","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: 0

An End-to-End Framework to Identify Pathogenic Social Media Accounts on Twitter 一个端到端框架来识别Twitter上的致病社交媒体账户

2019 2nd International Conference on Data Intelligence and Security (ICDIS) Pub Date : 2019-05-04 DOI: 10.1109/ICDIS.2019.00027

Elham Shaabani, Ashkan Sadeghi-Mobarakeh, Hamidreza Alvari, P. Shakarian

引用次数: 5

Detection of Violent Extremists in Social Media 发现社交媒体中的暴力极端分子

2019 2nd International Conference on Data Intelligence and Security (ICDIS) Pub Date : 2019-02-05 DOI: 10.1109/ICDIS.2019.00014

Hamidreza Alvari, Soumajyoti Sarkar, P. Shakarian

{"title":"Detection of Violent Extremists in Social Media","authors":"Hamidreza Alvari, Soumajyoti Sarkar, P. Shakarian","doi":"10.1109/ICDIS.2019.00014","DOIUrl":"https://doi.org/10.1109/ICDIS.2019.00014","url":null,"abstract":"The ease of use of the Internet has enabled violent extremists such as the Islamic State of Iraq and Syria (ISIS) to easily reach large audience, build personal relationships and increase recruitment. Social media are primarily based on the reports they receive from their own users to mitigate the problem. Despite efforts of social media in suspending many accounts, this solution is not guaranteed to be effective, because not all extremists are caught this way, or they can simply return with another account or migrate to other social networks. In this paper, we design an automatic detection scheme that using as little as three groups of information related to usernames, profile, and textual content of users, determines whether or not a given username belongs to an extremist user. We first demonstrate that extremists are inclined to adopt usernames that are similar to the ones that their like-minded have adopted in the past. We then propose a detection framework that deploys features which are highly indicative of potential online extremism. Results on a real-world ISIS-related dataset from Twitter demonstrate the effectiveness of the methodology in identifying extremist users.","PeriodicalId":181673,"journal":{"name":"2019 2nd International Conference on Data Intelligence and Security (ICDIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130189350","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: 18

Hawkes Process for Understanding the Influence of Pathogenic Social Media Accounts 霍克斯理解致病社交媒体账户影响的过程

2019 2nd International Conference on Data Intelligence and Security (ICDIS) Pub Date : 2019-02-05 DOI: 10.1109/ICDIS.2019.00013

Hamidreza Alvari, P. Shakarian

{"title":"Hawkes Process for Understanding the Influence of Pathogenic Social Media Accounts","authors":"Hamidreza Alvari, P. Shakarian","doi":"10.1109/ICDIS.2019.00013","DOIUrl":"https://doi.org/10.1109/ICDIS.2019.00013","url":null,"abstract":"Over the past years, political events and public opinion on the Web have been allegedly manipulated by accounts dedicated to spreading disinformation and performing malicious activities on social media. These accounts hereafter referred to as \"Pathogenic Social Media (PSM)\" accounts, are often controlled by terrorist supporters, water armies or fake news writers and hence can pose threats to social media and general public. Understanding and analyzing PSMs could help social media firms devise sophisticated and automated techniques that could be deployed to stop them from reaching their audience and consequently reduce their threat. In this paper, we leverage the well-known statistical technique \"Hawkes Process\" to quantify the influence of PSM accounts on the dissemination of malicious information on social media platforms. Our findings on a real world ISIS-related dataset from Twitter indicate that PSMs are significantly different from regular users in making a message viral. Specifically, we observed that PSMs do not usually post URLs from mainstream news sources. Instead, their tweets usually receive large impact on audience, if contained URLs from Facebook and alternative news outlets. In contrary, tweets posted by regular users receive nearly equal impression regardless of the posted URLs and their sources. Our findings can further shed light on understanding and detecting PSM accounts.","PeriodicalId":181673,"journal":{"name":"2019 2nd International Conference on Data Intelligence and Security (ICDIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127114852","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: 17