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Simulation Modeling Cyber Threats, Risks, and Prevention Costs 网络威胁、风险和预防成本的仿真建模
2018 IEEE International Conference on Electro/Information Technology (EIT) Pub Date : 2018-05-03 DOI: 10.1109/EIT.2018.8500240
James E. Lerums, La'Reshia D. Poe, J. E. Dietz
{"title":"Simulation Modeling Cyber Threats, Risks, and Prevention Costs","authors":"James E. Lerums, La'Reshia D. Poe, J. E. Dietz","doi":"10.1109/EIT.2018.8500240","DOIUrl":"https://doi.org/10.1109/EIT.2018.8500240","url":null,"abstract":"Money spent on cybersecurity doesn't easily translate into an increase in an organization's operational success or increase in revenues and profitability. However, an organization suffering from a cyber-attack could incur significant additional costs which can detrimentally impact an organization trivially or catastrophically. This paper introduces a simulation model for analyzing the effectiveness versus cost of cyber security options. The outcomes of this study is a simulation model using state charts capable of running a configurable attack scenario several times for a specified enterprise network and threat. Given publicly available information our findings after running a phishing attack on a departmental workstation with the internal network's domain controller as the final target revealed that the overall success rate of a phishing attack reaching any node in a “generic enterprise” architecture is 20%, with less than 0% of the attacks reaching the intended target.","PeriodicalId":188414,"journal":{"name":"2018 IEEE International Conference on Electro/Information Technology (EIT)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132744001","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: 6
Automatic Premature Ventricular Contractions Detection for Multi-Lead Electrocardiogram Signal 多导联心电图信号的室性早搏自动检测
2018 IEEE International Conference on Electro/Information Technology (EIT) Pub Date : 2018-05-03 DOI: 10.1109/EIT.2018.8500197
Mohamad Mahmoud Al Rahhal, N. A. Ajlan, Y. Bazi, H. Alhichri, T. Rabczuk
{"title":"Automatic Premature Ventricular Contractions Detection for Multi-Lead Electrocardiogram Signal","authors":"Mohamad Mahmoud Al Rahhal, N. A. Ajlan, Y. Bazi, H. Alhichri, T. Rabczuk","doi":"10.1109/EIT.2018.8500197","DOIUrl":"https://doi.org/10.1109/EIT.2018.8500197","url":null,"abstract":"In this paper, we propose an electrocardiogram (ECG) technique for the automatic detection of Premature Ventricular Contractions (PVC) based on multi-lead signals and on a deep learning architecture which is built using Stacked Denoising Autoencoders (SDAEs) networks. The proposed method consists of two main stages; feature learning and classification. In the first stage, we learn a new feature representation from data using SDAEs. Regarding the classification, we add a softmax regression layer on the top of the resulting hidden representation layer yielding a deep neural network (DNN). The proposed method fuses the results of several ECG leads (up to 12) in order to increase the detection accuracy. In the experiments, we use INCART database to test the proposed DNN multi-lead method. The obtained results are 98.6%, 91.4%, and 97.7% respectively for overall accuracy (OA), average sensitivity (Se), and average positive productivity (Pp).","PeriodicalId":188414,"journal":{"name":"2018 IEEE International Conference on Electro/Information Technology (EIT)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124259642","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: 11
Efficient and Scalable Certificate Revocation List Distribution in Hierarchical VANETs 分层vanet中有效和可扩展的证书撤销列表分发
2018 IEEE International Conference on Electro/Information Technology (EIT) Pub Date : 2018-05-03 DOI: 10.1109/EIT.2018.8500150
Kastuv M. Tuladhar, Kiho Lim
{"title":"Efficient and Scalable Certificate Revocation List Distribution in Hierarchical VANETs","authors":"Kastuv M. Tuladhar, Kiho Lim","doi":"10.1109/EIT.2018.8500150","DOIUrl":"https://doi.org/10.1109/EIT.2018.8500150","url":null,"abstract":"Vehicular ad-hoc networks (VANETs) have become an emerging technology that can fulfill the demand of evolving connected vehicles and growing need for Intelligent Transportation System (ITS). Certificates are used to secure vehicular communication but the certificates of vehicles need to be revoked if any vehicles are found as misbehaving nodes. In VANETs, certificate revocation list (CRL) must be quickly distributed to all vehicular nodes to prevent from undesirable communication with the malicious nodes. However, due to growing number of the certificates, the size of CRL continuously increases, and as a result, it becomes difficult to manage and distribute the CRL in the vehicular networks. In this paper, we present an efficient and scalable scheme to distribute certificate revocation list in the hierarchical architecture of VANETs. Our analysis shows that proposed scheme can distribute certificate revocation list promptly throughout the networks while maintaining low CRL size.","PeriodicalId":188414,"journal":{"name":"2018 IEEE International Conference on Electro/Information Technology (EIT)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116953760","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: 7
Stop Sign and Stop Line Detection and Distance Calculation for Autonomous Vehicle Control 自动驾驶车辆控制中的停车标志和停车线检测及距离计算
2018 IEEE International Conference on Electro/Information Technology (EIT) Pub Date : 2018-05-03 DOI: 10.1109/EIT.2018.8500268
Ashwin Arunmozhi, Shruti Gotadki, Jungme Park, Unmesh Gosavi
{"title":"Stop Sign and Stop Line Detection and Distance Calculation for Autonomous Vehicle Control","authors":"Ashwin Arunmozhi, Shruti Gotadki, Jungme Park, Unmesh Gosavi","doi":"10.1109/EIT.2018.8500268","DOIUrl":"https://doi.org/10.1109/EIT.2018.8500268","url":null,"abstract":"Environmental perception plays a crucial role in autonomous driving vehicle speed control. Autonomous vehicles must follow the traffic rules indicated in traffic sings. In this paper, a novel method is proposed for detection of stop sign and calculating the distance, which is an essential parameter in controlling the longitudinal velocity of an autonomous vehicle. As the vehicle moves closer to the stop sign, the stop sign falls out of the field of view of the camera, making it tough to bring the vehicle to stop at the desired distance from the sign. Hence, information on the position of stop line is essential to know where exactly to stop the vehicle. Stop sign detection is carried out using AdaBoost cascade classification based on three different feature types- Haar-like features, Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). The performance results of all the three classifiers are analyzed and compared to determine which one performs the best. To find the stop line a classic computer vision algorithm is proposed. The distance to stop sign and stop line is estimated in real time so that a decelerating torque can be applied accordingly to slow down the vehicle and eventually bring it to a complete standstill.","PeriodicalId":188414,"journal":{"name":"2018 IEEE International Conference on Electro/Information Technology (EIT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127210204","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: 6
Emotion Recognition Using Deep Neural Network with Vectorized Facial Features 基于矢量化面部特征的深度神经网络情感识别
2018 IEEE International Conference on Electro/Information Technology (EIT) Pub Date : 2018-05-03 DOI: 10.1109/EIT.2018.8500080
Guojun Yang, J. S. Y. Ortoneda, J. Saniie
{"title":"Emotion Recognition Using Deep Neural Network with Vectorized Facial Features","authors":"Guojun Yang, J. S. Y. Ortoneda, J. Saniie","doi":"10.1109/EIT.2018.8500080","DOIUrl":"https://doi.org/10.1109/EIT.2018.8500080","url":null,"abstract":"Emotion reveals valuable information regarding human communications. It is common to use facial expressions to express emotions during a conversation. Moreover, some interpersonal communication can be achieved using facial expressions only. Some facial expressions are universal, they express the same emotion across cultures. If a machine were able to interpret its user's facial expression correctly, it might be able to help its user more efficiently. In this paper, a novel vectorized facial feature for facial expression will be introduced. The vectorized facial feature can be used to build an DNN (Deep Neural Network) for emotion recognition. Using the proposed vectorized facial feature, the DNN can predict emotions with 84.33% accuracy. Nevertheless, compared with CNNs (Convolutional Neural Network) with similar performance, training such DNN requires less time and data.","PeriodicalId":188414,"journal":{"name":"2018 IEEE International Conference on Electro/Information Technology (EIT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127888373","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: 7
Acceleration of Image Stitching Using Embedded Graphics Processing Unit 利用嵌入式图形处理器加速图像拼接
2018 IEEE International Conference on Electro/Information Technology (EIT) Pub Date : 2018-05-03 DOI: 10.1109/EIT.2018.8500187
Karam M. Abughalieh, O. Bataineh, Shadi G. Alawneh
{"title":"Acceleration of Image Stitching Using Embedded Graphics Processing Unit","authors":"Karam M. Abughalieh, O. Bataineh, Shadi G. Alawneh","doi":"10.1109/EIT.2018.8500187","DOIUrl":"https://doi.org/10.1109/EIT.2018.8500187","url":null,"abstract":"Feature detection and matching are powerful techniques used in many computer vision applications such as image registration, tracking, and object detection. In this paper, a parallel implementation for invariant feature point based image warping and stitching using embedded GPU platform is implemented. The proposed solution is a mix of OpenCV functions and Unified Device Architecture (CUDA) kernels. CUDA kernel is used to perform the image translation tasks based on the translation info obtained by OpenCV. A sequential code is developed first to be used as a reference for the speed up calculations. The experimental results show a speed up of 100x and more using our GPU code with large images.","PeriodicalId":188414,"journal":{"name":"2018 IEEE International Conference on Electro/Information Technology (EIT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125561357","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: 2
Technology-Facilitated Detection of Mild Cognitive Impairment: A Review 技术促进轻度认知障碍的检测:综述
2018 IEEE International Conference on Electro/Information Technology (EIT) Pub Date : 2018-05-03 DOI: 10.1109/EIT.2018.8500151
Wenbing Zhao, Jagan A. Pillai, J. Leverenz, Xiong Luo
{"title":"Technology-Facilitated Detection of Mild Cognitive Impairment: A Review","authors":"Wenbing Zhao, Jagan A. Pillai, J. Leverenz, Xiong Luo","doi":"10.1109/EIT.2018.8500151","DOIUrl":"https://doi.org/10.1109/EIT.2018.8500151","url":null,"abstract":"Early diagnosis and management of dementia require accurate detection of symptoms and incidents in the pre-dementia stage of mild cognitive impairment (MCI). With the recent development of smart sensing technologies and machine learning algorithms, researchers have started exploring the possibility of automatically detecting symptoms of MCI based on home activity distributions. In this paper, we provide a brief review of the current state of the art in this line of research. We first present an overview of clinical studies on MCI. We then describe various technologies that have been used to collect data regarding patients cognitive levels and behaviors, and methods used to detect patterns and the deviation from these patterns. We also highlight the limitations of the current research work and outline future research tasks, including the development of cheaper and easily portable solutions, as well as personalized tracking technologies.","PeriodicalId":188414,"journal":{"name":"2018 IEEE International Conference on Electro/Information Technology (EIT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116054433","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: 7
Adaptive Dynamic Update for Greedy Routing Protocol Using Fuzzy Logic Controller and Mobility Prediction 基于模糊逻辑控制器和移动预测的贪心路由协议自适应动态更新
2018 IEEE International Conference on Electro/Information Technology (EIT) Pub Date : 2018-05-03 DOI: 10.1109/EIT.2018.8500126
M. Al-shugran, Mohammed M. Abu Shqier, Ghaith M. Jaradat
{"title":"Adaptive Dynamic Update for Greedy Routing Protocol Using Fuzzy Logic Controller and Mobility Prediction","authors":"M. Al-shugran, Mohammed M. Abu Shqier, Ghaith M. Jaradat","doi":"10.1109/EIT.2018.8500126","DOIUrl":"https://doi.org/10.1109/EIT.2018.8500126","url":null,"abstract":"Mobile Ad-hoc network (MANET) is characterized with high mobility and frequent topology change. Routing in MANET should follow these constrains, otherwise, it can severely degrade the performance of MANET. Greedy perimeter stateless routing (GPSR) in MANET uses one-hop neighbor's location information to make the forwarding decision. GPSR uses proactive update method to distribute position information via periodic “beacon” messages (PMs). The increasing in accuracy ratio leads to high communication overhead that will be in the expense of network resources. To address this problem, this paper presents a simple and efficient updating algorithm using Dynamic Fuzzy Logic Controller and Mobility Prediction (DBUM). DBUM is constructed of two patterns to improve the accuracy of node's location information. DBUM algorithm is able to improve the accuracy of node's location information with less beaconing traffic. The evaluation results show that DBUM outperforms traditional update mechanism in term of various types of load patterns.","PeriodicalId":188414,"journal":{"name":"2018 IEEE International Conference on Electro/Information Technology (EIT)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123840154","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: 3
A Hybrid Indoor Location Positioning System 一种混合室内定位系统
2018 IEEE International Conference on Electro/Information Technology (EIT) Pub Date : 2018-05-03 DOI: 10.1109/EIT.2018.8500265
Shuo Li, R. Rashidzadeh
{"title":"A Hybrid Indoor Location Positioning System","authors":"Shuo Li, R. Rashidzadeh","doi":"10.1109/EIT.2018.8500265","DOIUrl":"https://doi.org/10.1109/EIT.2018.8500265","url":null,"abstract":"Indoor location positioning methods have experienced an impressive growth in recent years. A wide range of indoor positioning algorithms have been developed for various applications. In this work a new hybrid indoor location positioning technique is presented which utilizes smartphones and low cost Bluetooth Low Energy (BLE) tags without any further infrastructure. The proposed method supports centimeter range positioning accuracy in its fine-positioning mode. The method includes coarse and fine location positioning. In the coarse positioning mode, a solution using received signal strength is employed while in the fine positioning mode an acoustic positioning technique is utilized. To ensure a high accuracy, the positioning system uses multilateration algorithm where only time synchronization between audio receivers is required. Experimental results using a commercially available BLE tags indicate that the proposed method, can determine indoor locations with less than 5 centimeters accuracy even in a noisy environment.","PeriodicalId":188414,"journal":{"name":"2018 IEEE International Conference on Electro/Information Technology (EIT)","volume":"40 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133753395","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: 6
Brain Tumor Classification via Statistical Features and Back-Propagation Neural Network 基于统计特征和反向传播神经网络的脑肿瘤分类
2018 IEEE International Conference on Electro/Information Technology (EIT) Pub Date : 2018-05-03 DOI: 10.1109/EIT.2018.8500308
Mustafa R. Ismael, I. Abdel-Qader
{"title":"Brain Tumor Classification via Statistical Features and Back-Propagation Neural Network","authors":"Mustafa R. Ismael, I. Abdel-Qader","doi":"10.1109/EIT.2018.8500308","DOIUrl":"https://doi.org/10.1109/EIT.2018.8500308","url":null,"abstract":"Classification of brain tumor is the heart of the computer-aided diagnosis (CAD) system designed to aid the radiologist in the diagnosis of such tumors using Magnetic Resonance Image (MRI). In this paper, we present a framework for classification of brain tumors in MRI images that combines statistical features and neural network algorithms. This algorithm uses region of interest (ROI), i.e. the tumor segment that is identified either manually by the technician/radiologist or by using any of the ROI segmentation techniques. We focus on feature selection by using a combination of the 2D Discrete Wavelet Transform (DWT) and 2D Gabor filter techniques. We create the features set using a complete set of the transform domain statistical features. For classification, back propagation neural network classifier has been selected to test the features selection impact. To do so, we used a large dataset consisting of 3,064 slices of T1-weighted MRI images with three types of brain tumors, Meningioma, Glioma, and Pituitary tumor. We obtained a total accuracy of 91.9%, and specificity of 96%, 96.29%, and 95.66% for Meningioma, Glioma, and Pituitary tumor respectively. Experimental results validate the effectiveness of the features selection method and indicate that it can compose an effective feature set to be used as a framework that can be combined with other classifications technique to enhance the performance.","PeriodicalId":188414,"journal":{"name":"2018 IEEE International Conference on Electro/Information Technology (EIT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114432514","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: 105
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