SoutheastCon 2017最新文献_第5页

Automated sleep data extraction and streaming using Zeo sleep personal manager and Raspberry Pi 自动睡眠数据提取和流使用Zeo睡眠个人管理器和树莓派

SoutheastCon 2017 Pub Date : 2017-03-01 DOI: 10.1109/SECON.2017.7925276

Joshua Haney, Sungkyun Lim, R. Chong

引用次数: 2

A W-band constructive wave oscillator in 130-nm SiGe BiCMOS 130-nm SiGe BiCMOS的w波段构造波振荡器

SoutheastCon 2017 Pub Date : 2017-03-01 DOI: 10.1109/SECON.2017.7925356

Goker Ariyak, Monique Kirkman-Bey, N. Dogan, Zhijian Xie, M. Ketel

引用次数: 2

Inrush current limiting for solid state devices using NTC resistor 用NTC电阻限制固态器件的浪涌电流

SoutheastCon 2017 Pub Date : 2017-03-01 DOI: 10.1109/SECON.2017.7925398

Taosha Jiang, P. Cairoli, Rostan Rodrigues, Yu Du

引用次数: 13

Graph mining for forensic databases 面向取证数据库的图挖掘

SoutheastCon 2017 Pub Date : 2017-03-01 DOI: 10.1109/SECON.2017.7925340

S. Thorpe, Miguel Bernard

引用次数: 1

The promise of machine learning in cybersecurity 机器学习在网络安全领域的前景

SoutheastCon 2017 Pub Date : 2017-03-01 DOI: 10.1109/SECON.2017.7925283

James B. Fraley, J. Cannady

{"title":"The promise of machine learning in cybersecurity","authors":"James B. Fraley, J. Cannady","doi":"10.1109/SECON.2017.7925283","DOIUrl":"https://doi.org/10.1109/SECON.2017.7925283","url":null,"abstract":"Over the last few years' machine learning has migrated from the laboratory to the forefront of operational systems. Amazon, Google and Facebook use machine learning every day to improve customer experiences, suggested purchases or connect people socially with new applications and facilitate personal connections. Machine learning's powerful capability is also there for cybersecurity. Cybersecurity is positioned to leverage machine learning to improve malware detection, triage events, recognize breaches and alert organizations to security issues. Machine learning can be used to identify advanced targeting and threats such as organization profiling, infrastructure vulnerabilities and potential interdependent vulnerabilities and exploits. Machine learning can significantly change the cybersecurity landscape. Malware by itself can represent as many as 3 million new samples an hour. Traditional malware detection and malware analysis is unable to pace with new attacks and variants. New attacks and sophisticated malware have been able to bypass network and end-point detection to deliver cyber-attacks at alarming rates. New techniques like machine learning must be leveraged to address the growing malware problem. This paper describes how machine learning can be used to detect and highlight advanced malware for cyber defense analysts. The results of our initial research and a discussion of future research to extend machine learning is presented.","PeriodicalId":368197,"journal":{"name":"SoutheastCon 2017","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133909133","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: 63

Artificial registration of network stress to self-Monitor an autonomic computing system 人工登记网络压力以实现自主计算系统的自我监控

SoutheastCon 2017 Pub Date : 2017-03-01 DOI: 10.1109/SECON.2017.7925334

Isong Idio, Rahmira Rufus, A. Esterline

{"title":"Artificial registration of network stress to self-Monitor an autonomic computing system","authors":"Isong Idio, Rahmira Rufus, A. Esterline","doi":"10.1109/SECON.2017.7925334","DOIUrl":"https://doi.org/10.1109/SECON.2017.7925334","url":null,"abstract":"The objective of this research is to associate artificial registrations of unauthorized network activity with stress knowledge representations to self-Monitor an autonomic computing system, which is a self-Managing system. Utilization of the danger theory perspective in artificial immune systems (AIS) is employed to illustrate how an AIS classification method contributes to four properties of autonomic computation: self-Configuration, self-Optimization, self-Healing, and self-Protection— known as the self-C.H.O.P. properties. When the stress knowledge representation is detected via self-Monitoring, then an autonomic response to self-C.H.O.P. is executed. The AIS senses its environment by monitoring system activity (components and performance) to detect activity that signals a self-C.H.O.P. property synonymous to the collaborative efforts performed by the natural immune system (NIS) and the autonomic nervous system (ANS), where involuntary bodily function regulation is achieved. The AIS is an embedded sensoring system component for the autonomic system that assists the autonomic system with signaling the registration of C.H.O.P. properties when approaching a stress threshold classified as dangerous.","PeriodicalId":368197,"journal":{"name":"SoutheastCon 2017","volume":"351 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115896219","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

Synchronous generator stabilization by thyristor controlled supercapacitor energy storage system 晶闸管控制的超级电容器储能系统对同步发电机的稳定

SoutheastCon 2017 Pub Date : 2017-03-01 DOI: 10.1109/SECON.2017.7925264

Md. Rishad Hossain, Md. Kamal Hossain, M. Ali, Yusheng Luo, R. Hovsapian

引用次数: 5

A review on cyber security issues and mitigation methods in smart grid systems 智能电网系统中的网络安全问题及缓解方法综述

SoutheastCon 2017 Pub Date : 2017-03-01 DOI: 10.1109/SECON.2017.7925278

Maneli Malek Pour, Arash Anzalchi, A. Sarwat

引用次数: 26

Development of a Video Game Design program in the college of engineering 工程学院电子游戏设计课程的开发

SoutheastCon 2017 Pub Date : 2017-03-01 DOI: 10.1109/SECON.2017.7925397

J. Ventura, J. Qualls, M. Ventura, Raymond McGinnis, Chad Baker, Breeana Nikaido

{"title":"Development of a Video Game Design program in the college of engineering","authors":"J. Ventura, J. Qualls, M. Ventura, Raymond McGinnis, Chad Baker, Breeana Nikaido","doi":"10.1109/SECON.2017.7925397","DOIUrl":"https://doi.org/10.1109/SECON.2017.7925397","url":null,"abstract":"The global video game industry's revenues reached 7.2 billion in 2007 and over $65 billion in 2012 with projected growth over $80 billion by 2017. As new consoles, PC, and mobile devices penetrate more foreign markets, revenues derived from these devices and software will continue to increase. In a nationally representative sample of U.S. teens, 99% of boys and 94% of girls played video games. Over 45% of women play video games and 30% of people over the age of 50 play games on a regular basis. Overall, 72% of Americans play video games in the U.S. and the amount of time spent playing games continues to increase, as do foreign markets. The increased market capitalization of the industry increases demand of qualified applicants. Other industries leveraging similar technology are beginning to grow and have topped over $10 billion in revenue in 2014. Students with expertise in game and related technology will have an advantage in these new job opportunities. Christian Brothers University (CBU) seeks to fill this need by the creation of a Bachelor of Science in Engineering Management (BSEM - Information Management) in Video Game Design. This degree will give students the skills needed to pursue a career within the game industry and others while developing engineering skills.","PeriodicalId":368197,"journal":{"name":"SoutheastCon 2017","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115459287","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

Towards real-time segmentation of 3D point cloud data into local planar regions 三维点云数据实时分割成局部平面区域

SoutheastCon 2017 Pub Date : 2017-03-01 DOI: 10.1109/SECON.2017.7925321

Aparna Tatavarti, J. Papadakis, A. Willis

{"title":"Towards real-time segmentation of 3D point cloud data into local planar regions","authors":"Aparna Tatavarti, J. Papadakis, A. Willis","doi":"10.1109/SECON.2017.7925321","DOIUrl":"https://doi.org/10.1109/SECON.2017.7925321","url":null,"abstract":"This article describes an algorithm for efficient segmentation of point cloud data into local planar surface regions. This is a problem of generic interest to researchers in the computer graphics, computer vision, artificial intelligence and robotics community where it plays an important role in applications such as object recognition, mapping, navigation and conversion from point clouds representations to 3D surface models. Prior work on the subject is either computationally burdensome, precluding real time applications such as robotic navigation and mapping, prone to error for noisy measurements commonly found at long range or requires availability of coregistered color imagery. The approach we describe consists of 3 steps: (1) detect a set of candidate planar surfaces, (2) cluster the planar surfaces merging redundant plane models, and (3) segment the point clouds by imposing a Markov Random Field (MRF) on the data and planar models and computing the Maximum A-Posteriori (MAP) of the segmentation labels using Bayesian Belief Propagation (BBP). In contrast to prior work which relies on color information for geometric segmentation, our implementation performs detection, clustering and estimation using only geometric data. Novelty is found in the fast clustering technique and new MRF clique potentials that are heretofore unexplored in the literature. The clustering procedure removes redundant detections of planes in the scene prior to segmentation using BBP optimization of the MRF to improve performance. The MRF clique potentials dynamically change to encourage distinct labels across depth discontinuities. These modifications provide improved segmentations for geometry-only depth images while simultaneously controlling the computational cost. Algorithm parameters are tunable to enable researchers to strike a compromise between segmentation detail and computational performance. Experimental results apply the algorithm to depth images from the NYU depth dataset which indicate that the algorithm can accurately extract large planar surfaces from depth sensor data.","PeriodicalId":368197,"journal":{"name":"SoutheastCon 2017","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115561137","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: 9