International Conference on Critical Infrastructure Protection最新文献_第6页

An introduction and review of petri net unfolding technology petri网展开技术的介绍与综述

International Conference on Critical Infrastructure Protection Pub Date : 2018-11-02 DOI: 10.1145/3290420.3290430

R. Tao, Faming Lu, Xueping Zhang, Guanye Zhu

引用次数: 1

Hardware implementation of real-time ECG R-wave detection with wavelet transform algorithm 基于小波变换算法的实时心电r波检测的硬件实现

International Conference on Critical Infrastructure Protection Pub Date : 2018-11-02 DOI: 10.1145/3290420.3290445

T. Zhuang, Chen Feng, Liang-Hung Wang, Jie Gao, Yi-Ting Yang

引用次数: 2

An increment detection algorithm to mitigate ISI for molecular communication based on drift diffusion 基于漂移扩散的分子通信ISI增量检测算法

International Conference on Critical Infrastructure Protection Pub Date : 2018-11-02 DOI: 10.1145/3290420.3290440

Xinlei Wang, Zhenqiang Wu, Jiawang Chen, Bo Liu

引用次数: 2

A beautiful image or not: a comparative study on classical machine learning and deep learning 美不美:经典机器学习与深度学习的比较研究

International Conference on Critical Infrastructure Protection Pub Date : 2018-11-02 DOI: 10.1145/3290420.3290463

Ying Zhang, Zhaotong Li, Qinpei Zhao, Hongfei Fan, Weixiong Rao, Jessie Chen

{"title":"A beautiful image or not: a comparative study on classical machine learning and deep learning","authors":"Ying Zhang, Zhaotong Li, Qinpei Zhao, Hongfei Fan, Weixiong Rao, Jessie Chen","doi":"10.1145/3290420.3290463","DOIUrl":"https://doi.org/10.1145/3290420.3290463","url":null,"abstract":"With the development of web services and Apps on the Internet, food images are emerging into our life. Consumers from yelp or the dianping service upload a lot of food pictures every day. The images usually express the users' feelings and are shared among the social network. There have been different researches on the images. However, there is few research on how to evaluate the food image is beautiful or not. Therefore, we came up with an idea to classify food pictures by their appearance, which is meaningful in multiple applications, especially picking beautiful pictures to help businesses attract customers. In order to realize this idea, we collected 1067 food images through web crawling and questionnaires. Each image has a unique label: beautiful or not beautiful. Machine learning methods are used in this paper to model the data. CNN models in deep learning: VGGNet, AlexNet, and ResNet can get good results, e.g., ResNet can achieve the accuracy of 95.83%. However, with a good feature engineering job, the classifiers, which are random forest and support vector machine can reach a better accuracy of 99.63%. The experimental results indicate feature engineering is a vital issue in the food image evaluation problem, which lacks of labeled data.","PeriodicalId":259201,"journal":{"name":"International Conference on Critical Infrastructure Protection","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127233062","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

Route leakage detection algorithm based on new feature discovery 基于新特征发现的路由泄漏检测算法

International Conference on Critical Infrastructure Protection Pub Date : 2018-11-02 DOI: 10.1145/3290420.3290437

Liying Cheng, Pei Zhang, Yan Ma

引用次数: 0

Portable computer vision-based cardiac estimation as a teaching aid 基于便携式计算机视觉的心脏评估教具

International Conference on Critical Infrastructure Protection Pub Date : 2017-11-24 DOI: 10.1145/3162957.3163006

D. Haar

{"title":"Portable computer vision-based cardiac estimation as a teaching aid","authors":"D. Haar","doi":"10.1145/3162957.3163006","DOIUrl":"https://doi.org/10.1145/3162957.3163006","url":null,"abstract":"The prevalence of pervasive computing has made computing platforms more portable and has introduced an array of sensors that are useful for many applications. The potential of these sensors are finally being realized in many fields. One area that is especially benefiting from these sensors is the field of the biometrics. However, we are still grappling with user acceptance issues such as intrusiveness and hygiene problems, which stifle the uptake of the technology. This study investigates the potential of using one of the more common sensors, a portable device camera, and using it to assist educators. By implementing a system that captures heart rate in a novel manner, a basic affective biometric system is formed that requires no contact and is portable. The system segments relevant areas that highlight blood flow in the face, extrapolates heart rate variability through color space changes. By analyzing the extent of color change, a cardiac waveform can be formed with a QRS complex derivative, which can be used for the task of sentiment classification. The sentiment derived can then be used by an educator to inform them of any potential uncertainty during their teaching in real-time. Abrupt changes in sentiment can then be addressed during the class, thereby improving the potential uptake of concepts taught in a classroom. The study validates that it is possible to derive heart rate variability using a camera, it also shows that using this heart rate for basic sentiment classification is feasible using a portable device even with limited resources and it warrants more attention.","PeriodicalId":259201,"journal":{"name":"International Conference on Critical Infrastructure Protection","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132428107","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

Using Text Mining to Evaluation for Online Shopping Rural Fruits and Vegetables 基于文本挖掘的农村果蔬网上购物评价

International Conference on Critical Infrastructure Protection Pub Date : 1900-01-01 DOI: 10.1145/3571662.3571671

Chien-Hua Wang, Chia-Hsuan Yeh, Chin-Tzong Pang

引用次数: 0

Malicious Code Classification Method of Advanced Persistent Threat Based on Asm2Vec 基于Asm2Vec的高级持续威胁恶意代码分类方法

International Conference on Critical Infrastructure Protection Pub Date : 1900-01-01 DOI: 10.1145/3571662.3571676

Kaijie Liu, Wei Hu, Jianyi Liu, Jie Cheng, Yating Gao, Jin Pang

{"title":"Malicious Code Classification Method of Advanced Persistent Threat Based on Asm2Vec","authors":"Kaijie Liu, Wei Hu, Jianyi Liu, Jie Cheng, Yating Gao, Jin Pang","doi":"10.1145/3571662.3571676","DOIUrl":"https://doi.org/10.1145/3571662.3571676","url":null,"abstract":"In recent years, network security has become the main factor that threatens the development of the Internet. Among the network security threats, advanced persistent threat (APT) is one of the most representative attacks and has brought unprecedented security challenges. APT attacks mainly depend on malicious code. At present, the homology analysis of malicious code for APT mainly converts the malicious code into a gray image or semantic fragment, which is realized by pre-training models such as neural network. The effect of the method based on pre-training depends heavily on the training process of the model and the form of the data set, which may lead to misjudgment of the organization of the malicious code in an APT real-time attack. In this paper, we propose a homology analysis of malicious code for APT groups based on Asm2Vec. The basic function blocks are obtained by disassembling and removing unimportant functions from the malicious code. The semantic representation model Asm2Vec is used to analyze and find out the possible APT group for targeted malware. The experimental results show that the Energetic Bear group classification accuracy of this paper is 91.30% and the F1-Score is 95.46%.","PeriodicalId":259201,"journal":{"name":"International Conference on Critical Infrastructure Protection","volume":"120 27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122533994","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