2015 International Conference on Pervasive Computing (ICPC)最新文献_第10页

Design consideration of Network Intrusion detection system using Hadoop and GPGPU 基于Hadoop和GPGPU的网络入侵检测系统的设计思考

2015 International Conference on Pervasive Computing (ICPC) Pub Date : 2015-04-16 DOI: 10.1109/PERVASIVE.2015.7087201

Sanraj Rajendra Bandre, J. Nandimath

{"title":"Design consideration of Network Intrusion detection system using Hadoop and GPGPU","authors":"Sanraj Rajendra Bandre, J. Nandimath","doi":"10.1109/PERVASIVE.2015.7087201","DOIUrl":"https://doi.org/10.1109/PERVASIVE.2015.7087201","url":null,"abstract":"Modern computing has primarily shifted towards the distributed environment using commodity resources which results in increase in data and its security concern. This paper deals with design consideration of Network Intrusion Detection System (NIDS) based on the Hadoop framework and acceleration of its performance by using General Purpose Graphical Processing Unit (GPGPU). The large volume of data from an entire infrastructure is assigned to Hadoop framework and intrusion detections are carried out on GPGPU. This approach improves NIDS performance and it enables to provide quick response to various attacks on the network. In order to perform the general purposed computation on the GPU, NVidia provides the Compute Unified Device Architecture (CUDA) which is a parallel programming model which performs high-end complex operations using GPU. In order to process large volumes of data in distributed networks, Hadoop framework has to configure with various supporting ecosystems like Flume, Pig, Hive and HBase. These ecosystems enable the Hadoop framework to handle streaming data on the network and large log files on servers. The proposed system is capable of performing analytics over intrusion pattern and their behavior on the network, which helps a network administrator to configure network security policy and settings. Analytics over intrusion is done by using a Score-Weight approach called as Pattern Frequency Inverse Cluster Frequency (PF-ICF). The design consideration of accelerated NIDS is a solution towards the performance issues of various NIDS that faces due to the large volumes of the network traffic.","PeriodicalId":442000,"journal":{"name":"2015 International Conference on Pervasive Computing (ICPC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116606945","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

Moving object detection: Review of recent research trends 运动目标检测:最新研究趋势综述

2015 International Conference on Pervasive Computing (ICPC) Pub Date : 2015-04-16 DOI: 10.1109/PERVASIVE.2015.7087138

Jaya S. Kulchandani, Kruti Dangarwala

{"title":"Moving object detection: Review of recent research trends","authors":"Jaya S. Kulchandani, Kruti Dangarwala","doi":"10.1109/PERVASIVE.2015.7087138","DOIUrl":"https://doi.org/10.1109/PERVASIVE.2015.7087138","url":null,"abstract":"Moving object detection is the task of identifying the physical movement of an object in a given region or area. Over last few years, moving object detection has received much of attraction due to its wide range of applications like video surveillance, human motion analysis, robot navigation, event detection, anomaly detection, video conferencing, traffic analysis and security. In addition, moving object detection is very consequential and efficacious research topic in field of computer vision and video processing since it forms a critical step for many complex processes like video object classification and video tracking activity. Consequently, identification of actual shape of moving object from a given sequence of video frames becomes pertinent. However, task of detecting actual shape of object in motion becomes tricky due to various challenges like dynamic scene changes, illumination variations, presence of shadow, camouflage and bootstrapping problem. To reduce the effect of these problems, researchers have proposed number of new approaches. This paper provides a brief classification of the classical approaches for moving object detection. Further, paper reviews recent research trends to detect moving object for single stationary camera along with discussion of key points and limitations of each approach.","PeriodicalId":442000,"journal":{"name":"2015 International Conference on Pervasive Computing (ICPC)","volume":"65 Pt 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123800475","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: 99

Design and implementation of successive cancellation decoder 逐次对消解码器的设计与实现

2015 International Conference on Pervasive Computing (ICPC) Pub Date : 2015-04-16 DOI: 10.1109/PERVASIVE.2015.7087210

Sachin A. Desale, Prof M M Jadhav

{"title":"Design and implementation of successive cancellation decoder","authors":"Sachin A. Desale, Prof M M Jadhav","doi":"10.1109/PERVASIVE.2015.7087210","DOIUrl":"https://doi.org/10.1109/PERVASIVE.2015.7087210","url":null,"abstract":"Polar Codes, a coding scheme was introduced by Arikan to achieve the symmetric channel capacity. Encoder is constructed with the 32 block-length to transmit data through various channels like binary symmetric channel (BSC) and binary erasure channel (BEC), at decoder data is decoded successively. By channel polarization it gets recursively combined and split the independent binary input discrete memoryless channel (B-DMC) of these get some of channel become error free to achieve the symmetric capacity and other are useless ones. In polarized channel coding send information bit through noiseless or clean channel and information is not send through any noisy channel. In this paper, the implementation results shows that when the encoded bits are passed through a encoder of size 1024 gives 30% of channel having zero capacity and 40% channels having one capacity. Similarly the receiver are implemented using FFT structure and successive cancellation structure on a discrete board. It shows that the computational time required for FFT Structure of 10240 computation is 10.24ms and the computation time for SC decoder structure of 5120 computation is 5.12ms for 1024 block length.","PeriodicalId":442000,"journal":{"name":"2015 International Conference on Pervasive Computing (ICPC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122213806","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

Novel multimodal identification technique using Iris & Palmprint traits with various matching score level proportions using BTC of bit plane slices 基于比特平面切片的BTC，利用不同匹配分数水平比例的虹膜和掌纹特征进行多模态识别

2015 International Conference on Pervasive Computing (ICPC) Pub Date : 2015-04-16 DOI: 10.1109/PERVASIVE.2015.7087147

Sudeep D. Thepade, Rupali K. Bhondave

引用次数: 7

WiFiPi-Tracking at mass events 大规模活动中的wifi追踪

2015 International Conference on Pervasive Computing (ICPC) Pub Date : 2015-04-16 DOI: 10.1109/PERVASIVE.2015.7087170

P. Patil, A. Kokil

引用次数: 10

Morphology based non ideal iris recognition using decision tree classifier 基于形态学的决策树分类非理想虹膜识别

2015 International Conference on Pervasive Computing (ICPC) Pub Date : 2015-04-16 DOI: 10.1109/PERVASIVE.2015.7087104

V. V. Satyanarayana Tallapragada, E. Rajan

引用次数: 12

Capturing, understanding and interpreting user interactions with the browser as implicit interest indicators 捕捉、理解和解释用户与浏览器的交互作为隐含的兴趣指示器

2015 International Conference on Pervasive Computing (ICPC) Pub Date : 2015-04-16 DOI: 10.1109/PERVASIVE.2015.7087075

S. Zahoor, Digvijaysingh Rajput, Dr Mangesh Bedekar, Pranali K. Kosamkar

引用次数: 4

Review on privacy preservation method by applying discrimination rules in data mining 基于区分规则的数据挖掘隐私保护方法综述

2015 International Conference on Pervasive Computing (ICPC) Pub Date : 2015-04-16 DOI: 10.1109/PERVASIVE.2015.7087192

Priya Meshram, S. Bodkhe

引用次数: 3

Tuning of fuzzy controller using set point weighing PID parameters 采用设定点称重PID参数对模糊控制器进行整定

2015 International Conference on Pervasive Computing (ICPC) Pub Date : 2015-04-16 DOI: 10.1109/PERVASIVE.2015.7087025

N. Patil, R. P. Borase

引用次数: 0

Infrared-based system for vehicle counting and classification 基于红外的车辆计数和分类系统

2015 International Conference on Pervasive Computing (ICPC) Pub Date : 2015-04-16 DOI: 10.1109/PERVASIVE.2015.7086998

Hemanshu S. Khatri, S. Somani

引用次数: 3