2016 8th International Conference on Computational Intelligence and Communication Networks (CICN)最新文献_第2页

Autonomous Agent Based Shortest Path Load Balancing in Cloud 基于自治代理的云系统最短路径负载均衡

2016 8th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2016-12-01 DOI: 10.1109/CICN.2016.15

Aarti Vig, R. S. Kushwah, R. S. Tomar, S. Kushwah

引用次数: 4

Nature-Inspired High-Directivity Microstrip Antennas: Fractals and Genetics 自然启发的高指向性微带天线:分形和遗传学

2016 8th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2016-12-01 DOI: 10.1109/CICN.2016.46

J. Anguera, A. Andújar, J. Jayasinghe, D. Uduwawala, M. K. Khattak, S. Kahng

引用次数: 3

Low Delay Based Full Adder/Subtractor by MIG and COG Reversible Logic Gate 基于MIG和COG可逆逻辑门的低延迟全加/减法器

2016 8th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2016-12-01 DOI: 10.1109/CICN.2016.120

Jacob B. Chacko, P. Whig

引用次数: 6

Improvement of Network Intrusion Detection Accuracy by Using Restricted Boltzmann Machine 利用受限玻尔兹曼机提高网络入侵检测精度

2016 8th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2016-12-01 DOI: 10.1109/CICN.2016.87

Sang-Il Seo, Seongchul Park, Juntae Kim

{"title":"Improvement of Network Intrusion Detection Accuracy by Using Restricted Boltzmann Machine","authors":"Sang-Il Seo, Seongchul Park, Juntae Kim","doi":"10.1109/CICN.2016.87","DOIUrl":"https://doi.org/10.1109/CICN.2016.87","url":null,"abstract":"In various data of network intrusion detection used for classification algorithm's learning, a great deal of noise and outlier data are mixed. In case of a learning performed by using data of high impurities, no matter how the performance of classification algorithm is outstanding, any network intrusion detection model of high performance becomes hard to anticipate. To increase the accuracy of network intrusion detection, not only the performance of classification algorithm should be increased but also the management on noises and outliers in the data used for the classification algorithm's learning. Restricted Boltzmann Machine (RBM) is a type of unsupervised learning that doesn't use class labels. RBM is a probabilistic generative model that composes new data on input data based on the trained probability. The new data composed through RBM show that the noises and outliers are removed from the input data. When the newly composed data are applied to the network intrusion detection model, negative effects from the noise and outlier data to the learning are eliminated. In this study, noises and outliers in KDD Cup 1999 Data are removed by applying the data to RBM and composing a new data. Then, use results between the existing data and the data from which noises and outliers are removed are compared. In conclusion, this study demonstrates the performance improvement of network intrusion detection resulted by removing noises and outliers included in the data through RBM.","PeriodicalId":189849,"journal":{"name":"2016 8th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"40 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114473191","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

A Modified more Rapid Sequential Extreme Learning Machine 一种改进的更快速的顺序极限学习机

2016 8th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2016-12-01 DOI: 10.1109/CICN.2016.72

Meiyi Li, Xiao Zhang

{"title":"A Modified more Rapid Sequential Extreme Learning Machine","authors":"Meiyi Li, Xiao Zhang","doi":"10.1109/CICN.2016.72","DOIUrl":"https://doi.org/10.1109/CICN.2016.72","url":null,"abstract":"The speed of machine learning has been a concern of the people. The speed of Extreme Learning Machine (ELM) has been improved very faster than others. However, the speed of Sequential Extreme Learning Machine is still slow. So, a fast sequence Extreme Learning Machine (Fast Sequential Extreme Learning Machine, FS-ELM) is present by the use of iterative calculation in calculation of the output weights at obtaining input weights and hidden bias randomly. Independent parts of data on the hidden layer are superimposed after acquiring the sequence training data. Then the output weights are obtained with calculation formula. In the initialization of the learning phase during training FS-ELM can accept any number of training data without affecting the accuracy of training and test impact. FS-ELM has a faster speed increase compared to OS-ELM in data training, and it ensure the test accuracy is quite similar comparing with ELM and Online Sequence Extreme Learning Machine OS-ELM. In order to verify the speed and accuracy performance which FS-ELM possesses, a number of adequate comparative experiments on different scale datasets are conducted.","PeriodicalId":189849,"journal":{"name":"2016 8th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117230677","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

Mitigation of Blackhole attack in MANET 无线局域网中黑洞攻击的缓解

2016 8th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2016-12-01 DOI: 10.1109/CICN.2016.171

Avni Tripathi, A. Mohapatra

{"title":"Mitigation of Blackhole attack in MANET","authors":"Avni Tripathi, A. Mohapatra","doi":"10.1109/CICN.2016.171","DOIUrl":"https://doi.org/10.1109/CICN.2016.171","url":null,"abstract":"Mobile ad hoc network (MANET) is a technology of spontaneous creation of ad hoc network for data exchange, in which nodes can act as end nodes or can act as router. In hostile environment some node may deny to forward packet in order to disrupt regular communications, this type of abnormal behavior is referred as packet drop attack or blackhole attack. Most of the techniques that are used to mitigate blackhole attack are based on the use of destination sequence number as a criterion to identify blackhole node. However, these schemes have limitation of quantifying the value of destination sequence number that is used to identify a blackhole node. As a result, attacker can change the value of destination sequence number according to value present in RREQ (Route REQuest) packet. In the proposed approach we have used hop count value to identify a blackhole node. This hop count is utilized by means of confirmation packet in this scheme. The advantage of proposed work is that blackhole node is not identified based upon destination sequence number value. The proposed work has been implemented in QualNet and result shows that blackhole node is identified.","PeriodicalId":189849,"journal":{"name":"2016 8th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128681376","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: 8

Certain Investigation in DNS Performance by Using Accelerator and Stub Network 利用加速器和存根网络对DNS性能的研究

2016 8th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2016-12-01 DOI: 10.1109/CICN.2016.39

K. S. Kumar, G. Venkatesan

{"title":"Certain Investigation in DNS Performance by Using Accelerator and Stub Network","authors":"K. S. Kumar, G. Venkatesan","doi":"10.1109/CICN.2016.39","DOIUrl":"https://doi.org/10.1109/CICN.2016.39","url":null,"abstract":"The Domain Name System (DNS) has transformed into a bit of enrolling due to its effectiveness and scalability. It gives a lookup organization to change over from understandable names to web tradition addresses. The result exhibits that the client side feebleness in DNS lookups is reasonably standard. Frustration cases degenerate the ordinary lookup time and improve the tail of response times. These sorts of disillusionments appear, in every way, to be brought on by an over-weight of the impermanent name server, and they are uncorrelated over various destinations. In an examination of live activity, demonstrates that a peering framework lessens reaction times and enhances devotion. With these perceptions, to build up a name lookup administration, SubDNS that utilizations peers in the remote destinations to give steady lookups amid disappointments SubDNS works in conjunction with neighborhood DNS, permitting the organization incrementally without the huge asset utilization. In any case, it can't keep on meeting the developing interest without new framework. Towards this, to be planned the DNS with the accelerator. The storing circulates the heap far from the server in which problem areas raised by mainstream data objects, shields the system from mistaken customers and decreases access inertness.","PeriodicalId":189849,"journal":{"name":"2016 8th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126766913","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

Various Mixed Approaches of Model Order Reduction 模型降阶的各种混合方法

2016 8th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2016-12-01 DOI: 10.1109/CICN.2016.138

Preeti Verma, P. K. Juneja, M. Chaturvedi

引用次数: 6

Contemporary Intelligent Amalgamation of Knowledge Management and Retrieval Technique for IR 当代IR知识管理与检索技术的智能融合

2016 8th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2016-12-01 DOI: 10.1109/CICN.2016.129

A. Khodaskar, S. Ladhake

引用次数: 0

Internet Protocol Identification Number Based Ideal Stealth Port Scan Detection Using Snort 基于Internet协议识别码的理想隐身端口扫描检测

2016 8th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2016-12-01 DOI: 10.1109/CICN.2016.89

S. Patel, Abhilash Sonker

{"title":"Internet Protocol Identification Number Based Ideal Stealth Port Scan Detection Using Snort","authors":"S. Patel, Abhilash Sonker","doi":"10.1109/CICN.2016.89","DOIUrl":"https://doi.org/10.1109/CICN.2016.89","url":null,"abstract":"Port scanning is a reconnaissance phase of networking and many researchers have implemented different techniques to secure the network from port scan attacks. Intrusion Detection System (IDS) is also one of them and SNORT is an open source tool for Intrusion Detection and Prevention System. Today port scanning is a growing technology in network security to perform penetration testing and hacking and mostly researchers have focused in this field to detect stealth port scan attacks named as FIN scan, XMUS and NULL scan. To detect these attacks some of them used signature or rule-based technique and some are anomaly based techniques to improve security of network. In above techniques rules used FIN, PSH, and URG flag to detect attack but in case of idle port scan attack rules used FIN and RST flags which is also part of TCP connect() method so using this flag directly will generate the false alarm. In this paper we propose an IP identification number based detection plug-in to detect idle port scan attack. In this proposed techniques we will able to detect the idle port scan attack using FIN and RST flag with IP ID number of packet.","PeriodicalId":189849,"journal":{"name":"2016 8th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125334814","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: 13