Programmable Device Circuits and Systems最新文献

Centralized Parallel form of Pattern Matching Algorithm in Packet Inspection by Efficient Utilization of Secondary Memory in Network Processor 数据包检测中模式匹配算法的集中式并行形式，有效利用网络处理器中的二级存储器

Programmable Device Circuits and Systems Pub Date : 2012-02-29 DOI: 10.5120/4951-7194

N. Raja, K. Arulanandam, B. Rajarajeswari

{"title":"Centralized Parallel form of Pattern Matching Algorithm in Packet Inspection by Efficient Utilization of Secondary Memory in Network Processor","authors":"N. Raja, K. Arulanandam, B. Rajarajeswari","doi":"10.5120/4951-7194","DOIUrl":"https://doi.org/10.5120/4951-7194","url":null,"abstract":"The network equipment has capable of inspecting packets in order to discover the worms and virus over the network. Many network users are hacked by attackers through malicious functions are mapped on network applications. Such unauthorized activities are required to delete by deep packet inspection in application layer. The high level network equipment provides in-depth packet inspection through pattern matching in network detection system. Researchers have developed high performance parallel deep packet filters for reconfigurable devices. Although some reconfigurable systems can be generated automatically from pattern database, obtaining high performance result from each subsequent reconfiguration can be a time consuming process. By presenting a novel architecture for programmable centralized parallel pattern matching algorithm for efficient packet inspection with network processor and coprocessor in order to retrieve the pattern with less time. Produce a hybrid system that is able to update the rules immediate during the time the new filter is being compiled reduction of resource-intensive task and increasing bandwidth used snort rule. . We mapped our centralized multi parallel pattern matching algorithm [CNMPPMA] for filter packet in parallel. The simulation result reveals that CNMPPMA significantly improves the matching performance. Also achieves the matching process with less cost.","PeriodicalId":428598,"journal":{"name":"Programmable Device Circuits and Systems","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115638993","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

FPGA Implementation of Braun’s Multiplier Using Spartan-3E, Virtex – 4, Virtex-5 and Virtex-6 基于Spartan-3E, Virtex- 4, Virtex-5和Virtex-6的Braun乘法器的FPGA实现

Programmable Device Circuits and Systems Pub Date : 2011-07-15 DOI: 10.1007/978-3-642-22543-7_49

R. Anitha, V. Bagyaveereswaran

引用次数: 6

Simulation and Hardware Analysis of Three Phase PWM Rectifier for Power Factor Correction 用于功率因数校正的三相PWM整流器仿真及硬件分析

Programmable Device Circuits and Systems Pub Date : 1900-01-01 DOI: 10.9790/1676-0812733

P. Manikandan, S. Umayal, A. Mary

引用次数: 7

PEARSON CORRELATION COEFFICIENT K-NEAREST NEIGHBOR OUTLIER CLASSIFICATION ON REAL-TIME DATASETS 实时数据集的Pearson相关系数k -最近邻离群值分类

Programmable Device Circuits and Systems Pub Date : 1900-01-01 DOI: 10.21917/ijsc.2020.0290

D. Rajakumari, S. Karthika

{"title":"PEARSON CORRELATION COEFFICIENT K-NEAREST NEIGHBOR OUTLIER CLASSIFICATION ON REAL-TIME DATASETS","authors":"D. Rajakumari, S. Karthika","doi":"10.21917/ijsc.2020.0290","DOIUrl":"https://doi.org/10.21917/ijsc.2020.0290","url":null,"abstract":"Detection and classification of data that do not meet the expected behavior (outliers) plays the major role in wide variety of applications such as military surveillance, intrusion detection in cyber security, fraud detection in on-line transactions. Nowadays, an accurate detection of outliers with high dimension is the major issue. The trade-off between the high-accuracy and low computational time is the major requirement in outlier prediction and classification. The presence of large size diverse features need the reduction mechanism prior to classification approach. To achieve this, the Distance-based Outlier Classification (DOC) is proposed in this paper. The proposed work utilizes the Pearson Correlation Coefficient (PCC) to measure the correlation between the data instances. The minimum instance learning through PCC estimation reduces the dimensionality. The proposed work is split up into two phases namely training and testing. During the training process, the labeling of most frequent samples isolates them from the infrequent reduce the data size effectively. The testing phase employs the k-Nearest Neighborhood (k-NN) scheme to classify the frequent samples effectively. The dimensionality and the k-value are inversely proportional to each other. In proposed work, the selection of large value of k offers the significant reduction in dimensionality. The combination of PCC-based instance learning and the high value of k reduces the dimensionality and noise respectively. The comparative analysis between the proposed PCC-k-NN with the conventional algorithms such as Decision Tree, Naive Bayes, Instance-Based K-means (IBK), Triangular Boundary-based Classification (TBC) regarding sensitivity, specificity, accuracy, precision, and recall proves its effectiveness in OC. Besides, the experimental validation of proposed PCC-k-NN with the state-of art methods regarding the execution time assures trade-off between the low-time consumption and high-accuracy.","PeriodicalId":428598,"journal":{"name":"Programmable Device Circuits and Systems","volume":"46 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":"131236238","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