Penalty-Reward Based Instance Selection Method in Cloud Environment Using the Concept of Nearest Neighbor

被引：5

作者：

Ghosh, Partha ^{[1
]}

Saha, Akash ^{[1
]}

Phadikar, Santanu ^{[2
]}

机构：

[1] Netaji Subhash Engn Coll, Kolkata 700152, India

[2] Maulana Abul Kalam Azad Univ Technol, Kolkata 700064, India

来源：

TWELFTH INTERNATIONAL CONFERENCE ON COMMUNICATION NETWORKS, ICCN 2016 / TWELFTH INTERNATIONAL CONFERENCE ON DATA MINING AND WAREHOUSING, ICDMW 2016 / TWELFTH INTERNATIONAL CONFERENCE ON IMAGE AND SIGNAL PROCESSING, ICISP 2016 | 2016年 / 89卷

关键词：

Cloud Computing; Instance Selection; Intrusion Detection System (IDS); NSL-KDD Dataset; Reverse Nearest Neighbor Reduction (RNNR); ALGORITHM;

D O I：

10.1016/j.procs.2016.06.012

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Cloud computing is the distribution of computing resources over the Internet. A shared pool of resources, including data storage space, computer processing power and applications are provided by Cloud computing. In spite of being attractive, it challenges with new security threats when it comes to deploying an Intrusion Detection System (IDS) in Cloud environment. It requires a lot of time to process the Cloud dataset and produce proper classification strategy. A Penalty-Reward based instance selection method to reduce the Cloud dataset is proposed here. Using this method all the noisy and boundary instances are removed from the training dataset. After that Reverse Nearest Neighbor Reduction (RNNR) method is applied on the remaining instances to select all relevant instances from them. This helps to reduce the training time as well as to produce better classification accuracy for IDS. (C) 2016 The Authors. Published by Elsevier B.V.

引用

页码：82 / 89

页数：8

共 25 条

[11] CONDENSED NEAREST NEIGHBOR RULE [J].