An Intrusion Detection Framework With Classification Using Support Vector Machine
Author(s):
Musali Umesh Kumar Reddy, Shapuram Harsha, Digavabasi Reddy Yeshwanth Reddy, Shaik Tabariz Ahammed, A. Gowtham
Keywords:
NSL–KDD, Logistic Regression, k-nearest neighbor, Random forest, Support vector machine, Intrusion Detection and System.
Abstract
Intrusion detection is a very important component of security technologies which include adaptive security appliances, intrusion and detection systems in other words intrusion prevention systems, and firewalls. There rises an issue with the performance of different intrusion detection algorithms that are deployed. The effectiveness of intrusion detection relies on accuracy, which must be raised in order to reduce false alarms and boost detection rates. Recent work has employed techniques such as multilayer perceptron and' support vector machine (SVM) t' to address performance difficulties. These methods have drawbacks and' be ineffective when applied to' massive data sets, such systems and' network data. Large volumes of traffic data are analyzed by the intrusion detection system; thus, an effective classification method is required to solve the issue. This study examines this matter. Popular machine learning methods are used, including' random forest, extreme learning machine (ELM), an' SVM. These methods' reputation stems from their ability to' classify different data. Utilized by the NSL-knowledge discovery and data mining data set, which are regarded as a standard for ' attacker’ intrusion detection systems. The outcomes show that ELM works better'n alternative strategies!!!!
Article Details
Unique Paper ID: 164121

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 114 - 119
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