ABSTRACT:
Intrusion detection is a fundamental
part of security tools, such as adaptive security appliances, intrusion
detection systems, intrusion prevention systems, and firewalls. Various
intrusion detection techniques are used, but their performance is an issue.
Intrusion detection performance depends on accuracy, which needs to improve to
decrease false alarms and to increase the detection rate. To resolve concerns
on performance, multilayer perceptron, support vector
machine (SVM), and other techniques have been used in recent work. Such
techniques indicate limitations and are not efficient for use in large data
sets, such as system and network data. The intrusion detection system is used
in analyzing huge traffic data; thus, an efficient classification technique is
necessary to overcome the issue. This problem is considered in this paper.
Well-known machine learning techniques, namely, SVM, random forest, and extreme
learning machine (ELM) are applied. These techniques are well-known because of
their capability in classification. The NSL_knowledge
discovery and data mining data set is used, which is considered a benchmark in
the evaluation of intrusion detection mechanisms. The results indicate that ELM
outperforms other approaches.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
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System : Pentium Dual Core.
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Hard Disk : 120 GB.
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Monitor : 15’’ LED
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Input Devices : Keyboard, Mouse
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Ram : 1 GB
SOFTWARE REQUIREMENTS:
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Operating system : Windows 7.
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Coding Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database : MYSQL
REFERENCE:
IFTIKHAR AHMAD 1,
MOHAMMAD BASHERI1, MUHAMMAD JAVED IQBAL, AND ANEEL RAHIM, “Performance
Comparison of Support Vector Machine, Random Forest, and Extreme Learning
Machine for Intrusion Detection”, IEEE Access, 2018.