A Hybrid Machine Learning Method for Intrusion Detection
محل انتشار: ماهنامه بین المللی مهندسی، دوره: 29، شماره: 9
سال انتشار: 1395
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 422
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شناسه ملی سند علمی:
JR_IJE-29-9_009
تاریخ نمایه سازی: 12 دی 1395
چکیده مقاله:
Data security is an important area of concern for every computer system owner. An intrusion detection system is a device or software application that monitors a network or systems for malicious activity or policy violations. Already various techniques of artificial intelligence have been used for intrusion detection. The main challenge in this area is the running speed of the available implementations. In this research work, we present a hybrid approach which is based on the linear discernment analysis and the extreme learning machine to build a tool for intrusion detection. In the proposed method, the linear discernment analysis is used to reduce the dimensions of data and the extreme learning machine neural network is used for data classification. This idea allowed us to benefit from the advantages of both methods. We implemented the proposed method on a microcomputer with core i5 1.6 GHz processor by using machine learning toolbox. In order to evaluate the performance of the proposed method, we run it on a comprehensive data set concerning intrusion detection. The data set is called KDD, which is a version of the data set DARPA presented by MIT Lincoln Labs. The experimental results were organized in related tables and charts. Analysis of the results show meaningful improvements in intrusion detection. In general, compared to the existing methods, the proposed approach works faster with higher accuracy.
کلیدواژه ها:
Intrusion DetectionLinear Discernment AnalysisExtreme Learning Machine
نویسندگان
H.R Hemati
Computer Department, Engineering Campus, Yazd University, Yazd, Iran
M Ghasemzadeh
Assoc. Prof. at Yazd University in Iran and Guest Researcher at HPI, Potsdam, Germany
C Meinel
President and CEO of Hasso Plattner Institute (HPI), at Potsdam University, Potsdam, Germany