التفاصيل البيبلوغرافية
| العنوان: |
IDS-MIU: An Intrusion Detection System Based on Machine Learning Techniques for Mixed type, Incomplete, and Uncertain Data Set. |
| المؤلفون: |
Riyadh, Musaab, Ali, Basim Jamil, Alshibani, Dina Riadh |
| المصدر: |
International Journal of Intelligent Engineering & Systems; 2021, Vol. 14 Issue 3, p493-502, 10p |
| مصطلحات موضوعية: |
MACHINE learning, SUPPORT vector machines, RANDOM forest algorithms, DATA integrity, NETWORK PC (Computer), COMPUTER networks |
| مستخلص: |
The rapid growth of computer networks has led to massive flow of data every second. Some of these data flow is a malicious activity and cannot detect by anti-malware and firewalls. Therefore, an intrusion detection system is an urgent issue aims to distinguish between non relevant and relevant data in order to maintain data availability and integrity. Due to this, a hybrid intrusion detection system is proposed in this study based on machine learning techniques to tackle various challenging issues in data set such as mixed type data, incomplete, and uncertain data. The proposed system has achieved its objectives by supporting: Firstly, the density based clustering approach due to its robustness to noise removal. Secondly, the K means and K-nearest neighbour algorithms to transform n dimensional data into one dimensional data in order to deal with mixed type data and minimize the running time. Finally, a special type of dissimilarity measure has been supported to tackle the problem of missing data. The experimental results illustrate that the proposed classifier has better or similar classification accuracy (92.9%) as compared with support vector machine (91.5%), CANN(92.2%), Random forest(93.3%), and EIDS-ACC- OD(91.9%) classifiers in KDD Cup99 data set. However, the proposed classifier has the best performance (92.2%) and (91.2%) when randomly removing 5% and 10% of the KDD Cup99 data set in spite of decreasing the overall accuracy classification for all the classifiers. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |