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Title |
Proposing a Hierarchical Classifier to Detect Attack in Network Intrusion Detection |
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Author |
Amin Shahraki Moghaddam, Javad Hosseinkhani, Anoosh Mansouri Birgani, Amirreza Sardarzadeh, Zeynab Sayad Arbabi, and Sadegh Gilani |
| Citation |
Vol. 25 No. 4 pp. 155-158 |
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Abstract |
The task of intrusion detection system intrusion detection and disclosure practices are responsible. This system monitors network traffic and reports by user activity, detects illegal activities. Detect, identify and classify classes of attacks on computer networks, one of the major challenges in the field of intrusion detection is to determine the type of attack class. Neural networks, support vector machines and Bayesian networks as a classifier to classify and identify the type of attacks are used. Many researches have been conducted using a combination of the classifier. This classifier with putting together several different classifiers to detect attacks that are used to determine the type.be used as it is challenging. The classifier of support vector machine and a neural network classifier to determine the best of each class have detected the attack. And also the best way to arrange those bands that plays a big part in yield is proposed. Simulation results show that the proposed classifier can improve the classification performance better than similar acts. |
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Keywords |
Intrusion Detection, Support Vector Machine (SVM), Neural Network, Hierarchical Classifier. |
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