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Enhancement Methods of Intrusion Detection Systems Using Artificial Intelligence Methods "TLBO" Algorithm

المصدر: مجلة ميسان للدراسات الأكاديمية
الناشر: جامعة ميسان - كلية التربية الأساسية
المؤلف الرئيسي: Al-Hammash, Mohammed Saeed Hashim (Author)
مؤلفين آخرين: Maarouf, Haitham (Co-Author)
المجلد/العدد: مج23, ع49
محكمة: نعم
الدولة: العراق
التاريخ الميلادي: 2024
الشهر: آذار
الصفحات: 105 - 112
DOI: 10.54633/2333-023-049-010
ISSN: 1994-697X
رقم MD: 1458145
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: EduSearch, HumanIndex
مواضيع:
كلمات المؤلف المفتاحية:
IDS | TLBO | SVM | Feature Selection
رابط المحتوى:
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المستخلص: Many methods have been used to build intrusion detection system based on the objective to be achieved in the prescribed manner. Hybrid methods (multiple methods) usually give better results and accuracy. The recent developments and popularization of network & information technologies have necessitated the need for network information security. Human-based smart intrusion detection systems (IDSs) are built with the capability to either warn or intercept network intrusion; this is not possible with the conventional network security systems. However, most information security studies have focused on improvement of the effectiveness of smart network IDSs. This study used TLBO algorithm as a feature selection algorithm to choose the best subset features and SVM classifier to classify the packet if it is intrusion or normal packet, two machine learning datasets used to test the proposed algorithm, the results show that the proposed algorithm perform better than many of the existing work in IDS.

ISSN: 1994-697X

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