Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System
المؤلفون المشاركون
Aljanabi, Mohammad
Ismail, Mohd Arfian
Mezhuyev, Vitaly
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-18، 18ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-05-31
دولة النشر
مصر
عدد الصفحات
18
التخصصات الرئيسية
الملخص EN
Many optimisation-based intrusion detection algorithms have been developed and are widely used for intrusion identification.
This condition is attributed to the increasing number of audit data features and the decreasing performance of human-based smart intrusion detection systems regarding classification accuracy, false alarm rate, and classification time.
Feature selection and classifier parameter tuning are important factors that affect the performance of any intrusion detection system.
In this paper, an improved intrusion detection algorithm for multiclass classification was presented and discussed in detail.
The proposed method combined the improved teaching-learning-based optimisation (ITLBO) algorithm, improved parallel JAYA (IPJAYA) algorithm, and support vector machine.
ITLBO with supervised machine learning (ML) technique was used for feature subset selection (FSS).
The selection of the least number of features without causing an effect on the result accuracy in FSS is a multiobjective optimisation problem.
This work proposes ITLBO as an FSS mechanism, and its algorithm-specific, parameterless concept (no parameter tuning is required during optimisation) was explored.
IPJAYA in this study was used to update the C and gamma parameters of the support vector machine (SVM).
Several experiments were performed on the prominent intrusion ML dataset, where significant enhancements were observed with the suggested ITLBO-IPJAYA-SVM algorithm compared with the classical TLBO and JAYA algorithms.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Aljanabi, Mohammad& Ismail, Mohd Arfian& Mezhuyev, Vitaly. 2020. Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System. Complexity،Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1142367
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Aljanabi, Mohammad…[et al.]. Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System. Complexity No. 2020 (2020), pp.1-18.
https://search.emarefa.net/detail/BIM-1142367
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Aljanabi, Mohammad& Ismail, Mohd Arfian& Mezhuyev, Vitaly. Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System. Complexity. 2020. Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1142367
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1142367
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر