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Intelligent hybrid approach for classification accuracy of intrusion detection system
مقدم أطروحة جامعية
مشرف أطروحة جامعية
الجامعة
جامعة الإسراء
الكلية
كلية تكنولوجيا المعلومات
القسم الأكاديمي
قسم هندسة البرمجيات
دولة الجامعة
الأردن
الدرجة العلمية
ماجستير
تاريخ الدرجة العلمية
2020
الملخص الإنجليزي
Intrusion detection system (I.D.S) is an essential component, which enhances the security of computer systems by actively detecting all forms of attack at the early stages.
The main use of IDS is the monitoring of the network traffics and analyzing the behavior of the users in searching for any abnormal activity or attack signature for real-time intrusion detection.
The main weakness in any IDS is their inability to offer adequate sensitivity and accuracy; coupled with their inability to process enormous data.
To address these issues (such as the increasing traffic, huge behavior profiles, large signature databases, and the inability of differentiating normal behaviors from the suspicious ones), several algorithms have been developed.
Hence, the main aim of this work is to choose the differentiating features for the development of an optimal machine learning algorithm which can offer high detection rates, fast training, and testing processes offline.
The proposed machine learning model contains a feature selection algorithm (wrapper type) which is based on the integration of the Binary Firefly algorithm enhanced for feature selection by crossover operator taking from the genetic algorithm, called (GA-FA) with the Naïve Bayesian Classifier (NBC).
The performance of the proposed model was tested on NSL_KDD data sets prepared by the MIT Lincoln Laboratory.
The model testing was based on several experiments and different scenarios (the effect of swarm size, number of iterations, and the ????).
For evaluating the ability to select the minimum number of features with the higher value of classification accuracy, the algorithm worked perfectly and selected a comparable number of features.
The model achieved the best average accuracy of 97.011%.
In conclusion, the proposed feature selection algorithm has the ability to select the most relevant features which enhance the classification accuracy of the network intrusion detection system.
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الموضوعات
عدد الصفحات
57
قائمة المحتويات
Table of contents.
Abstract.
Chapter One : Introduction.
Chapter Two : Background and literature review.
Chapter Three : Design and implementation.
Chapter Four : Results and discussion.
Chapter Five : Conclusion
References.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Abbas, Mustafa Nihad. (2020). Intelligent hybrid approach for classification accuracy of intrusion detection system. (Master's theses Theses and Dissertations Master). Isra University, Jordan
https://search.emarefa.net/detail/BIM-988718
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Abbas, Mustafa Nihad. Intelligent hybrid approach for classification accuracy of intrusion detection system. (Master's theses Theses and Dissertations Master). Isra University. (2020).
https://search.emarefa.net/detail/BIM-988718
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Abbas, Mustafa Nihad. (2020). Intelligent hybrid approach for classification accuracy of intrusion detection system. (Master's theses Theses and Dissertations Master). Isra University, Jordan
https://search.emarefa.net/detail/BIM-988718
لغة النص
الإنجليزية
نوع البيانات
رسائل جامعية
رقم السجل
BIM-988718
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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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
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