An enhanced steady state genetic algorithm model for misuse network intrusion detection system

العناوين الأخرى

نموذج محسن للخوارزمية الجينية المستقرة لاكتشاف التطفل في الشبكات الحاسوبية

مقدم أطروحة جامعية

al-Absi, Firas Muhammad Ahmad

مشرف أطروحة جامعية

Naum, Riyad Shakir

أعضاء اللجنة

Salit, Azzam
al-Huraybat, Muhammad

الجامعة

جامعة الشرق الأوسط

الكلية

كلية تكنولوجيا المعلومات

القسم الأكاديمي

قسم علم الحاسوب

دولة الجامعة

الأردن

الدرجة العلمية

ماجستير

تاريخ الدرجة العلمية

2012

الملخص الإنجليزي

The networks usage has been increased in the last decades.

The intruders began to do violations and abuses over the networks.

This had led the researchers to do additional researches to support Intrusion Detection Systems.

The main aim of this thesis is to build Intrusion Detection System supported by enhanced Steady State Genetic Algorithm in order to increase Detection Rate and to decrease False Positive Rate.

This proposed research proved Reward Penality based Fitness Function to be used in the evaluation process.

It also compared selection and crossover to choose the best choice to implement it in a system; it was found that Stochastic Universal Sampling Selection can be used with Uniform Crossover to produce the best results.

This research was applied Stochastic Universal Sampling Selection and Uniform Crossover as parameters in Steady State Genetic Algorithm to be used in Network Intrusion Detection System.

In this thesis an enhancement has been done to the algorithm by using Reward- Penality based Fitness Function and choosing the best choice for selection and crossover; this has affected the Misuse based Network Intrusion Detection System by increase DR to be equal 95% and decrease FPR to be equal 0.297%.

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

عدد الصفحات

102

قائمة المحتويات

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-694120