Intrusion detection system based on carpenter grossberg artificial neural network

العناوين الأخرى

نظام كشف التطفل استنادا على شبكة كاربنتر كروسبيرك العصبونية الاصطناعية

مقدم أطروحة جامعية

al-Rabii, Ammar Muhanna Kazim

مشرف أطروحة جامعية

Naum, Riyad Shakir

أعضاء اللجنة

Kayid, Ahmad
Khalid, Mamun
Hirz Allah, Nail

الجامعة

جامعة الشرق الأوسط

الكلية

كلية تكنولوجيا المعلومات

القسم الأكاديمي

قسم علم الحاسوب

دولة الجامعة

الأردن

الدرجة العلمية

ماجستير

تاريخ الدرجة العلمية

2014

الملخص الإنجليزي

Over the last few decades, computer applications have evolved and became very important part of our life.

This led to widespread concerns of network service disruption due to large-scale malicious attacks on computer networks.

The development of a secure infrastructure to defend these applications from all challenges coming from intruders, hackers, and unauthorized access is a major challenge.

Intrusion detection system (IDS) is regarded as the second line of defense against network anomalies and threats.

IDSs play an important role in detecting malicious and suspicious activities, and providing warning for unauthorized access over the network.

This research simulates a model of intrusion detection system.

Artificial neural network (ANN) and machine learning (ML) combined with clustering algorithm as a pre-classifier are used to enhance the detection of network intrusion.

This IDS use both Adaptive Resonance Theory (ART1) and k-mean clustering algorithm, where ART1 is a version of Carpenter/Grossberg’s ANN and the key core in this system.

The simulation system includes three main phases: 1.

Preprocessing, in which converts the data and cluster the categories.

2.

Training phase, in which trains ART1 neural network.

3.

Testing phase, which tests ART1 network and check the performance and the stability of the IDS system.

At training phase, the sample space was randomly selected, where all known attack patterns are selected from KDD 99 dataset.

Furthermore, many parameters were adjusted such as; norm, vigilance test, and weight factors.

For testing purposes, the sample space is also randomly selected, that contains a number of duplicated patterns in order to test the stability.

The results of this research has a detection rate about 96.8% with an accuracy rate 96% , false positive rate 1.19% and False Negative Rate about 0.54% The results are compared with other previous studies.

The results from this research showed better performance than the compared approaches.

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

عدد الصفحات

109

قائمة المحتويات

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-699451