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Article
Packet Identification By Using Data Mining Techniques

Authors: Safaa O. Al-Mamory --- Ali Hussein Ali
Journal: Journal of University of Babylon مجلة جامعة بابل ISSN: 19920652 23128135 Year: 2016 Volume: 24 Issue: 3 Pages: 565-579
Publisher: Babylon University جامعة بابل

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Abstract

Accurate internet traffic identification and classification are fundamental to numerous network activities, including network management and security monitoring, traffic modeling and network planning, accounting and Quality of Service provision. With the development of network, P2P as new generation of network technology is widely used. Starting from the first popular one (Napster), a number of new P2P based multimedia file sharing systems have been developed (FastTrack, eDonkey, Gnutella, Direct Connect, etc.). A fundamental types of networks architectures in today's world are Client/ server and Peer to Peer. A promising approach that has recently received some attention is traffic classification using machine learning techniques. The term data mining is used for methods and algorithms that allow analyzing data in order to find rules and patterns describing the characteristic properties of the data. The aim of this research is to classify traffic accuracy which can be achieved by using machine learning techniques such as K-Means and Birch algorithms. This system depends on the extracted attributes and then use it in the proposed system to distinguish all types of packets. The goal of system of packet identification is to detect the types of packets and identification of application usage and trends , also identification of emerging applications diagnosing anomalies is critical for both network operators and end user in term of data security and service availability.

التصنيف والتعريف الدقيق لحركة المرور على الانترنيت هو أمر أساسي للعديد من أنشطة الشبكة التي تتضمن إدارة الشبكات ,تخطيط الشبكات, المراقبة الأمنية، ونوعية الخدمة المقدمة . مع تطور الشبكات نشأ لدينا جيل جديد وهو تقنية نظير إلى نظير واستخدمت على نطاق واسع . بدأ من خلال تطوير (Napster)والذي يعتبر الأكثر شيوعاً من بين الأنظمة التي تستخدم أنظمة مشاركة الملفات مثل(. FastTrack, eDonkey, Gnutella, Direct Connect, etc)الأنواع الأساسية لمعماريات الشبكات في الوقت الحاضر هي العميل / الخادم و النظير لنظير . الطريقة الواعدة التي نالت بعض الاهتمام هي تصنيف الحزم باستخدام تقنيات التعلم الآلي . أن مفهوم تنقيب البيانات يعبرعن الطرق والخوارزميات التي تسمح بتحليل البيانات لغرض إيجاد القواعد والأنماط لوصف الخصائص المميزة للبيانات . الهدف من نظام تعريف الحزم هو لتحديد أنواع الحزم ,تحديد استعمالها واتجاهات التطبيق . كما يمكن التعرف على التطبيقات الناشئة لان تشخيص التشوهات أمر بالغ الأهمية لكل من المشغل والمستخدم للشبكة من ناحية أمن البيانات وتوفر الخدمة .


Article
Enhancing of DBSCAN based on Sampling and Density-based Separation

Authors: Safaa O. Al-mamory --- iqIsraa Saleh Kamil
Journal: Iraqi Journal for Computers and Informatics ijci المجلة العراقية للحاسبات والمعلوماتية ISSN: 2313190X 25204912 Year: 2016 Volume: 42 Issue: 1 Pages: 38-47
Publisher: University Of Informatics Technology And Communications جامعة تكنولوجيا المعلومات و الاتصالات

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Abstract

DBSCAN (Density-Based Clustering of Applications with Noise )is one of the attractive algorithms among density-based clustering algorithms. It characterized by its ability to detect clusters of various sizes and shapes with the presence of noise, but its performance degrades when data have different densities .In this paper, we proposed a new technique to separate data based on its density with a new sampling technique , the purpose of these new techniques is for getting data with homogenous density .The experimental results on synthetic data and real world data show that the new technique enhanced the clustering of DBSCAN to large extent.

Keywords

DBSCAN --- Sampling --- Density-based --- Separation


Article
Combining the Attribute Oriented Induction and Graph Visualization to Enhancement Association Rules Interpretation

Authors: Safaa O. Al-Mamory د. صفاء عبيس المعموري --- Zahraa Najim Abdullah زهراء نجم عبدالله
Journal: Iraqi Journal for Computers and Informatics ijci المجلة العراقية للحاسبات والمعلوماتية ISSN: 2313190X 25204912 Year: 2016 Volume: 42 Issue: 1 Pages: 10-22
Publisher: University Of Informatics Technology And Communications جامعة تكنولوجيا المعلومات و الاتصالات

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Abstract

The important methods of data mining is large andfrom these methods is mining of association rule. The miningof association rule gives huge number of the rules. These hugerules make analyst consuming more time when searchingthrough the large rules for finding the interesting rules. One ofthe solutions for this problem is combing between one of theAssociation rules visualization method and generalizationmethod. Association rules visualization method is graph-basedmethod. Generalization method is Attribute OrientedInduction algorithm (AOI). AOI after combing calls ModifiedAOI because it removes and changes in the steps of thetraditional AOI. The graph technique after combing also callsgrouped graph method because it displays the aggregated thatresults rules from AOI. The results of this paper are ratio ofcompression that gives clarity of visualization. These resultsprovide the ability for test and drill down in the rules orunderstand and roll up.

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