![](/images/graphics-bg.png)
Analyzing the behavior of multiple dimensionality reduction algorithms to obtain better accuracy using benchmark KDD CUP dataset
المؤلفون المشاركون
Jambunathan, Suriya Prakash
Ramadass, Suguna
kumaran, Palanivel Rajan Selva
المصدر
The International Arab Journal of Information Technology
العدد
المجلد 19، العدد 1 (31 يناير/كانون الثاني 2022)، ص ص. 121-131، 11ص.
الناشر
جامعة الزرقاء عمادة البحث العلمي
تاريخ النشر
2022-01-31
دولة النشر
الأردن
عدد الصفحات
11
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
In the ubiquitously connected world of IT infrastructure, Intrusion Detection System (IDS) plays vital role.
IDS is considered as a critical component of security infrastructure and is implemented either through hardware or software devices and can detect malicious activities in a networked environment.
To detect or prevent network attacks, Network Intrusion Detection (NID) system may be equipped with machine learning algorithms to achieve better accuracy and faster detection speed.
Analyzing different attacks effectively through Dimensionality Reduction Algorithms is an efficient mechanism.
The significance of these algorithms is they improvise feature selection from huge datasets.
Also through this the learning speed is enhanced.
Speed is a crucial parameter in the success of network intrusion detection systems for defending reactions.
In this paper open source datasets Knowledge Discovery in Databases (KDD CUP) dataset and 10% KDD CUP dataset are employed for experimentation.
These datasets are provided to Dimensionality Reduction Algorithms like Principal Component Analysis (PCA), Linear Discriminate Analysis (LDA) and Kernel PCA with different kernels and classified with Logistic Regression classification algorithm for procuring accurate results.
Further to boost up the accuracy achieved so far K-fold algorithm is utilized.
Finally a comparative study of different accuracy results is done by using K-fold algorithm and also without the usage of this algorithm.
The empirical study on KDD CUP data confirms the effectiveness of the proposed scheme.
In this paper we discovered the combination of multiple dimensionality reduction algorithm such as PCA , LDA and Kernel PCA with classification algorithm and this combination of algorithm gives best result.
Our study will help out the researchers to uncover critical area such as intrusion detection in network traffic environment.
The results what we identified will be very much helpful for researchers for their future research on KDD CUP dataset.
In this the new theory will be arrived by this research that the best accuracy achieved by PCA with 10% KDD CUP dataset experimental results without KFold attained 98% and with KFold attained 99%.
LDA with 10% KDD CUP Dataset experimental results without KFold attained 98% and with KFold attained 99%.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Jambunathan, Suriya Prakash& Ramadass, Suguna& kumaran, Palanivel Rajan Selva. 2022. Analyzing the behavior of multiple dimensionality reduction algorithms to obtain better accuracy using benchmark KDD CUP dataset. The International Arab Journal of Information Technology،Vol. 19, no. 1, pp.121-131.
https://search.emarefa.net/detail/BIM-1437424
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Jambunathan, Suriya Prakash…[et al.]. Analyzing the behavior of multiple dimensionality reduction algorithms to obtain better accuracy using benchmark KDD CUP dataset. The International Arab Journal of Information Technology Vol. 19, no. 1 (Jan. 2022), pp.121-131.
https://search.emarefa.net/detail/BIM-1437424
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Jambunathan, Suriya Prakash& Ramadass, Suguna& kumaran, Palanivel Rajan Selva. Analyzing the behavior of multiple dimensionality reduction algorithms to obtain better accuracy using benchmark KDD CUP dataset. The International Arab Journal of Information Technology. 2022. Vol. 19, no. 1, pp.121-131.
https://search.emarefa.net/detail/BIM-1437424
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references : p. 130-131
رقم السجل
BIM-1437424
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
![](/images/ebook-kashef.png)
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
![](/images/kashef-image.png)