TY  - JOUR
T1  - Implementing Secure Cluster using Hadoop and Snort for ID (Intrusion Detection)
AU - M. Almuttairi, Rafah AU - Kamal Al-Anni, Maad AU - A. Aljburi, Dalya 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 22
SP  - 9789
EP  - 9799
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.9789.9799
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.9789.9799
KW  - Web server
KW  -Apache Hadoop
KW  -DoS
KW  -MapReduce
KW  -snort
KW  -DDoS
KW  -big data
KW  -alerts
AB  - Among the best ways for someone or a company to get famous and well known is through certain
electronic media using web applications to make customers know what companies have through their websites,
as the formers shop and inquire through web services of the latters. Therefore, it is necessary to protect the
website against any attack by those who are not interested in the progress of high-level companies and the
spread of their fame. One of the attacks that the Web server is likely to experience is the Denial of Service
Distributed (DDoS), across the application layer. The increased volume of data resulting from the attack makes
the current detection systems inefficient to detect the hacker. In this research, a new methodology is proposed
to detect and prevent attacks through the use of Hadoob framework which will accelerate the analysis of data
to discover the attack and deliver it to the Snort to be blocked and stop harm. After the analysis of the data
we found that the proposed could provide a 99.01% reduction rate, 99.27, 99.72% for the original alerts 1668,
2182 and 2698, respectively, compared to the traditional model.
ER  -