TITLE:
An Effective Network Traffic Data Control Using Improved Apriori Rule Mining
AUTHORS:
Subbiyan Prakash, Murugasamy Vijayakumar
KEYWORDS:
Network Traffic, Internet, Traffic Condition, Rule Mining, Decision Rule Framework, Interestingness, Traffic Data, Web Log
JOURNAL NAME:
Circuits and Systems,
Vol.7 No.10,
August
23,
2016
ABSTRACT: The increasing usage of
internet requires a significant system for effective communication. Topro- vide an effective communication
for the internet users, based on nature of their queries,shortest routing path is usually
preferred for data forwarding. But when more number of data chooses the same
path, in that case, bottleneck occurs in the traffic this leads to data loss or
provides irrelevant data to the users. In this paper, a Rule Based System using
Improved Apriori (RBS-IA) rule mining framework is proposed for effective
monitoring of traffic occurrence over the network and control the network
traffic. RBS-IA framework integrates both the traffic control and decision
making system to enhance the usage of internet trendier. At first, the network
traffic data are ana- lyzed and the incoming and outgoing data information is
processed using apriori rule mining algorithm. After generating the set of
rules, the network traffic condition is analyzed. Based on the traffic
conditions, the decision rule framework is introduced which derives and assigns
the set of suitable rules to the appropriate states of the network. The
decision rule framework improves the effectiveness of network traffic control
by updating the traffic condition states for identifying the relevant route
path for packet data transmission. Experimental evaluation is conducted by
extrac- ting the Dodgers loop sensor data set from UCI repository to detect the
effectiveness of theproposed Rule Based System using Improved Apriori (RBS-IA)
rule mining framework. Performance evaluation shows that the proposed RBS-IA
rule mining framework provides significant improvement in managing the network
traffic control scheme. RBS-IA rule mining framework is evaluated over the
factors such as accuracy of the decision being obtained, interestingness
measure and execution time.