Using Data Mining Techniques in Building Dataset for Network Intrusion Detection

2015 ◽  
Vol 10 (7) ◽  
pp. 652
Author(s):  
Manar Jacob Aljabr

Network Intrusion Detection is a significant apparatus to distinguish and examine security dangers to a correspondence arrange. It supplements other system security procedures, for example, firewalls, by giving data about the recurrence and nature of assaults. A system interruption discovery framework (NIDS) frequently comprises of a sensor that examines each bundle on the system under perception, and advances the parcels which are considered fascinating, together with an alarm message to a backend framework, that stores them for further examination and relationship with different occasions. The assessment procedure of the MAC address contrasted with the CADL is improved and streamlined with the help of the J48 choice tree calculation. The pursuit procedure is completed in the created arrangement esteem through tree based characterization.


2015 ◽  
Vol 76 (12) ◽  
Author(s):  
Mohd Afizi Mohd Shukran ◽  
Kamaruzaman Maskat

Network Intrusion Detection is to detect malicious attacks to the networks for different uses from military to enterprise. Currently available approaches either rely on the known network attacks or have high proportion of normal network traffics that were erroneously reported as anomalous traffics. The aim of this paper is to develop an efficient algorithm for intrusion detection without prior knowledge of network attacks. Uniquely, our approach will integrate a newly developed data mining technique for data feature classification with techniques commonly used for human detection. The key idea is to achieve on-line and automated learning of new attacks for precise and real-time intrusion detection.


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