ABSTRACT-In today’s world the security of computer system is of great concern. Because the last few years have seen a dramatic increase in the number of attacks, intrusion detection has become the mainstream of information insurance. Firewalls provide some protection. They do not provide full protection and still need to be complimented by an intrusion detection system. Data mining techniques are a new approach for intrusion detection. Recent studies show that as compared to the single algorithm, cascading of multiple algorithm’s gives much better performance. False alarm rate was also high in such system. Therefore, combination of different algorithms is performed to solve this problem. In this paper, we use two hybrid algorithms for developing the intrusion detection system. C4.5 decision tree and Support Vector Machine are combined to achieve high accuracy and diminish the wrong alarm rate. Data mining, or knowledge discovery, is the computer-assisted process of digging through and analyzing enormous sets of data and then extracting the meaning of the data. Data mining tools predict behaviors and future trends, allowing businesses to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally were too time consuming to resolve. Data mining derives its name from the similarities between searching for valuable information in a large database and mining a mountain for a vein of valuable ore. Both processes require either sifting through an immense amount of material, or intelligently probing it to find where the value resides.
Amardeep Singh, Sharanjit Singh, Simmy
Data Mining, Support Vector Machine, VPN, SVC, SVR
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|Published in :
||Volume 2 | Issue 2 | March-April - 2016
|Date of Publication
Cite This Article
Amardeep Singh, Sharanjit Singh, Simmy, "Anomaly Detection in Network Using Data Mining Algorithms", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 2, pp.1325-1328, March-April-2016.
URL : http://ijsrset.com/IJSRSET1622445.php