Automatic firewall rules generator for anomaly detection systems with Apriori algorithm

Network intrusion detection systems have become a crucial issue for computer systems security infrastructures. Different methods and algorithms are developed and proposed in recent years to improve intrusion detection systems. The most important issue in current systems is that they are poor at dete...

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Bibliographic Details
Published in:2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE) Vol. 6; pp. V6-57 - V6-60
Main Authors: Saboori, Ehsan, Parsazad, Shafigh, Sanatkhani, Yasaman
Format: Conference Proceeding
Language:English
Published: IEEE 01.08.2010
Subjects:
Anomaly detection
Apriori algorithm
Association rule learning
Association rules
Classification algorithms
Data mining
Firewalls (computing)
Intrusion
Intrusion detection
Intrusion detection systems
Itemsets
Real-time systems
Software
Training
ISBN:1424465397, 9781424465392
ISSN:2154-7491
Online Access:Get full text
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Summary:Network intrusion detection systems have become a crucial issue for computer systems security infrastructures. Different methods and algorithms are developed and proposed in recent years to improve intrusion detection systems. The most important issue in current systems is that they are poor at detecting novel anomaly attacks. These kinds of attacks refer to any action that significantly deviates from the normal behaviour which is considered intrusion. This paper proposed a model to improve this problem based on data mining techniques. Apriori algorithm is used to predict novel attacks and generate real-time rules for firewall. Apriori algorithm extracts interesting correlation relationships among large set of data items. This paper illustrates how to use Apriori algorithm in intrusion detection systems to cerate a automatic firewall rules generator to detect novel anomaly attack. Apriori is the best-known algorithm to mine association rules. This is an innovative way to find association rules on large scale.
ISBN:1424465397
9781424465392
ISSN:2154-7491
DOI:10.1109/ICACTE.2010.5579365