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During the last decade, various machine learning and data mining techniques have been applied to Intrusion Detection Systems (IDSs) which have played an important role in defending critical computer systems and networks from cyber attacks. Unsupervised anomaly detection techniques have received a particularly great amount of attention because they enable construction of intrusion detection models...
Intrusion Detection Systems (IDSs) play an important role detecting various kinds of attacks and defend our computer systems from them. There are basically two main types of detection techniques: signature-based and anomaly-based. A signature-based IDS cannot detect unknown attacks because a signature has not been written. To overcome this shortcoming, many researchers have been developing anomaly-based...
Intrusion detection systems (IDSs) play an important role to defend networks from cyber attacks. Among them, anomaly-based IDSs can detect unknown attacks like 0-day attacks that are hard to detect by using signature-based system. However, they have problems that their performance depends on a learning dataset. It is very hard to prepare an appropriate learning dataset in a static fashion, because...
This research aims to construct a high-performance anomaly based intrusion detection system. Most of past studies of anomaly based IDS adopt k-means based clustering, this paper points out that the following reasons cause performance degradation of k-means based clustering when it is deployed in real traffic environment. First, k-means based algorithms have weakness for high dimensional data. Second,...
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