Abstract
Intrusion detection is becoming a hot topic of research for the information security people. There are mainly two classes of intrusion detection techniques available till today namely anomaly detection techniques and signature recognition techniques. Anomaly detection techniques are becoming area of interest for the researchers and new techniques are developing every day. However, no techniques have been found to be absolutely perfect. Clustering is an important data mining techniques used to find patterns and data distribution in the datasets. It is mainly used to identify the dense regions and sparse regions in the datasets. The sparse regions were often considered as outliers. There are several clustering algorithms developed till today for the discovery outliers in the datasets. K-means algorithm. K-medoids algorithm, CLARA, CLARANS, DBSCAN, ROCK, BIRCH, CACTUS etc. are some of the popular algorithms dealing with numeric datasets, categorical datasets, spatial datasets or hybrid datasets. Clustering techniques have been successfully used in detection anomaly in dataset. The techniques were found to be useful in the design of a couple of anomaly based Intrusion Detection Systems (IDS). But most of clustering techniques used for these purpose have taken partitioning approach. In this article, we propose a different clustering algorithm for the anomaly detection on network datasets. Our algorithm is an agglomerative hierarchical clustering algorithm which tries to find clusters on the dataset consisting of both numeric and categorical datasets i.e. hybrid datasets. For this purpose, we define a suitable similarity measure on both numeric and categorical attributes available on any network datasets.
Original language | English |
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Title of host publication | Information Science and Applications 2018 |
Subtitle of host publication | ICISA 2018 |
Editors | Kuinam J. Kim, Nakhoon Baek |
Publisher | Springer |
Pages | 475-484 |
Number of pages | 10 |
ISBN (Electronic) | 9789811310560 |
ISBN (Print) | 9789811310553 |
DOIs | |
Publication status | Published - 2018 |
Event | 9th iCatse Conference on Information Science and Applications 2018 - Hong Kong, China Duration: 25 Jun 2018 → 27 Jun 2018 |
Publication series
Name | Lecture Notes in Electrical Engineering |
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Publisher | Springer |
Volume | 514 |
ISSN (Print) | 1876-1100 |
ISSN (Electronic) | 1876-1119 |
Conference
Conference | 9th iCatse Conference on Information Science and Applications 2018 |
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Country/Territory | China |
City | Hong Kong |
Period | 25/06/18 → 27/06/18 |
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Lilia Georgieva
- School of Mathematical & Computer Sciences - Assistant Professor
- School of Mathematical & Computer Sciences, Computer Science - Assistant Professor
Person: Academic (Research & Teaching)