An efficient intrusion detection system for identification from suspicious URLs using data mining algorithms Online publication date: Mon, 15-May-2017
by Kotoju Rajitha; Doddapaneni VijayaLakshmi
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 12, No. 2, 2017
Abstract: The main objective of this paper is to design intrusion detection from suspicious URLs using optimal fuzzy logic system. Basically, the system consists of three modules such as: 1) feature extraction; 2) feature selection; 3) classification. At first, we extract the four kinds of feature from the dataset which have a total of 30 features. Among that, we select the important features using hybridisation of firefly and cuckoo search algorithm (HFFCS). Then, we train the selected features using fuzzy logic classifier and then we calculate the fuzzy logic score. Finally in testing, the fuzzy logic classifier detected the malicious URL based on the fuzzy score. In this work, we use two types of database such as URL reputation dataset and phishing websites dataset. The experimental results demonstrate that the proposed malicious URL detection method outperforms other existing methods.
Online publication date: Mon, 15-May-2017
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Intelligence and Data Mining (IJBIDM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org