Notice Board :

Call for Paper
Vol. 5 Issue 2

Submission Start Date:
February 01, 2019

Acceptence Notification Start:
February 10, 2019

Submission End:
February 15, 2019

Final MenuScript Due:
February 25, 2019

Publication Date:
February 28, 2019


                         Notice Board: Call for PaperVol. 5 Issue 2      Submission Start Date: February 01, 2019      Acceptence Notification Start: February 10, 2019      Submission End: February 15, 2019      Final MenuScript Due: February 25, 2019      Publication Date: February 28, 2019




Volume III Issue IX

Author Name
Nitika Kadam
Year Of Publication
2017
Volume and Issue
Volume 3 Issue 9
Abstract
Web robots are software programs which automatically traverse through hyperlink structure of Web to retrieve Web resources. Robots can be used for variety of tasks such as crawling and indexing information for search engines, offline browsing, shopping comparison and email collectors. Apart from that robots can also be used for some malicious purposes like sending spam mails, stealing business intelligence etc. It is necessary to detect robots due to privacy, security and performance of server related issues. Several well-known techniques to detect robots are : robots.txt check, known robot’s IP address, User agent mapping, keywords matching in User agent field, browsing speed, unassigned referrer etc. In this paper we have discussed as well as implemented various robot identification techniques on real server log data and compared their performance for a given dataset.
PaperID
2017/IJRRETAS/10/2017/31610

Author Name
Deepika Khatwa , Mr. Rahul Kaul
Year Of Publication
2017
Volume and Issue
Volume 3 Issue 9
Abstract
We present a novel approach for detecting fraudulent behaviors from automated teller machine (ATM) usage data by analyzing geo-behavioral habits of the customers describe the use of a fuzzy rule-based system capable of classifying suspicious and non-suspicious transactions. We first compute the geographic entropies of ATM cardholders to form customer classes based on these entropies. ATM transactions are spatiotemporal by inclusion of location information. The transition data can be generated by using transaction data from the current location to the next one. Once, the transition data are generated, statistical outlier detection techniques can be utilized. On top of classical methods, we can easily use crisp unsupervised methods to detect outliers in the transition data. We analyze ATM usage dataset which contains around two years’ worth of data, provided by a mid-size Turkish bank. We have shown that a significant bulk of ATM users does not leave the vicinity of their living plac
PaperID
2017/IJRRETAS/10/2017/32616