City Research Online

Using Diverse Detectors for Detecting Malicious Web Scraping Activity

Marques, P., Dabbabi, Z., Mironesc, M-M, Thonnard, O., Bessan, A., Buontempo, F. and Gashi, I. ORCID: 0000-0002-8017-3184 (2018). Using Diverse Detectors for Detecting Malicious Web Scraping Activity. Paper presented at the IEEE/IFIP International Conference on Dependable Systems and Networks, 25-28 Jun 2018, Luxembourg.

Abstract

We present ongoing work about how the use of diverse tools may help with detecting malicious web scraping behavior. We use a real dataset of Apache HTTP Access logs for an e-commerce application provided by Amadeus, a large multinational IT provider for the global travel and tourism industry. Two tools have been used to detect scraping activities based on the HTTP requests: a commercial tool, and an in-house tool called Arcane. Preliminary results suggest there is considerable diversity in alerting behavior of these tools.

Publication Type: Conference or Workshop Item (Paper)
Additional Information: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: security assessment; software diversity; security tools; botnet detection
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
URI: http://openaccess.city.ac.uk/id/eprint/19790
[img]
Preview
Text - Accepted Version
Download (131kB) | Preview
Official URL: https://dsn2018.uni.lu/

Export

Downloads

Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login