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Efficiently Detecting Web Spambots in a Temporally Annotated Sequence

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Original languageEnglish
Title of host publicationAdvanced Information Networking and Applications - Proceedings of the 34th International Conference on Advanced Information Networking and Applications, AINA-2020, Caserta, Italy, 15-17 April
EditorsLeonard Barolli, Flora Amato, Francesco Moscato, Tomoya Enokido, Makoto Takizawa
PublisherSpringer
Pages1007-1019
Number of pages13
Volume1151
ISBN (Print)9783030440404
DOIs
Published5 Sep 2020
Event34th International Conference on Advanced Information Networking and Applications, AINA 2020 - Caserta, Italy
Duration: 15 Apr 202017 Apr 2020

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1151 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference34th International Conference on Advanced Information Networking and Applications, AINA 2020
CountryItaly
CityCaserta
Period15/04/202017/04/2020

King's Authors

Abstract

Web spambots are becoming more advanced, utilizing techniques that can defeat existing spam detection algorithms. These techniques include performing a series of malicious actions with variable time delays, repeating the same series of malicious actions multiple times, and interleaving legitimate (decoy) and malicious actions. Existing methods that are based on string pattern matching are not able to detect spambots that use these techniques. In response, we define a new problem to detect spambots utilizing the aforementioned techniques and propose an efficient algorithm to solve it. Given a dictionary of temporally annotated sequences hat modeling spambot actions, each associated with a time window, a long, temporally annotated sequence T modeling a user action log, and parameters f and k, our problem seeks to detect each sequence in hat that occurs in T at least f times within its associated time window, and with at most k mismatches. Our algorithm solves the problem exactly, it requires linear time and space, and it employs advanced data structures and the Kangaroo method, to deal with the problem efficiently.

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