Efficiently Detecting Web Spambots in a Temporally Annotated Sequence

TY - CHAP

T1 - Efficiently Detecting Web Spambots in a Temporally Annotated Sequence

AU - Alamro, Hayam

AU - Iliopoulos, Costas S.

AU - Loukides, Grigorios

PY - 2020/9/5

Y1 - 2020/9/5

N2 - 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.

AB - 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.

KW - Action logs

KW - Temporally annotated sequence

KW - Web spambot

UR - http://www.scopus.com/inward/record.url?scp=85083737105&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-44041-1\_87

DO - 10.1007/978-3-030-44041-1\_87

M3 - Conference paper

AN - SCOPUS:85083737105

SN - 9783030440404

VL - 1151

T3 - Advances in Intelligent Systems and Computing

SP - 1007

EP - 1019

BT - Advanced Information Networking and Applications - Proceedings of the 34th International Conference on Advanced Information Networking and Applications, AINA-2020, Caserta, Italy, 15-17 April

A2 - Barolli, Leonard

A2 - Amato, Flora

A2 - Moscato, Francesco

A2 - Enokido, Tomoya

A2 - Takizawa, Makoto

PB - Springer

T2 - 34th International Conference on Advanced Information Networking and Applications, AINA 2020

Y2 - 15 April 2020 through 17 April 2020

ER -