Website Defacer Classification

A Finite Mixture Model Approach

Datos Bibliográficos

ID12170504
AutoresGeorge W Burru (0000-0002-1148-7446, University of South Florida, Tampa, FL, USA, autor de correspondencia), C Jordan Howell (0000-0003-4443-5068, The University of Texas at El Paso, TX, USA), David Maimon (0000-0003-1492-2762, Georgia State University, Atlanta, GA, USA), Fangzhou Wang (0000-0001-6212-3047, Georgia State University, Atlanta, GA, USA)
Año2021
Volumen40
Número3
Páginas775-787
Fecha de publicación2021-02-23
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaSocial Science Computer Review (JOURNAL)
Identificadores de la revistaISSN: 0894-4393 • E-ISSN: 1552-8286
EditorialSAGE Publishing (PUBLISHER • US)
DOI10.1177/0894439321994232
OpenAlexW3130186835
IdiomaEN
Citas recibidas4
Referencias citadas26

Hackers often engage in website defacement early in their criminal careers to establish a reputation. Some hackers become increasingly prolific and launch a large number of attacks against their targets, whereas others only launch a few attacks before eventually desisting from a life of crime. A better understanding of why some hackers launch a large number of attacks, while others do not, will assist in the implementation of targeted intervention strategies. Therefore, the current study, using a sample of 119 active hackers, seeks to answer two research questions: (1) Are there different groups of website defacers based on attack volume? (2) Which observed hacker-level characteristics can be used to predict latent class membership? We find that two unique groups of website defacers exist: low-volume defacers (69%) and high-volume defacers (31%). Social media presence, the content of the defacement, and the type of defacement are all predictive of latent class membership. Policy implications are discussed

Class (philosophy · Computer security · Hacker · Internet privacy · Reputation · Sample (material · Social science · Sociology · Volume (thermodynamics · Computer Science · Crime, Illicit Activities, and Governance · Cybercrime and Law Enforcement Studies · Spam and Phishing Detection · Artificial Intelligence