TY - GEN
T1 - Classification of Cybercrime Indicators in Open Social Data
AU - Ullah, Ihsan
AU - Lane, Caoilfhionn
AU - Buda, Teodora Sandra
AU - Drury, Brett
AU - Mellotte, Marc
AU - Assem, Haytham
AU - Madden, Michael G.
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Posting information on social media platforms is a popular activity through which personal and confidential information can leak into the public domain. Consequently, social media can contain information that provides an indication that an organization has been compromised or suffered a data breach. This paper describes a technique for inferring if an organization has been compromised from information posted on social media. The proposed strategy forms the basis of an alarm system which generates an alert for possible unreported cybercrime incidents. The proposed strategy used two social media cybercrime related datasets that were collected from the Irish and New York regions from financial organizations’ Twitter accounts. The Tweets are labelled as either containing cybercrime indicators or not, and then the cybercrime Tweets were labelled further into crime categories. A deep dense pyramidal Neural Network model is used to classify the Tweets. This approach achieves an AUC of 0.85±0.03 which outperforms the baseline of deep convolutional neural networks.
AB - Posting information on social media platforms is a popular activity through which personal and confidential information can leak into the public domain. Consequently, social media can contain information that provides an indication that an organization has been compromised or suffered a data breach. This paper describes a technique for inferring if an organization has been compromised from information posted on social media. The proposed strategy forms the basis of an alarm system which generates an alert for possible unreported cybercrime incidents. The proposed strategy used two social media cybercrime related datasets that were collected from the Irish and New York regions from financial organizations’ Twitter accounts. The Tweets are labelled as either containing cybercrime indicators or not, and then the cybercrime Tweets were labelled further into crime categories. A deep dense pyramidal Neural Network model is used to classify the Tweets. This approach achieves an AUC of 0.85±0.03 which outperforms the baseline of deep convolutional neural networks.
KW - Cybercrime classification
KW - Data breaches
KW - Open social data
KW - Pyramidal deep learning model
UR - https://www.scopus.com/pages/publications/85111103657
U2 - 10.1007/978-3-030-76228-5_23
DO - 10.1007/978-3-030-76228-5_23
M3 - Conference Publication
AN - SCOPUS:85111103657
SN - 9783030762278
T3 - Communications in Computer and Information Science
SP - 317
EP - 332
BT - Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings
A2 - Lossio-Ventura, Juan Antonio
A2 - Valverde-Rebaza, Jorge Carlos
A2 - Díaz, Eduardo
A2 - Alatrista-Salas, Hugo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th Annual International Conference on Information Management and Big Data, SIMBig 2020
Y2 - 1 October 2020 through 3 October 2020
ER -