Detecting Malicious Accounts in Online Developer Communities Using Deep Learning
Loading...
Access rights
openAccess
acceptedVersion
URL
Journal Title
Journal ISSN
Volume Title
A4 Artikkeli konferenssijulkaisussa
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Date
Major/Subject
Mcode
Degree programme
Language
en
Pages
Series
CIKM '19:Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1251-1260, ACM International Conference on Information & Knowledge Management
Abstract
Online developer communities like GitHub provide services such as distributed version control and task management, which allow a massive number of developers to collaborate online. However, the openness of the communities makes themselves vulnerable to different types of malicious attacks, since the attackers can easily join and interact with legitimate users. In this work, we formulate the malicious account detection problem in online developer communities, and propose GitSec, a deep learning-based solution to detect malicious accounts. GitSec distinguishes malicious accounts from legitimate ones based on the account profiles as well as dynamic activity characteristics. On one hand, GitSec makes use of users' descriptive features from the profiles. On the other hand, GitSec processes users' dynamic behavioral data by constructing two user activity sequences and applying a parallel neural network design to deal with each of them, respectively. An attention mechanism is used to integrate the information generated by the parallel neural networks. The final judgement is made by a decision maker implemented by a supervised machine learning-based classifier. Based on the real-world data of GitHub users, our extensive evaluations show that GitSec is an accurate detection system, with an F1-score of 0.922 and an AUC value of 0.940.Description
Other note
Citation
Gong, Q, Zhang, J, Chen, Y, Li, Q, Xiao, Y, Wang, X & Hui, P 2019, Detecting Malicious Accounts in Online Developer Communities Using Deep Learning. in CIKM '19:Proceedings of the 28th ACM International Conference on Information and Knowledge Management. ACM International Conference on Information & Knowledge Management, ACM, pp. 1251-1260, ACM International Conference on Information and Knowledge Management, Beijing, China, 03/11/2019. https://doi.org/10.1145/3357384.3357971