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Detecting Malicious Accounts in Online Developer Communities Using Deep Learning
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AI
DOI：10.1109/TKDE.2023.3237838.png)
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摘要
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Online developer communities like GitHub allow a massive number of developers to collaborate. However, the openness of the communities makes them vulnerable to different types of malicious attacks, since attackers can easily join these communities and interact with legitimate users. In this work, we propose GitSec, a deep learning-based solution for detecting malicious accounts in online developer communities. GitSec distinguishes malicious accounts from legitimate ones based on the account profiles, dynamic activity characteristics, as well as social interactions. First, GitSec introduces two user activity sequences and applies a parallel neural network design with an attention mechanism to process the sequences. Second, GitSec constructs two graphs to represent the interactions between users according to their repository operations. Especially, graph neural networks and structural hole theory are employed to deal with the two constructed graphs. Third, GitSec makes use of the descriptive features to enhance the detection performance. 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 comprehensive evaluations show that GitSec achieves a better performance than state-of-the-art solutions, with an AUC value of 0.916.
Keyword:
Software development management
Codes
Data collection
Stars
Companies
C plus plus languages
Blogs
Deep learning
graph neural networks
malicious account detection
online developer communities
social networks
structural hole theory
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
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