Hacker community detection in online social network: a case study

Xiangjun Ma; Yuzi Yi; Yehong Luo; Ahsan Wajahat; Ahsan Nazir

doi:10.1117/12.3010341

20 October 2023 Hacker community detection in online social network: a case study

Xiangjun Ma, Yuzi Yi, Yehong Luo, Ahsan Wajahat, Ahsan Nazir

Proceedings Volume 12814, Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023); 1281414 (2023) https://doi.org/10.1117/12.3010341
Event: Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023), 2023, Chongqing, China

Abstract

Online social networks serve as platforms facilitating communication, collaboration, and information exchange among hackers. Consequently, the detection and monitoring of hacker communities within these networks have emerged as significant research areas in the field of cybersecurity. Numerous methods have been employed to detect these communities, with some primarily utilizing network structural information and others incorporating user-generated content topics. However, most existing methods rely on probabilistic models such as TF-IDF or LDA to analyze textual similarity, resulting in limited accuracy. In this paper, we introduce a Siamese Attention-augmented Recurrent Convolutional Neural Network aimed at analyzing text similarity among users and evaluating the significance of different users by comparing their text similarity with seed users. Subsequently, a weighted network is constructed, and community detection is performed using a modularity maximization algorithm. To validate the proposed framework, we collected Twitter friendship data and user-generated content from a pre-existing regional hacker group. A comprehensive series of experiments were conducted on this dataset. The experimental results provide strong evidence of the effectiveness of our proposed framework, showcasing its remarkable performance across various indicators such as community quality and topic relevance. Consequently, our framework exhibits promising prospects for practical applications in the field of hacker community detection.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiangjun Ma, Yuzi Yi, Yehong Luo, Ahsan Wajahat, and Ahsan Nazir "Hacker community detection in online social network: a case study", Proc. SPIE 12814, Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023), 1281414 (20 October 2023); https://doi.org/10.1117/12.3010341

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KEYWORDS

Social networks

Analytical research

Network security

Computer security

Convolutional neural networks

Computer networks

Semantics

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