Abstract
Online social networks (OSNs) are playing a crucial role in daily life, cyber security guys such as hackers, cyber criminals, and researchers also like to communication and publish opinions. Their discussions and relations can provide unprecedented opportunities for researcher to develop better insights about those accounts’ activities in communities, which could be helpful for different purposes like cyber threat intelligent hunting and attack attribution. In this paper, we propose a scheme for cyber security community detection named CSCD on OSNs. We present a social relevance analysis method by building an ego network from one seed account. Through multidimensional analysis, features organized into four categories are taken into consideration and a recognition model is used to detect security-related accounts. Then we construct the social network, consisting of detected accounts, and propound a pruning strategy to remove weak relationships between accounts on the basis of edge features. An unsupervised overlapping community detection model is applied to unearthing potential communities. To evaluate our proposed scheme, we utilize Twitter as the platform to construct datasets. The recognition model achieves an accuracy up to 95.1%, and the community detection model obtains the best performance comparing to other former algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ab Razak, M.F., Anuar, N.B., Salleh, R., Firdaus, A.: The rise of “malware’’: bibliometric analysis of malware study. J. Netw. Comput. Appl. 75, 58–76 (2016)
Alves, F., Bettini, A., Ferreira, P.M., Bessani, A.: Processing tweets for cybersecurity threat awareness. Inf. Syst. 95, 101586 (2021)
Aslan, Ç.B., Sağlam, R.B., Li, S.: Automatic detection of cyber security related accounts on online social networks: Twitter as an example. In: Proceedings of the 9th International Conference on Social Media and Society, pp. 236–240 (2018)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Chakraborty, T., Dalmia, A., Mukherjee, A., Ganguly, N.: Metrics for community analysis: a survey. ACM Comput. Surv. (CSUR) 50(4), 1–37 (2017)
Djaballah, K.A., Boukhalfa, K., Ghalem, Z., Boukerma, O.: A new approach for the detection and analysis of phishing in social networks: the case of Twitter. In: 2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 1–8. IEEE (2020)
Egele, M., Stringhini, G., Kruegel, C., Vigna, G.: Towards detecting compromised accounts on social networks. IEEE Trans. Dependable Secure Comput. 14(4), 447–460 (2015)
Ellaky, Z., Benabbou, F., Ouahabi, S., Sael, N.: A survey of spam bots detection in online social networks. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 58–65. IEEE (2021)
Huang, S.Y., Ban, T.: Monitoring social media for vulnerability-threat prediction and topic analysis. In: 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp. 1771–1776 (2020)
Lau, R.Y., Xia, Y., Ye, Y.: A probabilistic generative model for mining cybercriminal networks from online social media. IEEE Comput. Intell. Mag. 9(1), 31–43 (2014)
Lingam, G., Rout, R.R., Somayajulu, D.V., Das, S.K.: Social botnet community detection: a novel approach based on behavioral similarity in Twitter network using deep learning. In: Proceedings of the 15th ACM Asia Conference on Computer and Communications Security, pp. 708–718 (2020)
Mahaini, M.I., Li, S.: Detecting cyber security related twitter accounts and different sub-groups: a multi-classifier approach. In: Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 599–606 (2021)
Mcauley, J., Leskovec, J.: Discovering social circles in ego networks. ACM Trans. Knowl. Discov. Data (TKDD) 8(1), 1–28 (2014)
McDaid, A.F., Greene, D., Hurley, N.: Normalized mutual information to evaluate overlapping community finding algorithms. arXiv preprint arXiv:1110.2515 (2011)
Pennacchiotti, M., Popescu, A.M.: Democrats, republicans and starbucks afficionados: user classification in Twitter. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 430–438 (2011)
Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying communities in networks. Proc. Natl. Acad. Sci. 101(9), 2658–2663 (2004)
Shchur, O., Günnemann, S.: Overlapping community detection with graph neural networks. In: Deep Learning on Graphs Workshop, KDD (2019)
Shen, H., Cheng, X., Cai, K., Hu, M.B.: Detect overlapping and hierarchical community structure in networks. Physica A 388(8), 1706–1712 (2009)
Wang, B., Jia, J., Zhang, L., Gong, N.Z.: Structure-based sybil detection in social networks via local rule-based propagation. IEEE Trans. Netw. Sci. Eng. 6(3), 523–537 (2018)
Wu, Y., Lian, D., Xu, Y., Wu, L., Chen, E.: Graph convolutional networks with Markov random field reasoning for social spammer detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 1054–1061 (2020)
Yamak, Z., Saunier, J., Vercouter, L.: Sockscatch: automatic detection and grouping of sockpuppets in social media. Knowl.-Based Syst. 149, 124–142 (2018)
Zhang, Y., Zhang, H., Yuan, X., Tzeng, N.F.: TweetScore: scoring tweets via social attribute relationships for twitter spammer detection. In: Proceedings of the 2019 ACM Asia Conference on Computer and Communications Security, pp. 379–390 (2019)
Acknowledgment
This research is funded by the National Key Research and Development Program of China (No. 2021YFB3100500), CCF-NSFOCUS KunPeng Research Fund (No. 202105).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Zeng, Y., Yu, H., Wu, T., Chen, Y., Lan, X., Huang, C. (2023). CSCD: A Cyber Security Community Detection Scheme on Online Social Networks. In: Goel, S., Gladyshev, P., Nikolay, A., Markowsky, G., Johnson, D. (eds) Digital Forensics and Cyber Crime. ICDF2C 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 508. Springer, Cham. https://doi.org/10.1007/978-3-031-36574-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-36574-4_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-36573-7
Online ISBN: 978-3-031-36574-4
eBook Packages: Computer ScienceComputer Science (R0)