Abstract
This paper presents a multi-modal fusion machine learning algorithm to detect active malicious accounts in social networks. First, we use the XGBoost algorithm to rank features’ importance and reduce the impact of redundant features. Then, we use density detection algorithms to monitor malicious accounts according to the actual situation and the cooperative behavior of malicious accounts. Finally, we employ neural network algorithms to make secondary judgments on the results obtained in the previous step based on the periodic activity characteristics of active malicious accounts. We evaluate our approach on a real-world social network dataset. We have conducted experiments that demonstrate that the XGBoost algorithm aids in obtaining better results than other feature selection algorithms. Moreover, the comparison with other malicious account detection algorithms is also illustrated by extensive experiments. The result concludes that our proposed model is more efficient, more accurate, takes less time, and has a certain degree of scalability, thus performing well in practical applications.
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
Sheikhi, F.: An efficient method for detection of fake accounts on the Instagram platform. Rev. D Intell. Artif. 34(4), 429–436 (2020)
Dover, Y., Goldenberg, J., Shapira, D.: Uncovering Social Network Structures Through Penetration Data. Social Science Electronic Publishing, Rochester (2009)
Schuetz, S.W., Wei, J.: When your friends render you vulnerable: a social network analysis perspective on users’ vulnerability to socially engineered phishing attacks. In: ICIS 2019 (2019)
Stein, T., Chen, E., Mangla, K.: Facebook immune system. In: Proceedings of the 4th Workshop on Social Network Systems, pp. 1–8 (2011)
Lyu, C., et al.: Predictable model for detecting sybil attacks in mobile social networks. In: 2021 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6. IEEE (2021)
Lobo, A., Mandekar, Y., Pundpal, S., Roy, B.: Detection of sybil attacks in social networks. In: Chellappan, S., Choo, K.-K., Phan, N. (eds.) CSoNet. LNCS, vol. 12575, pp. 366–377. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-66046-8_30
Xu, E.H.W., Hui, P.M.: Uncovering complex overlapping pattern of communities in large-scale social networks. Appl. Netw. Sci. 4(1), 1–16 (2019). https://doi.org/10.1007/s41109-019-0138-z
Wang, G., Konolige, T., Wilson, C., Wang, X., Zheng, H., Zhao, B.Y.: You are how you click: clickstream analysis for sybil detection. In: 22nd USENIX Security Symposium ({USENIX}Security 13), pp. 241–256 (2013)
Shin, K., Hooi, B., Kim, J., Faloutsos, C.: D-cube: dense-block detection interabyte-scale tensors. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 681–689 (2017)
Tang, B., Zhang, L.: Local preserving logistic i-relief for semi-supervised feature selection. Neurocomputing 399, 48–64 (2020)
Chen, J., Tang, G.: A feature selection model to filter periodic variable stars with data-sensitive light-variable characteristics. J. Signal Process. Syst. 93(7), 733–744 (2021)
Liu, H., Motoda, H.: Computational Methods of Feature Selection. CRC Press, Boca Raton (2007)
Selvalakshmi, B., Subramaniam, M.: Intelligent ontology based semantic information retrieval using feature selection and classification. Clust. Comput. 22(5), 12871–12881 (2018). https://doi.org/10.1007/s10586-018-1789-8
Ndaoud, M.: Contributions to variable selection, clustering and statistical estimation in high dimension (2019)
Wang, X., Wang, Z., Zhang, Y., Jiang, X., Cai, Z.: Latent representation learning based autoencoder for unsupervised feature selection in hyper-spectral imagery. Multimedia Tools Appl. 1–15 (2021). https://doi.org/10.1007/s11042-020-10474-8
Zhang, L., Huang, X., Zhou, W.: Logistic local hyperplane-relief: a feature weighting method for classification. Knowl.-Based Syst. 181, 104741 (2019)
Klein, A., Melard, G.: Invertibility condition of the fisher information matrix of a varmax process and the tensor sylvester matrix. In: Working Papers ECARES (2020)
Kl, A., Xy, A., Hy, A., Jm, B., Pwb, C., Xc, A.: Rough set based semi-supervised feature selection via ensemble selector. Knowl.-Based Syst. 165, 282–296 (2019)
Li, J., et al.: Feature selection: a data perspective. ACM Comput. Surv. 50(6), 1–45 (2016)
Zeng, X., Zheng, H.: CS sparse k-means: An algorithm for cluster-specific feature selection in high-dimensional clustering (2019)
Jza, B., Hp, A., Jt, A., Ql, A.: Generalized refined composite multiscale fuzzy entropy and multi-cluster feature selection based intelligent fault diagnosis of rolling bearing. ISA Trans. (2021)
Li, K., Zhang, J., Fang, Z.: Communication emitter identification based on kernel semi-supervised discriminant analysis. In: 2019 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS) (2019)
Benabdeslem, K., Hindawi, M.: Efficient semi-supervised feature selection: constraint, relevance, and redundancy. IEEE Trans. Knowl. Data Eng. 26(5), 1131–1143 (2014)
Fang, H., Tang, P., Si, H.: Feature selections using minimal redundancy maximal relevance algorithm for human activity recognition in smarthome environments. J. Healthc. Eng. 2020(1), 1–13 (2020)
Rastogi, A., Mehrotra, M.: Opinion spam detection in online reviews. J. Inf. Knowl. Manag. 16(04), 1750036 (2017)
Gayo-Avello, D., Brenes, D.J.: Overcoming spammers in Twitter-a taleof five algorithms (2010)
Velammal, B.L., Aarthy, N.: Improvised spam detection in twitter datausing lightweight detectors and classifiers. Int. J. Web-Based Learn. Teach. Technol. (IJWLTT) 16, 12–32 (2021)
Mojiri, M.M., Ravanmehr, R.: Event detection in Twitter using multi timing chained windows. Comput. Inf. 39(6), 1336–1359 (2020)
Aswani, R., Kar, A.K., Ilavarasan, P.V.: Detection of spammers in Twitter marketing: a hybrid approach using social media analytics and bioinspired computing. Inf. Syst. Front. 20(3), 515–530 (2018)
Youlve, C., Kaiyun, B., Jiangtian, C.: Credit decision system based on combination weight and extreme gradient boosting algorithm. J. Phys. Conf. Ser. 1955, 012081(2021)
Shin, K., Hooi, B., Faloutsos, C.: M-zoom: fast dense-block detection in tensors with quality guarantees. In: Frasconi, P., Landwehr, N., Manco, G., Vreeken, J. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9851, pp. 264–280. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46128-1_17
Charikar, M.: Greedy approximation algorithms for finding dense components in a graph. In: Jansen, K., Khuller, S. (eds.) APPROX 2000. LNCS, vol. 1913, pp. 84–95. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-44436-X_10
Jiang, M., Beutel, A., Cui, P., Hooi, B., Yang, S., Faloutsos, C.: A general suspiciousness metric for dense blocks in multimodal data. In: 2015 IEEE International Conference on Data Mining, pp. 781–786. IEEE (2015)
Acknowledgements
This work is supported by the National Natural Science Foundation of China (Grant No. 61976087 and Grant No. 62072170),
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Tang, Y., Zhang, D., Liang, W., Li, KC., Sukhija, N. (2022). Active Malicious Accounts Detection with Multimodal Fusion Machine Learning Algorithm. In: Wang, G., Choo, KK.R., Ko, R.K.L., Xu, Y., Crispo, B. (eds) Ubiquitous Security. UbiSec 2021. Communications in Computer and Information Science, vol 1557. Springer, Singapore. https://doi.org/10.1007/978-981-19-0468-4_4
Download citation
DOI: https://doi.org/10.1007/978-981-19-0468-4_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-0467-7
Online ISBN: 978-981-19-0468-4
eBook Packages: Computer ScienceComputer Science (R0)