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.
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This work is supported by the National Natural Science Foundation of China (Grant No. 61976087 and Grant No. 62072170),
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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
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