An Effective Double-layer Detection System Against Social Engineering Attacks

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

2 Scopus Citations
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Author(s)

  • Daojing He
  • Xin Lv
  • Xueqian Xu
  • Shui Yu
  • Dawei Li
  • Mohsen Guizani

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)92-98
Journal / PublicationIEEE Network
Volume36
Issue number6
Online published1 Aug 2022
Publication statusPublished - Nov 2022

Abstract

In recent years, social engineering attacks that use phishing emails as the medium and target specific groups of people have occurred frequently. Current enterprise systems are vulnerable to detect social engineering attacks. In addition, existing detection methods are relatively ineffective. Therefore, we propose a double-layer detection framework based on deep learning technology. First, a phishing email detection model based on Long Short-Term Memory (LSTM) and extreme gradient boosting tree (XGBoost) is designed from the perspective of individual security. Then, an insider threat detection model based on Bidirectional LSTM and Attention mechanism is designed from the perspective of group security. Finally, combined with the social engineering network attack simulation theory, a social engineering attack and defense simulation platform is established. In the double-layer framework, we use Bi-LSTM to obtain long-range dependent features of email body and user sequence information. Then XGBoost and Attention mechanism are used to further strengthen the network structure and improve the classification accuracy. Compared with traditional methods, our model does not require manual feature extraction, and can accurately identify phishing emails and insider threats. Finally, our proposed social engineering simulation platform verifies the effectiveness of the two-layer model. The experimental results show that our proposed framework has the characteristics of timely detection and after-the-fact investigation, which can effectively detect phishing attacks and insider threats faced by enterprise systems.

Research Area(s)

  • Data mining, Deep learning, Electronic mail, Feature extraction, Hidden Markov models, Phishing, Psychology

Citation Format(s)

An Effective Double-layer Detection System Against Social Engineering Attacks. / He, Daojing; Lv, Xin; Xu, Xueqian et al.
In: IEEE Network, Vol. 36, No. 6, 11.2022, p. 92-98.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review