Original scientific paper
https://doi.org/10.24138/jcomss-2021-0113
Semantic Detection of Targeted Attacks Using DOC2VEC Embedding
Mariam S. El-Rahmany
; Department of Computer Science, Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt
Ensaf Hussein Mohamed
; Department of Computer Science, Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt
Mohamed H. Haggag
; Department of Computer Science, Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt
Abstract
The targeted attack is one of the social engineering attacks. The detection of this type of attack is considered a challenge as it depends on semantic extraction of the intent of the attacker. However, previous research has primarily relies on the Natural Language Processing or Word Embedding techniques that lack the context of the attacker's text message. Based on Sentence Embedding and machine learning approaches, this paper introduces a model for semantic detection of targeted attacks. This model has the advantage of encoding relevant information, which helps to improve the performance of the multi-class classification process. Messages will be categorized based on the type of security rule that the attacker has violated. The suggested model was tested using a dialogue dataset taken from phone calls, which was manually categorized into four categories. The text is pre-processed using natural language processing techniques, and the semantic features are extracted as Sentence Embedding vectors that are augmented with security policy sentences. Machine Learning algorithms are applied to classify text messages. The experimental results show that sentence embeddings with doc2vec achieved high prediction accuracy 96.8%. So, it outperformed the method applied to the same dialog dataset.
Keywords
Doc2vec; Multi-class text classification; Pretexting; Sentence Embedding; Targeted Attacks Detection
Hrčak ID:
270738
URI
Publication date:
30.12.2021.
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