Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/1807
Title: Applying BERT for Early-Stage Recognition of Persistence in Chat-Based Social Engineering Attacks
Authors: Tsinganos, Nikolaos
Fouliras, Panagiotis
Mavridis, Ioannis
Type: Article
Subjects: FRASCATI::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering
Keywords: cybersecurity
sensitive data
chat-based social engineering attack
persistence
deep learning
natural language processing
transfer learning
BERT
Issue Date: 2022
Source: Applied Sciences
Volume: 12
Issue: 23
First Page: 12353
Abstract: Chat-based social engineering (CSE) attacks are attracting increasing attention in the Small-Medium Enterprise (SME) environment, given the ease and potential impact of such an attack. During a CSE attack, malicious users will repeatedly use linguistic tricks to eventually deceive their victims. Thus, to protect SME users, it would be beneficial to have a cyber-defense mechanism able to detect persistent interlocutors who repeatedly bring up critical topics that could lead to sensitive data exposure. We build a natural language processing model, called CSE-PersistenceBERT, for paraphrase detection to recognize persistency as a social engineering attacker’s behavior during a chat-based dialogue. The CSE-PersistenceBERT model consists of a pre-trained BERT model fine-tuned using our handcrafted CSE-Persistence corpus; a corpus appropriately annotated for the specific downstream task of paraphrase recognition. The model identifies the linguistic relationship between the sentences uttered during the dialogue and exposes the malicious intent of the attacker. The results are satisfactory and prove the efficiency of CSE-PersistenceBERT as a recognition mechanism of a social engineer’s persistent behavior during a CSE attack.
URI: https://doi.org/10.3390/app122312353
https://ruomo.lib.uom.gr/handle/7000/1807
ISSN: 2076-3417
Other Identifiers: 10.3390/app122312353
Appears in Collections:Department of Applied Informatics

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