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Cacography based Ransomware Email Phishing Attack Prevention using Language Pack Tuned Transformer Language Model.

S Abiramasundari1, V Ramaswamy2

  • 1Computer Science and Engineering, SASTRA Deemed University, Kumbakonam, India. sabiarul07@gmail.com.

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Summary
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This study introduces a new Language Pack-based Tuned Transformer Language (LPTTL) framework to combat sophisticated ransomware email phishing (REP) attacks. The LPTTL framework effectively detects and classifies these threats using advanced cacography and machine learning algorithms, achieving high accuracy.

Keywords:
CacographyNatural language processingPhishing emailRecurrent neural networkTransformer model

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Area of Science:

  • Cybersecurity
  • Natural Language Processing
  • Machine Learning

Background:

  • Ransomware email phishing (REP) attacks pose a significant threat, utilizing deceptive emails and text messages.
  • Existing detection methods often overlook linguistic nuances like cacography and language packs within email text.
  • Current models lack the sophistication to analyze the specific textual characteristics employed in novel phishing strategies.

Purpose of the Study:

  • To propose a novel Language Pack-based Tuned Transformer Language (LPTTL) framework for enhanced detection of ransomware email phishing (REP) attacks.
  • To introduce advanced cacography algorithms for nuanced text analysis in identifying phishing attempts.
  • To develop robust classification models for accurate REP threat classification based on tokenized email text.

Main Methods:

  • The LPTTL framework utilizes cacography algorithms, including Language Pack Tuned Bidirectional Encoder Representation Transformer (LPT-BERT) and Text-Text Transfer Transformer (LPT-T5), for detecting REP attacks.
  • Email body text is tokenized to analyze words and embedded linguistic patterns.
  • Classification employs algorithms such as Lyrebird Optimization Algorithm-Long Short-Term Memory (LOA-LSTM), Hippopotamus Optimization (HO)-Gated Recurrent Neural Network (HO-GRU), and Meerkat Optimization Algorithm-Bidirectional Long Short-Term Memory (MOA-BiLSTM).

Main Results:

  • The proposed LPTTL framework demonstrates a high accuracy of approximately 95.47%.
  • The system achieved a precision of 96.8%, recall of 95.63%, and an F1-score of 96.21%.
  • These performance metrics significantly outperform existing methods in detecting ransomware email phishing.

Conclusions:

  • The LPTTL framework offers a significant advancement in combating novel ransomware email phishing attacks.
  • The integration of cacography analysis and advanced transformer models provides a more effective detection mechanism.
  • The study highlights the potential of language pack-specific analysis for improving cybersecurity defenses against sophisticated phishing.