@inproceedings{alshdadi-2024-lstm,
    title = "{LSTM}-{PSO}: {NLP}-based model for detecting Phishing Attacks",
    author = "Alshdadi, Abdulrahman A.",
    editor = "Mitkov, Ruslan  and
      Ezzini, Saad  and
      Ranasinghe, Tharindu  and
      Ezeani, Ignatius  and
      Khallaf, Nouran  and
      Acarturk, Cengiz  and
      Bradbury, Matthew  and
      El-Haj, Mo  and
      Rayson, Paul",
    booktitle = "Proceedings of the First International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security",
    month = jul,
    year = "2024",
    address = "Lancaster, UK",
    publisher = "International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.nlpaics-1.9/",
    pages = "70--79",
    abstract = "Detecting phishing attacks involves recognizing and stopping attempts to trick users into revealing information, like passwords, credit card details or personal data without authorization. While most recent related work focus on detecting phishing attacks by analyzing, URLs, email header and content and web pages based on their content, regardless of entering text sequentially into Deep Learning (DL) algorithms. This aapproach causes the intrinsic richness of the relationship between words and part of speech to be lost. This study main contribution is to detect phishing attacks by introducing an integrated model that emphasizes on analyzing the text content of suspicious web pages a model that detects not on URL addresses. The approach of the proposed model is based on using Natural Language Processing (NLP) for processing webpage content, Particle swarm optimization algorithm (PSO) for optimizing feature extraction process and Deep Learning (DL) algorithms for classifying web page content into phishing or legitimate. NLP techniquees are used to preprocess webpage content and word2vector embeddings for Word Representation to extract and select best features into DL algorithm. Two different approaches Long Short-Term Memory (LSTM) are assessed: traditional LSTM and enhanced LSTM-PSO. The results show promising outcomes by the proposed model in detecting phishing attacks as both LSTM and LSTM-PSO achieved an accuracy of 97{\%} and 98.3 respectively."
}