SEADer++ v2: Detecting Social Engineering Attacks using Natural Language Processing and Machine Learning

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBN

Abstract

Social engineering attacks are well known attacks in the cyberspace and relatively easy to try and implement because no technical knowledge is required. In various online environments such as business domains where customers talk through a chat service with employees or in social networks potential hackers can try to manipulate other people by employing social attacks against them to gain information that will benefit them in future attacks. Thus, we have used a number of natural language processing steps and a machine learning algorithm to identify potential attacks. The proposed method has been tested on a semi-synthetic dataset and it is shown to be both practical and effective.
Original languageEnglish
Title of host publication2020 International Conference on Innovations in Intelligent Systems and Applications
Subtitle of host publicationINISTA 2020
PublisherIEEE
ISBN (Print)9781728167992
Publication statusAccepted/In press - 2020

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    Lansley, M., Kapetanakis, S., & Polatidis, N. (Accepted/In press). SEADer++ v2: Detecting Social Engineering Attacks using Natural Language Processing and Machine Learning. In 2020 International Conference on Innovations in Intelligent Systems and Applications: INISTA 2020 IEEE.