SEADer++ v2: Detecting Social Engineering Attacks using Natural Language Processing and Machine Learning
Merton Lansley, Stelios Kapetanakis, Nikolaos Polatidis
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.
Topics & Concepts
HackerSocial engineering (security)Computer scienceCyberspaceComputer securityNatural (archaeology)PhishingNatural languageService (business)World Wide WebArtificial intelligenceThe InternetHistoryEconomyArchaeologyEconomicsSpam and Phishing DetectionAdvanced Malware Detection TechniquesNetwork Security and Intrusion Detection