| Title | SEADer++ v2: Detecting Social Engineering Attacks using Natural Language Processing and Machine Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Lansley, M., Kapetanakis, S., Polatidis, N. |
| Conference Name | 2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) |
| Keywords | attack detection, blacklisting, Classification algorithms, cyber security, Forestry, future attacks, Human Behavior, Internet, learning (artificial intelligence), Libraries, machine learning, Mathematical model, natural language processing, natural language processing steps, potential attacks, pubcrawl, Resiliency, Scalability, SEADer++ v2, security of data, social attacks, Social Engineering, social engineering attacks detection, social networking (online), social networks potential hackers |
| 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. |
| DOI | 10.1109/INISTA49547.2020.9194623 |
| Citation Key | lansley_seader_2020 |
SEADer++ v2: Detecting Social Engineering Attacks using Natural Language Processing and Machine Learning