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

Merton Lansley; Stelios Kapetanakis; Nikolaos Polatidis

doi:10.1109/INISTA49547.2020.9194623

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

Merton Lansley, Stelios Kapetanakis, Nikolaos Polatidis

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

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 language	English
Title of host publication	INISTA 2020 - 2020 International Conference on INnovations in Intelligent SysTems and Applications, Proceedings
Subtitle of host publication	INISTA 2020
Editors	Mirjana Ivanovic, Tulay Yildirim, Goce Trajcevski, Costin Badica, Ladjel Bellatreche, Igor Kotenko, Amelia Badica, Burcu Erkmen, Milos Savic
Publisher	IEEE
Pages	1-6
ISBN (Electronic)	9781728167992
ISBN (Print)	9781728168005
DOIs	https://doi.org/10.1109/INISTA49547.2020.9194623
Publication status	Published - 11 Sept 2020
Event	IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) - Duration: 24 Aug 2020 → 26 Aug 2020

Publication series

Name	INISTA 2020 - 2020 International Conference on INnovations in Intelligent SysTems and Applications, Proceedings

Conference

Conference	IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)
Period	24/08/20 → 26/08/20

Keywords

Attack detection
Cyber security
Machine Learning
Natural language processing
Social Engineering

Access to Document

10.1109/INISTA49547.2020.9194623

SEADer++ v2: Detecting Social Engineering Attacks using Natural Language Processing and Machine LearningAccepted author manuscript, 390 KB

Cite this

Lansley, M., Kapetanakis, S., & Polatidis, N. (2020). SEADer++ v2: Detecting Social Engineering Attacks using Natural Language Processing and Machine Learning. In M. Ivanovic, T. Yildirim, G. Trajcevski, C. Badica, L. Bellatreche, I. Kotenko, A. Badica, B. Erkmen, & M. Savic (Eds.), INISTA 2020 - 2020 International Conference on INnovations in Intelligent SysTems and Applications, Proceedings: INISTA 2020 (pp. 1-6). Article 9194623 (INISTA 2020 - 2020 International Conference on INnovations in Intelligent SysTems and Applications, Proceedings). IEEE. https://doi.org/10.1109/INISTA49547.2020.9194623

Lansley, Merton ; Kapetanakis, Stelios ; Polatidis, Nikolaos. / SEADer++ v2 : Detecting Social Engineering Attacks using Natural Language Processing and Machine Learning. INISTA 2020 - 2020 International Conference on INnovations in Intelligent SysTems and Applications, Proceedings: INISTA 2020. editor / Mirjana Ivanovic ; Tulay Yildirim ; Goce Trajcevski ; Costin Badica ; Ladjel Bellatreche ; Igor Kotenko ; Amelia Badica ; Burcu Erkmen ; Milos Savic. IEEE, 2020. pp. 1-6 (INISTA 2020 - 2020 International Conference on INnovations in Intelligent SysTems and Applications, Proceedings).

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title = "SEADer++ v2: Detecting Social Engineering Attacks using Natural Language Processing and Machine Learning",

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.",

keywords = "Attack detection, Cyber security, Machine Learning, Natural language processing, Social Engineering",

author = "Merton Lansley and Stelios Kapetanakis and Nikolaos Polatidis",

year = "2020",

month = sep,

day = "11",

doi = "10.1109/INISTA49547.2020.9194623",

language = "English",

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series = "INISTA 2020 - 2020 International Conference on INnovations in Intelligent SysTems and Applications, Proceedings",

publisher = "IEEE",

pages = "1--6",

editor = "Mirjana Ivanovic and Tulay Yildirim and Goce Trajcevski and Costin Badica and Ladjel Bellatreche and Igor Kotenko and Amelia Badica and Burcu Erkmen and Milos Savic",

booktitle = "INISTA 2020 - 2020 International Conference on INnovations in Intelligent SysTems and Applications, Proceedings",

note = "IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) ; Conference date: 24-08-2020 Through 26-08-2020",

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Lansley, M, Kapetanakis, S & Polatidis, N 2020, SEADer++ v2: Detecting Social Engineering Attacks using Natural Language Processing and Machine Learning. in M Ivanovic, T Yildirim, G Trajcevski, C Badica, L Bellatreche, I Kotenko, A Badica, B Erkmen & M Savic (eds), INISTA 2020 - 2020 International Conference on INnovations in Intelligent SysTems and Applications, Proceedings: INISTA 2020., 9194623, INISTA 2020 - 2020 International Conference on INnovations in Intelligent SysTems and Applications, Proceedings, IEEE, pp. 1-6, IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA), 24/08/20. https://doi.org/10.1109/INISTA49547.2020.9194623

SEADer++ v2: Detecting Social Engineering Attacks using Natural Language Processing and Machine Learning. / Lansley, Merton; Kapetanakis, Stelios; Polatidis, Nikolaos.
INISTA 2020 - 2020 International Conference on INnovations in Intelligent SysTems and Applications, Proceedings: INISTA 2020. ed. / Mirjana Ivanovic; Tulay Yildirim; Goce Trajcevski; Costin Badica; Ladjel Bellatreche; Igor Kotenko; Amelia Badica; Burcu Erkmen; Milos Savic. IEEE, 2020. p. 1-6 9194623 (INISTA 2020 - 2020 International Conference on INnovations in Intelligent SysTems and Applications, Proceedings).

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

TY - GEN

T1 - SEADer++ v2

T2 - IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)

AU - Lansley, Merton

AU - Kapetanakis, Stelios

AU - Polatidis, Nikolaos

PY - 2020/9/11

Y1 - 2020/9/11

N2 - 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.

AB - 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.

KW - Attack detection

KW - Cyber security

KW - Machine Learning

KW - Natural language processing

KW - Social Engineering

UR - http://inista.org/accepted-papers.php

UR - http://www.scopus.com/inward/record.url?scp=85091978571&partnerID=8YFLogxK

U2 - 10.1109/INISTA49547.2020.9194623

DO - 10.1109/INISTA49547.2020.9194623

M3 - Conference contribution with ISSN or ISBN

SN - 9781728168005

T3 - INISTA 2020 - 2020 International Conference on INnovations in Intelligent SysTems and Applications, Proceedings

SP - 1

EP - 6

BT - INISTA 2020 - 2020 International Conference on INnovations in Intelligent SysTems and Applications, Proceedings

A2 - Ivanovic, Mirjana

A2 - Yildirim, Tulay

A2 - Trajcevski, Goce

A2 - Badica, Costin

A2 - Bellatreche, Ladjel

A2 - Kotenko, Igor

A2 - Badica, Amelia

A2 - Erkmen, Burcu

A2 - Savic, Milos

PB - IEEE

Y2 - 24 August 2020 through 26 August 2020

ER -

Lansley M, Kapetanakis S, Polatidis N. SEADer++ v2: Detecting Social Engineering Attacks using Natural Language Processing and Machine Learning. In Ivanovic M, Yildirim T, Trajcevski G, Badica C, Bellatreche L, Kotenko I, Badica A, Erkmen B, Savic M, editors, INISTA 2020 - 2020 International Conference on INnovations in Intelligent SysTems and Applications, Proceedings: INISTA 2020. IEEE. 2020. p. 1-6. 9194623. (INISTA 2020 - 2020 International Conference on INnovations in Intelligent SysTems and Applications, Proceedings). doi: 10.1109/INISTA49547.2020.9194623

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

Abstract

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Nikolaos Polatidis

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

Abstract

Publication series

Conference

Keywords

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Nikolaos Polatidis

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