Adversarial Machine Learning Applied to Intrusion and Malware Scenarios: A Systematic Review
Cyber-security is the practice of protecting computing systems and networks from digital attacks, which are a rising concern in the Information Age. With the growing pace at which new attacks are developed, conventional signature based attack detection methods are often not enough, and machine learn...
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doaj-32e7b5b3c0a44c42b68b388c6323b6d12021-03-30T02:42:48ZengIEEEIEEE Access2169-35362020-01-018354033541910.1109/ACCESS.2020.29747529001114Adversarial Machine Learning Applied to Intrusion and Malware Scenarios: A Systematic ReviewNuno Martins0https://orcid.org/0000-0003-1665-6226Jose Magalhaes Cruz1https://orcid.org/0000-0003-4516-6752Tiago Cruz2https://orcid.org/0000-0001-9278-6503Pedro Henriques Abreu3https://orcid.org/0000-0002-9278-8194Faculty of Engineering, University of Porto, Porto, PortugalFaculty of Engineering, University of Porto, Porto, PortugalFaculty of Sciences and Technology, University of Coimbra, Coimbra, PortugalFaculty of Sciences and Technology, University of Coimbra, Coimbra, PortugalCyber-security is the practice of protecting computing systems and networks from digital attacks, which are a rising concern in the Information Age. With the growing pace at which new attacks are developed, conventional signature based attack detection methods are often not enough, and machine learning poses as a potential solution. Adversarial machine learning is a research area that examines both the generation and detection of adversarial examples, which are inputs specially crafted to deceive classifiers, and has been extensively studied specifically in the area of image recognition, where minor modifications are performed on images that cause a classifier to produce incorrect predictions. However, in other fields, such as intrusion and malware detection, the exploration of such methods is still growing. The aim of this survey is to explore works that apply adversarial machine learning concepts to intrusion and malware detection scenarios. We concluded that a wide variety of attacks were tested and proven effective in malware and intrusion detection, although their practicality was not tested in intrusion scenarios. Adversarial defenses were substantially less explored, although their effectiveness was also proven at resisting adversarial attacks. We also concluded that, contrarily to malware scenarios, the variety of datasets in intrusion scenarios is still very small, with the most used dataset being greatly outdated.https://ieeexplore.ieee.org/document/9001114/Cybersecurityadversarial machine learningintrusion detectionmalware detection |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Nuno Martins Jose Magalhaes Cruz Tiago Cruz Pedro Henriques Abreu |
spellingShingle |
Nuno Martins Jose Magalhaes Cruz Tiago Cruz Pedro Henriques Abreu Adversarial Machine Learning Applied to Intrusion and Malware Scenarios: A Systematic Review IEEE Access Cybersecurity adversarial machine learning intrusion detection malware detection |
author_facet |
Nuno Martins Jose Magalhaes Cruz Tiago Cruz Pedro Henriques Abreu |
author_sort |
Nuno Martins |
title |
Adversarial Machine Learning Applied to Intrusion and Malware Scenarios: A Systematic Review |
title_short |
Adversarial Machine Learning Applied to Intrusion and Malware Scenarios: A Systematic Review |
title_full |
Adversarial Machine Learning Applied to Intrusion and Malware Scenarios: A Systematic Review |
title_fullStr |
Adversarial Machine Learning Applied to Intrusion and Malware Scenarios: A Systematic Review |
title_full_unstemmed |
Adversarial Machine Learning Applied to Intrusion and Malware Scenarios: A Systematic Review |
title_sort |
adversarial machine learning applied to intrusion and malware scenarios: a systematic review |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Cyber-security is the practice of protecting computing systems and networks from digital attacks, which are a rising concern in the Information Age. With the growing pace at which new attacks are developed, conventional signature based attack detection methods are often not enough, and machine learning poses as a potential solution. Adversarial machine learning is a research area that examines both the generation and detection of adversarial examples, which are inputs specially crafted to deceive classifiers, and has been extensively studied specifically in the area of image recognition, where minor modifications are performed on images that cause a classifier to produce incorrect predictions. However, in other fields, such as intrusion and malware detection, the exploration of such methods is still growing. The aim of this survey is to explore works that apply adversarial machine learning concepts to intrusion and malware detection scenarios. We concluded that a wide variety of attacks were tested and proven effective in malware and intrusion detection, although their practicality was not tested in intrusion scenarios. Adversarial defenses were substantially less explored, although their effectiveness was also proven at resisting adversarial attacks. We also concluded that, contrarily to malware scenarios, the variety of datasets in intrusion scenarios is still very small, with the most used dataset being greatly outdated. |
topic |
Cybersecurity adversarial machine learning intrusion detection malware detection |
url |
https://ieeexplore.ieee.org/document/9001114/ |
work_keys_str_mv |
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