A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems
As the world moves towards being increasingly dependent on computers and automation, building secure applications, systems and networks are some of the main challenges faced in the current decade. The number of threats that individuals and businesses face is rising exponentially due to the increasin...
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doaj-d11d511c82b948cbab6036c431d2d3612021-03-30T02:57:38ZengIEEEIEEE Access2169-35362020-01-01810465010467510.1109/ACCESS.2020.30001799108270A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection SystemsHanan Hindy0https://orcid.org/0000-0002-5195-8193David Brosset1https://orcid.org/0000-0002-9677-1445Ethan Bayne2https://orcid.org/0000-0003-1853-2921Amar Kumar Seeam3https://orcid.org/0000-0002-8393-3214Christos Tachtatzis4https://orcid.org/0000-0001-9150-6805Robert Atkinson5https://orcid.org/0000-0002-6206-2229Xavier Bellekens6https://orcid.org/0000-0003-1849-5788Division of Cyber Security, Abertay University, Dundee, U.K.Naval Academy Research Institute, Brest, FranceDivision of Cyber Security, Abertay University, Dundee, U.K.Department of Computer Science, Middlesex University, Uniciti, MauritiusEEE Department, University of Strathclyde, Glasgow, U.K.EEE Department, University of Strathclyde, Glasgow, U.K.Division of Cyber Security, Abertay University, Dundee, U.K.As the world moves towards being increasingly dependent on computers and automation, building secure applications, systems and networks are some of the main challenges faced in the current decade. The number of threats that individuals and businesses face is rising exponentially due to the increasing complexity of networks and services of modern networks. To alleviate the impact of these threats, researchers have proposed numerous solutions for anomaly detection; however, current tools often fail to adapt to ever-changing architectures, associated threats and zero-day attacks. This manuscript aims to pinpoint research gaps and shortcomings of current datasets, their impact on building Network Intrusion Detection Systems (NIDS) and the growing number of sophisticated threats. To this end, this manuscript provides researchers with two key pieces of information; a survey of prominent datasets, analyzing their use and impact on the development of the past decade's Intrusion Detection Systems (IDS) and a taxonomy of network threats and associated tools to carry out these attacks. The manuscript highlights that current IDS research covers only 33.3% of our threat taxonomy. Current datasets demonstrate a clear lack of real-network threats, attack representation and include a large number of deprecated threats, which together limit the detection accuracy of current machine learning IDS approaches. The unique combination of the taxonomy and the analysis of the datasets provided in this manuscript aims to improve the creation of datasets and the collection of real-world data. As a result, this will improve the efficiency of the next generation IDS and reflect network threats more accurately within new datasets.https://ieeexplore.ieee.org/document/9108270/Anomaly detectiondatasetsintrusion detection systemsnetwork attacksnetwork securitysecurity threats |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hanan Hindy David Brosset Ethan Bayne Amar Kumar Seeam Christos Tachtatzis Robert Atkinson Xavier Bellekens |
spellingShingle |
Hanan Hindy David Brosset Ethan Bayne Amar Kumar Seeam Christos Tachtatzis Robert Atkinson Xavier Bellekens A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems IEEE Access Anomaly detection datasets intrusion detection systems network attacks network security security threats |
author_facet |
Hanan Hindy David Brosset Ethan Bayne Amar Kumar Seeam Christos Tachtatzis Robert Atkinson Xavier Bellekens |
author_sort |
Hanan Hindy |
title |
A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems |
title_short |
A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems |
title_full |
A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems |
title_fullStr |
A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems |
title_full_unstemmed |
A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems |
title_sort |
taxonomy of network threats and the effect of current datasets on intrusion detection systems |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
As the world moves towards being increasingly dependent on computers and automation, building secure applications, systems and networks are some of the main challenges faced in the current decade. The number of threats that individuals and businesses face is rising exponentially due to the increasing complexity of networks and services of modern networks. To alleviate the impact of these threats, researchers have proposed numerous solutions for anomaly detection; however, current tools often fail to adapt to ever-changing architectures, associated threats and zero-day attacks. This manuscript aims to pinpoint research gaps and shortcomings of current datasets, their impact on building Network Intrusion Detection Systems (NIDS) and the growing number of sophisticated threats. To this end, this manuscript provides researchers with two key pieces of information; a survey of prominent datasets, analyzing their use and impact on the development of the past decade's Intrusion Detection Systems (IDS) and a taxonomy of network threats and associated tools to carry out these attacks. The manuscript highlights that current IDS research covers only 33.3% of our threat taxonomy. Current datasets demonstrate a clear lack of real-network threats, attack representation and include a large number of deprecated threats, which together limit the detection accuracy of current machine learning IDS approaches. The unique combination of the taxonomy and the analysis of the datasets provided in this manuscript aims to improve the creation of datasets and the collection of real-world data. As a result, this will improve the efficiency of the next generation IDS and reflect network threats more accurately within new datasets. |
topic |
Anomaly detection datasets intrusion detection systems network attacks network security security threats |
url |
https://ieeexplore.ieee.org/document/9108270/ |
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