This is a non-IMPACT record, meaning that access to the data is not controlled by IMPACT. For access, see the directions below.

Disclaimer:
This Resource is offered and provided outside of the IMPACT mediation framework. IMPACT and the IMPACT Coordination Council/Blackfire Technology, Inc. expressly disclaim all conditions, representations and warranties including but not limited to Resource availability, quality, accuracy, non-infringement, and non-interference. All Resource information and access is controlled by entities and under terms that are external to the IMPACT legal framework.

Summary

DS-1208
network-threats-taxonomy
External Dataset
External Data Source
GitHub
Unknown
Unknown
56 (lowest rank is 56)

Category & Restrictions

Other
cyber defense, intrusion detection
Unrestricted
true

Description


Machine Learning based Intrusion Detection Systems are difficult to evaluate due to a shortage of datasets representing accurately network traffic and their associated threats.

In this project we attempt at solving this problem by presenting two taxonomies    A Taxonomy and Survey of Intrusion Detection System Design Techniques, Network Threats and Datasets    and    A Taxonomy of Malicious Traffic for Intrusion Detection Systems , classifying threats as well as evaluating current datasets.

The result shows that a large portion of current research published train IDS algorithms against outdated datasets and outdated threats. To this end, we provide the source ofour threat taxonomy, allowing other researchers to contribute and modify it. The taxonomy is a collaboration between Abertay University, The University of Strathclyde, The Naval Academy Research Institute and Middlesex University (Mauritius Campus)

The taxonomy classifies each network threat according to:
Its Source (i.e. Networking, Host, Software, Hardware, Human)
OSI Layer
Active / Passive


We have released a pre-print of our article on Arxiv, it includes the original taxonomies in a paper called
A Taxonomy and Survey of Intrusion Detection System Design Techniques, Network Threats and Datasets

Additional Details

275B
false
Unknown
threats, network, taxonomy, network-threats-taxonomy, 1208, source, external data source, corporation, inferlink corporation, inferlink, external, datasets, detection, intrusion, traffic, systems, evaluate, difficult, based, learning, accurately, machine, representing, shortage, techniques, survey, design, taxonomies, system, threat, current, outdated, arxiv, networking, academy, human, ofour, attempt, host, classifies, paper, strathclyde, abertay, original, software, collaboration, osi, called, print, modify, passive, allowing, middlesex, project, released, malicious, evaluating, ids, researchers, campus, naval, active, provide, includes, published, classifying, institute, pre, result, train, layer, mauritius, hardware, contribute, portion, solving, other, algorithms, article