Dark Web Analytics : A Comparative Study of Feature Selection and Prediction Algorithms

6 Pages Posted: 27 Oct 2021

See all articles by Andrew Allhusen

Andrew Allhusen

Syracuse University

Izzat Alsmadi

Texas A&M University (TAMU) - San Antonio

Abdullah Wahbeh

Slippery Rock University

Mohammad Al-Ramahi

Department of Computing and Cyber Security, Texas A&M, San Antonio, TX, USA

Ahmad Al-Omari

Our Lady of the Lake University

Date Written: October 25, 2021

Abstract

The value and size of information exchanged through dark-web pages are remarkable.
Recently Many researches showed values and interests in using machine-learning
methods to extract security-related useful knowledge from those dark-web pages. In this scope, our goals in this research focus on evaluating best prediction models while analyzing traffic level
data coming from the dark web.
Results and analysis showed that feature selection played an important role when trying to identify the best models. Sometimes the right combination of features would increase the model’s
accuracy. For some feature set and classifier combinations, the Src Port and Dst Port both proved to be important features.
When available, they were always selected over most other features. When absent, it resulted in many other features being selected to compensate for the information they provided. The Protocol feature was never selected as a feature, regardless of whether Src Port and Dst Port were available

Suggested Citation

Allhusen, Andrew and Alsmadi, Izzat and Wahbeh, Abdullah and Al-Ramahi, Mohammad and Al-Omari, Ahmad, Dark Web Analytics : A Comparative Study of Feature Selection and Prediction Algorithms (October 25, 2021). Available at SSRN: https://ssrn.com/abstract=3949786 or http://dx.doi.org/10.2139/ssrn.3949786

Andrew Allhusen

Syracuse University ( email )

900 S. Crouse Avenue
Syracuse, NY 13244-2130
United States

Izzat Alsmadi (Contact Author)

Texas A&M University (TAMU) - San Antonio ( email )

One University Way
San Antonio, TX 78224
United States

Abdullah Wahbeh

Slippery Rock University ( email )

14 Maltby Drive
Slippery Rock, PA 16057
United States

Mohammad Al-Ramahi

Department of Computing and Cyber Security, Texas A&M, San Antonio, TX, USA ( email )

United States

Ahmad Al-Omari

Our Lady of the Lake University ( email )

411 S.W. 24th Street
San Antonio, TX 78207
United States

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