English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The most critical challenge of web attack forensic investigations is the sheer amount of data and level of complexity. Machine learning technology might be an efficient solution for web attack analysis and investigation. Consequently, machine learning applications have been applied in various areas of information security and digital forensics, and have improved over time. Moreover, feature selection is a crucial step in machine learning; in fact, selecting an optimal feature subset could enhance the accuracy and performance of the predictive model. To date, there has not been an adequate approach to select optimal features for the evidence of web attack. In this study, a hybrid approach that selects the relevant web attack features by combining the filter and wrapper methods is proposed. This approach has been validated by experimental measurements on 3 web attack datasets. The results show that our proposed approach can find the evidence with high recall, high accuracy, and low error rates. We believe that the results presented herein may help us to improve accuracy and recall of machine learning techniques; particularly, in the field of web attack investigation. The tools that use this approach may help digital forensic professionals and law enforcement in finding the evidence much more efficiently and faster.

A hybrid feature-selection approach for finding the digital evidence of webapplication attacks