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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Machine learning (ML) has developed rapidly in the past few years and hassuccessfully been utilized for a broad range of tasks, including phishingdetection. However, building an effective ML-based detection system is not atrivial task, and requires data scientists with knowledge of the relevantdomain. Automated Machine Learning (AutoML) frameworks have received a lot ofattention in recent years, enabling non-ML experts in building a machinelearning model. This brings to an intriguing question of whether AutoML canoutperform the results achieved by human data scientists. Our paper comparesthe performances of six well-known, state-of-the-art AutoML frameworks on tendifferent phishing datasets to see whether AutoML-based models can outperformmanually crafted machine learning models. Our results indicate thatAutoML-based models are able to outperform manually developed machine learningmodels in complex classification tasks, specifically in datasets where thefeatures are not quite discriminative, and datasets with overlapping classes orrelatively high degrees of non-linearity. Challenges also remain in building areal-world phishing detection system using AutoML frameworks due to the currentsupport only on supervised classification problems, leading to the need forlabeled data, and the inability to update the AutoML-based modelsincrementally. This indicates that experts with knowledge in the domain ofphishing and cybersecurity are still essential in the loop of the phishingdetection pipeline.

Man versus Machine: AutoML and Human Experts' Role in Phishing Detection