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

Mitigating Denial-of-Service (DoS) attacks is vital for online servicesecurity and availability. While machine learning (ML) models are used for DoSattack detection, new strategies are needed to enhance their performance. Wesuggest an innovative method, combinatorial fusion, which combines multiple MLmodels using advanced algorithms. This includes score and rank combinations,weighted techniques, and diversity strength of scoring systems. Throughrigorous evaluations, we demonstrate the effectiveness of this fusion approach,considering metrics like precision, recall, and F1-score. We address thechallenge of low-profiled attack classification by fusing models to create acomprehensive solution. Our findings emphasize the potential of this approachto improve DoS attack detection and contribute to stronger defense mechanisms.

Enhancing ML-Based DoS Attack Detection Through Combinatorial Fusion Analysis