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

Web request query strings (queries), which pass parameters to the referencedresource, are always manipulated by attackers to retrieve sensitive data andeven take full control of victim web servers and web applications. However,existing malicious query detection approaches in the current literature cannotcope with changing web attacks with constant detection models. In this paper,we propose AMODS, an adaptive system that periodically updates the detectionmodel to detect the latest unknown attacks. We also propose an adaptivelearning strategy, called SVM HYBRID, leveraged by our system to minimizemanual work. In the evaluation, an up-to-date detection model is trained on aten-day query dataset collected from an academic institute's web server logs.Our system outperforms existing web attack detection methods, with an F-valueof 94.79% and FP rate of 0.09%. The total number of malicious queries obtainedby SVM HYBRID is 2.78 times that by the popular Support Vector Machine AdaptiveLearning (SVM AL) method. The malicious queries obtained can be used to updatethe Web Application Firewall (WAF) signature library.

Adaptively Detecting Malicious Queries in Web Attacks