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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

A multitude of toxic online behaviors, ranging from network attacks toanonymous traffic and spam, have severely disrupted the smooth operation ofnetworks. Due to the inherent sender-receiver nature of network behaviors,graph-based frameworks are commonly used for detecting anomalous behaviors.However, in real-world scenarios, the boundary between normal and anomalousbehaviors tends to be ambiguous. The local heterophily of graphs interfereswith the detection, and existing methods based on nodes or edges introduceunwanted noise into representation results, thereby impacting the effectivenessof detection. To address these issues, we propose PhoGAD, a graph-based anomalydetection framework. PhoGAD leverages persistent homology optimization toclarify behavioral boundaries. Building upon this, the weights of adjacentedges are designed to mitigate the effects of local heterophily. Subsequently,to tackle the noise problem, we conduct a formal analysis and propose adisentangled representation-based explicit embedding method, ultimatelyachieving anomaly behavior detection. Experiments on intrusion, traffic, andspam datasets verify that PhoGAD has surpassed the performance ofstate-of-the-art (SOTA) frameworks in detection efficacy. Notably, PhoGADdemonstrates robust detection even with diminished anomaly proportions,highlighting its applicability to real-world scenarios. The analysis ofpersistent homology demonstrates its effectiveness in capturing the topologicalstructure formed by normal edge features. Additionally, ablation experimentsvalidate the effectiveness of the innovative mechanisms integrated withinPhoGAD.

PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization