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

In the Internet age, cyber-attacks occur frequently with complex types.Traffic generated by access activities can record website status and userrequest information, which brings a great opportunity for network attackdetection. Among diverse network protocols, Hypertext Transfer Protocol (HTTP)is widely used in government, organizations and enterprises. In this work, wepropose DeepHTTP, a semantics structure integration model utilizingBidirectional Long Short-Term Memory (Bi-LSTM) with attention mechanism tomodel HTTP traffic as a natural language sequence. In addition to extractingtraffic content information, we integrate structural information to enhance thegeneralization capabilities of the model. Moreover, the application ofattention mechanism can assist in discovering critical parts of anomaloustraffic and further mining attack patterns. Additionally, we demonstrate how toincrementally update the data set and retrain model so that it can be adaptedto new anomalous traffic. Extensive experimental evaluations over large trafficdata have illustrated that DeepHTTP has outstanding performance in trafficdetection and pattern discovery.

DeepHTTP: Semantics-Structure Model with Attention for Anomalous HTTP Traffic Detection and Pattern Mining