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

Modern Artificial Intelligence (AI) enabled Intrusion Detection Systems (IDS)are complex black boxes. This means that a security analyst will have little tono explanation or clarification on why an IDS model made a particularprediction. A potential solution to this problem is to research and developExplainable Intrusion Detection Systems (X-IDS) based on current capabilitiesin Explainable Artificial Intelligence (XAI). In this paper, we create a SelfOrganizing Maps (SOMs) based X-IDS system that is capable of producingexplanatory visualizations. We leverage SOM's explainability to create bothglobal and local explanations. An analyst can use global explanations to get ageneral idea of how a particular IDS model computes predictions. Localexplanations are generated for individual datapoints to explain why a certainprediction value was computed. Furthermore, our SOM based X-IDS was evaluatedon both explanation generation and traditional accuracy tests using the NSL-KDDand the CIC-IDS-2017 datasets.

Creating an Explainable Intrusion Detection System Using Self Organizing Maps