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

The purpose of the work described in this paper is to provide an intrusion detection system (IDS), by applying genetic algorithm (GA) to network intrusion detection system. Parameters and evolution process for GA are discussed in detail and implemented. This approach uses information theory to filter the traffic data and thus reduce the complexity. We use a linear structure rule to classify the network behaviors into normal and abnormal behaviors. This approach applied to the KDD99 benchmark dataset and obtained high detection rate up to 99.87% as well as low false positive rate 0.003%. Finally the results of this approach compared with available machine learning techniques.

Performance Evaluation of a Genetic Algorithm Based Approach to Network Intrusion Detection System