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

With the increasing modernism in our society, networked computers are playing a pivotal role in dispersion of knowledge, and the protection of critical data in information systems has become a challenge for the research and industrial community. The intrusion detection systems undermine huge amounts of attack data to extrapolate patterns using machine learning techniques. In this paper, a two-stage intrusion detection model has been proposed to employ a blend of diverse attribute selection techniques and machine learning algorithms to provide high performance intrusion detection. The first stage extracts the relevant attributes by applying a hybrid meta-heuristic feature selection algorithm, and in the second stage, supervised machine learning algorithms have been implemented to improve the detection accuracy, execution time, and error rate. NSL-KDD dataset has been used, and the performance of CFS-MHA has been evaluated using different classification strategies. By using 10 attributes and random tree ensemble techniques, CFS-MHA has achieved an accuracy of 81.2% in detection of attacks.

CFS-MHA: A Two-Stage Network Intrusion Detection Framework