  A vital element of a cyberspace infrastructure is cybersecurity. Many
protocols proposed for security issues, which leads to anomalies that affect
the related infrastructure of cyberspace. Machine learning (ML) methods used to
mitigate anomalies behavior in mobile devices. This paper aims to apply a High
Performance Extreme Learning Machine (HP-ELM) to detect possible anomalies in
two malware datasets. Two widely used datasets (the CTU-13 and Malware) are
used to test the effectiveness of HP-ELM. Extensive comparisons are carried out
in order to validate the effectiveness of the HP-ELM learning method. The
experiment results demonstrate that the HP-ELM was the highest accuracy of
performance of 0.9592 for the top 3 features with one activation function.
