  As machine learning and cybersecurity continue to explode in the context of
the digital ecosystem, the complexity of cybersecurity data combined with
complicated and evasive machine learning algorithms leads to vast difficulties
in designing an end to end system for intelligent, automatic anomaly
classification. On the other hand, traditional systems use elementary
statistics techniques and are often inaccurate, leading to weak centralized
data analysis platforms. In this paper, we propose a novel system that
addresses these two problems, titled CAMLPAD, for Cybersecurity Autonomous
Machine Learning Platform for Anomaly Detection. The CAMLPAD systems
streamlined, holistic approach begins with retrieving a multitude of different
species of cybersecurity data in real time using elasticsearch, then running
several machine learning algorithms, namely Isolation Forest, Histogram Based
Outlier Score (HBOS), Cluster Based Local Outlier Factor (CBLOF), and K Means
Clustering, to process the data. Next, the calculated anomalies are visualized
using Kibana and are assigned an outlier score, which serves as an indicator
for whether an alert should be sent to the system administrator that there are
potential anomalies in the network. After comprehensive testing of our platform
in a simulated environment, the CAMLPAD system achieved an adjusted rand score
of 95 percent, exhibiting the reliable accuracy and precision of the system.
All in all, the CAMLPAD system provides an accurate, streamlined approach to
real time cybersecurity anomaly detection, delivering a novel solution that has
the potential to revolutionize the cybersecurity sector.
