  With increased reliance on Internet based technologies, cyberattacks
compromising users' sensitive data are becoming more prevalent. The scale and
frequency of these attacks are escalating rapidly, affecting systems and
devices connected to the Internet. The traditional defense mechanisms may not
be sufficiently equipped to handle the complex and ever-changing new threats.
The significant breakthroughs in the machine learning methods including deep
learning, had attracted interests from the cybersecurity research community for
further enhancements in the existing anomaly detection methods. Unfortunately,
collecting labelled anomaly data for all new evolving and sophisticated attacks
is not practical. Training and tuning the machine learning model for anomaly
detection using only a handful of labelled data samples is a pragmatic
approach. Therefore, few-shot weakly supervised anomaly detection is an
encouraging research direction. In this paper, we propose an enhancement to an
existing few-shot weakly-supervised deep learning anomaly detection framework.
This framework incorporates data augmentation, representation learning and
ordinal regression. We then evaluated and showed the performance of our
implemented framework on three benchmark datasets: NSL-KDD, CIC-IDS2018, and
TON_IoT.
