Abstract:
"
With society’s increasing dependency on computer networks for collaboration,
information, profit and recreation, there is also a greater need for maintaining the
security and safety of our communication channels.
Reducing malicious network traffic starts with accurate identification of network
intrusions which is usually achieved using attack signatures or detecting anomalies in
traffic patterns. Research into finding better attack detection systems have extended to
artificial intelligence techniques of creating suitable algorithmic models based on
machine learning (ML) and deep learning (DL).
This research set out to develop a suitable AI model to detect novel network intrusions
with improved accuracy and a reduced false alarm rate using a hybrid of ML & Dl
algorithms, using a small number of network features.
Compared to existing systems reported in literature, the system developed performs
equally or better in detecting network intrusions and reducing the false alarm rate
significantly. The detection module was developed with DNN and RNN with optimal
feature selection process.
A prototype system was developed with an easy user-interface to a) demonstrate the
capabilities of the developed model and b) to enable testing of the prototype against
new attack traffic. The prototype may be re-purposed into an open-source network
intrusion detection system with the integration of an appropriate in-line network feature
extractor.
The developed detection engine can be improved further by fine-tuning the model using
newer network attack datasets. This would enable the detection of modern variants of
legacy attacks and would increase the chances of novel attack detection in future"
