  As the technological advancement and capabilities of automated systems have
increased drastically, the usage of unmanned aerial vehicles for performing
human-dependent tasks without human indulgence has also spiked. Since unmanned
aerial vehicles are heavily dependent on Information and Communication
Technology, they are highly prone to cyber-attacks. With time more advanced and
new attacks are being developed and employed. However, the current Intrusion
detection system lacks detection and classification of new and unknown attacks.
Therefore, for having an autonomous and reliable operation of unmanned aerial
vehicles, more robust and automated cyber detection and protection schemes are
needed. To address this, we have proposed an autonomous self-incremental
learning architecture, capable of detecting known and unknown cyber-attacks on
its own without any human interference. In our approach, we have combined
signature-based detection along with anomaly detection in such a way that the
signature-based detector autonomously updates its attack classes with the help
of an anomaly detector. To achieve this, we have implemented an incremental
learning approach, updating our model to incorporate new classes without
forgetting the old ones. To validate the applicability and effectiveness of our
proposed architecture, we have implemented it in a trial scenario and then
compared it with the traditional offline learning approach. Moreover, our
anomaly-based detector has achieved a 100% detection rate for attacks.
