  Autonomous aerial surveillance using drone feed is an interesting and
challenging research domain. To ensure safety from intruders and potential
objects posing threats to the zone being protected, it is crucial to be able to
distinguish between normal and abnormal states in real-time. Additionally, we
also need to consider any device malfunction. However, the inherent uncertainty
embedded within the type and level of abnormality makes supervised techniques
less suitable since the adversary may present a unique anomaly for intrusion.
As a result, an unsupervised method for anomaly detection is preferable taking
the unpredictable nature of attacks into account. Again in our case, the
autonomous drone provides heterogeneous data streams consisting of images and
other analog or digital sensor data, all of which can play a role in anomaly
detection if they are ensembled synergistically. To that end, an ensemble
detection mechanism is proposed here which estimates the degree of abnormality
of analyzing the real-time image and IMU (Inertial Measurement Unit) sensor
data in an unsupervised manner. First, we have implemented a Convolutional
Neural Network (CNN) regression block, named AngleNet to estimate the angle
between a reference image and current test image, which provides us with a
measure of the anomaly of the device. Moreover, the IMU data are used in
autoencoders to predict abnormality. Finally, the results from these two
pipelines are ensembled to estimate the final degree of abnormality.
Furthermore, we have applied adversarial attack to test the robustness and
security of the proposed approach and integrated defense mechanism. The
proposed method performs satisfactorily on the IEEE SP Cup-2020 dataset with an
accuracy of 97.8%. Additionally, we have also tested this approach on an
in-house dataset to validate its robustness.
