Thwarting Cybersecurity Attacks with Explainable Concept Drift
Ibrahim Shaer Abdallah Shami
Cyber-security attacks pose a significant threat to the operation of
autonomous systems. Particularly impacted are the Heating, Ventilation, and Air
Conditioning (HVAC) systems in smart buildings, which depend on data gathered
by sensors and Machine Learning (ML) models using the captured data. As such,
attacks that alter the readings of these sensors can severely affect the HVAC
system operations impacting residents' comfort and energy reduction goals. Such
attacks may induce changes in the online data distribution being fed to the ML
models, violating the fundamental assumption of similarity in training and
testing data distribution. This leads to a degradation in model prediction
accuracy due to a phenomenon known as Concept Drift (CD) - the alteration in
the relationship between input features and the target variable. Addressing CD
requires identifying the source of drift to apply targeted mitigation
strategies, a process termed drift explanation. This paper proposes a Feature
Drift Explanation (FDE) module to identify the drifting features. FDE utilizes
an Auto-encoder (AE) that reconstructs the activation of the first layer of the
regression Deep Learning (DL) model and finds their latent representations.
When a drift is detected, each feature of the drifting data is replaced by its
representative counterpart from the training data. The Minkowski distance is
then used to measure the divergence between the altered drifting data and the
original training data. The results show that FDE successfully identifies 85.77
% of drifting features and showcases its utility in the DL adaptation method
under the CD phenomenon. As a result, the FDE method is an effective strategy
for identifying drifting features towards thwarting cyber-security attacks.