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.

提出一种特征漂移解释（FDE）模块来识别漂移特征，该模块通过自动编码器（AE）重构回归深度学习模型的第一层激活，并找到它们的潜在表示。结果表明，FDE 成功识别了 85.77％的漂移特征，并展示了其在概念漂移现象下的 DL 适应方法中的实用性，从而成为识别漂移特征以阻止网络安全攻击的有效策略。