Adaptive deep learning models for efficient multivariate anomaly detection in IoT infrastructures

Adapting machine learning-based systems to dynamic environments poses significant challenges due to their diverse and rapidly changing nature. Traditional Deep Neural Network (DNN) algorithms often struggle to cope effectively with such variations. This paper presents a novel evolutionary algorithm named Double Evaluation Genetic Evolution (DEGE), specifically tailored to evolve DNNs within dynamic contexts. DEGE represents a pioneering approach in evolutionary computing, focusing on the adaptive evolution of DNN structures across generations. This adaptability plays a crucial role in enabling DNNs to seamlessly adjust to evolving environmental conditions and complexities. To evaluate the efficacy of DEGE, we apply it to the domain of anomaly detection, rigorously testing the adapted DNNs within this specific context. Furthermore, we conduct comparative analyses between DEGE and established optimization methods using standard metrics to elucidate its advantages. Our findings shed light on DEGE’s effectiveness in addressing the challenges posed by dynamic environments, indicating its potential to revolutionize DNN optimization. As a practical application, we integrate DEGE-based DNNs into an IoT anomaly detection system to assess the overall impact of DEGE on anomaly detection performance. Our experiments demonstrate the efficiency of DEGE across 10 generations, showcasing its high adaptability to the dynamism inherent in IoT infrastructures. The proposed DEGE-based anomaly detection system processes highly dynamic environments within IoT infrastructure and classifies/predicts different types of anomalies efficiently with 99% detection accuracy across multiple benchmark and live experiment datasets. Solving the problem of multiclassification in dynamic abnormality detection, the proposed DEGE-based anomaly detection system was highly adaptable to the environment across generations, reaching the optimal DNN structure that delivers the best accuracy, and precision, with minimum loss value.