Intelligent Time Series Anomaly Detection in IoT Using Feature Extraction and Hybrid Classification
Keywords:
Denial of Service; Internet of Things; Cybersecurity; Artificial Butterfly Optimization; Hybrid CapsNet; Anomaly Detection.Abstract
IoT cyberattacks are becoming more frequent and complicated, threatening individuals and organizations. IoT networks are vulnerable to internal and external cyberattacks because to their openness and self-configuration. DoS attacks are particularly destructive, stopping
genuine users from accessing key services. Traditional anomaly detection approaches fail to identify complex temporal correlations and are inaccurate and not robust.
References
hang, C., & Ding, W. (2018). Anomaly Detection in Time Series Using GANs. Proceedings of the 2018 SIAM International Conference on Data Mining, 1-9.
u, G., Shen, W., & Wang, X. (2019). Research on Anomaly Detection of Time Series Data Based on LSTM. IEEE Access, 7, 163776-163786.
i, D., Chen, D., Jin, B., Shi, L., Goh, J., & Ng, S. K. (2019). MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks. International Conference on Artificial Neural Networks, 703-716
