@Article{cmes.2025.070348,
AUTHOR = {Binu Sudhakaran Pillai, Raghavendra Kulkarni, Venkata Satya Suresh kumar Kondeti, Surendran Rajendran},
TITLE = {Wavelet Transform-Based Bayesian Inference Learning with Conditional Variational Autoencoder for Mitigating Injection Attack in 6G Edge Network},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {145},
YEAR = {2025},
NUMBER = {1},
PAGES = {1141--1166},
URL = {http://www.techscience.com/CMES/v145n1/64340},
ISSN = {1526-1506},
ABSTRACT = {Future 6G communications will open up opportunities for innovative applications, including Cyber-Physical Systems, edge computing, supporting Industry 5.0, and digital agriculture. While automation is creating efficiencies, it can also create new cyber threats, such as vulnerabilities in trust and malicious node injection. Denial-of-Service (DoS) attacks can stop many forms of operations by overwhelming networks and systems with data noise. Current anomaly detection methods require extensive software changes and only detect static threats. Data collection is important for being accurate, but it is often a slow, tedious, and sometimes inefficient process. This paper proposes a new wavelet transform assisted Bayesian deep learning based probabilistic (WT-BDLP) approach to mitigate malicious data injection attacks in 6G edge networks. The proposed approach combines outlier detection based on a Bayesian learning conditional variational autoencoder (Bay-LCVariAE) and traffic pattern analysis based on continuous wavelet transform (CWT). The Bay-LCVariAE framework allows for probabilistic modelling of generative features to facilitate capturing how features of interest change over time, spatially, and for recognition of anomalies. Similarly, CWT allows emphasizing the multi-resolution spectral analysis and permits temporally relevant frequency pattern recognition. Experimental testing showed that the flexibility of the Bayesian probabilistic framework offers a vast improvement in anomaly detection accuracy over existing methods, with a maximum accuracy of 98.21% recognizing anomalies.},
DOI = {10.32604/cmes.2025.070348}
}