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.

Next-generation 6G networks seek to provide ultra-reliable and low-latency communications, necessitating network designs that are intelligent and adaptable. Network slicing has developed as an effective option for resource separation and service-level differentiation inside virtualized infrastructures. Nonetheless, sustaining elevated Quality of Service (QoS) in dynamic, resource-limited systems poses significant hurdles. This study introduces an innovative packet-based proactive end-to-end (ETE) resource management system that facilitates network slicing with improved resilience and proactivity. To get around the drawbacks of conventional reactive systems, we develop a cost-efficient slice provisioning architecture that takes into account limits on radio, processing, and transmission resources. The optimization issue is non-convex, NP-hard, and requires online resolution in a dynamic setting. We offer a hybrid solution that integrates an advanced Deep Reinforcement Learning (DRL) methodology with an Improved Manta-Ray Foraging Optimization (ImpMRFO) algorithm. The ImpMRFO utilizes Chebyshev chaotic mapping for the formation of a varied starting population and incorporates Lévy flight-based stochastic movement to avert premature convergence, hence facilitating improved exploration-exploitation trade-offs. The DRL model perpetually acquires optimum provisioning strategies via agent-environment interactions, whereas the ImpMRFO enhances policy performance for effective slice provisioning. The solution, developed in Python, is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements. The DRL model perpetually acquires optimum provisioning methods via agent-environment interactions, while the ImpMRFO enhances policy performance for effective slice provisioning. The solution, developed in Python, is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements. Experimental findings reveal that the proactive ETE system outperforms DRL models and non-resilient provisioning techniques. Our technique increases PSSRr, decreases average latency, and optimizes resource use. These results demonstrate that the hybrid architecture for robust, real-time, and scalable slice management in future 6G networks is feasible.