Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems. Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems. In this study, we propose a hybrid model, BertGCN, that integrates BERT-based contextual embedding with Graph Convolutional Networks (GCNs) to identify anomalies in raw system logs, thereby eliminating the need for log parsing. The BERT module captures semantic representations of log messages, while the GCN models the structural relationships among log entries through a text-based graph. This combination enables BertGCN to capture both the contextual and semantic characteristics of log data. BertGCN showed excellent performance on the HDFS and BGL datasets, demonstrating its effectiveness and resilience in detecting anomalies. Compared to multiple baselines, our proposed BertGCN showed improved precision, recall, and F1 scores.

The surge in smishing attacks underscores the urgent need for robust, real-time detection systems powered by advanced deep learning models. This paper introduces PhishNet, a novel ensemble learning framework that integrates transformer-based models (RoBERTa) and large language models (LLMs) (GPT-OSS 120B, LLaMA3.3 70B, and Qwen3 32B) to enhance smishing detection performance significantly. To mitigate class imbalance, we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques. Our system employs a dual-layer voting mechanism: weighted majority voting among LLMs and a final ensemble vote to classify messages as ham, spam, or smishing. Experimental results show an average accuracy improvement from 96% to 98.5% compared to the best standalone transformer, and from 93% to 98.5% when compared to LLMs across datasets. Furthermore, we present a real-time, user-friendly application to operationalize our detection model for practical use. PhishNet demonstrates superior scalability, usability, and detection accuracy, filling critical gaps in current smishing detection methodologies.