Background: Breast cancer is one of the leading causes of death in women, making early detection through mammography crucial for improving survival rates. However, human interpretation of mammograms is often prone to diagnostic errors. This study addresses the challenge of improving the accuracy of breast cancer detection by leveraging advanced machine learning techniques. Methods: We propose an extended ensemble deep learning model that integrates three state-of-the-art convolutional neural network (CNN) architectures: VGG16, DenseNet121, and InceptionV3. The model utilizes multi-scale feature extraction to enhance the detection of both benign and malignant masses in mammograms. This ensemble approach is evaluated on two benchmark datasets: INbreast and CBIS-DDSM. Results: The proposed ensemble model achieved significant performance improvements. On the INbreast dataset, the ensemble model attained an accuracy of 90.1%, recall of 88.3%, and an F1-score of 89.1%. For the CBIS-DDSM dataset, the model reached 89.5% accuracy and 90.2% specificity. The ensemble method outperformed each individual CNN model, reducing both false positives and false negatives, thereby providing more reliable diagnostic results. Conclusions: The ensemble deep learning model demonstrated strong potential as a decision support tool for radiologists, offering more accurate and earlier detection of breast cancer. By leveraging the complementary strengths of multiple CNN architectures, this approach can improve clinical decision making and enhance the accessibility of high-quality breast cancer screening.

The increasing complexity and sophistication of cyber threats underscore the critical need for advanced threat detection mechanisms within Security Operations Centers (SOCs) to effectively mitigate risks and enhance cybersecurity resilience. This study enhances the capabilities of Wazuh, an open-source Security Information and Event Management (SIEM) system, by addressing its primary limitation: high false-positive rates in rule-based detection. We propose a hybrid approach that integrates machine learning (ML) techniques—specifically, Random Forest (RF) and DBSCAN—into Wazuh’s detection pipeline to improve both accuracy and operational efficiency. Experimental results show that RF achieves 97.2% accuracy, while DBSCAN yields 91.06% accuracy with a false-positive rate of 0.0821, significantly improving alert quality. Real-time deployment requirements are rigorously evaluated, with all models maintaining end-to-end processing latencies below 100 milliseconds and 95% of events processed within 500 milliseconds. Scalability testing confirms linear performance up to 500 events per second, with an average processing latency of 45 milliseconds under typical SOC workloads. This integration demonstrates a practical, resource-efficient solution for enhancing real-time threat detection in modern cybersecurity environments.

There is a dearth of academic research literature on the practices and commitments of information security governance in organizations. Despite the existence of referential and standards of the security governance, the research literature remains limited regarding the practices of organizations and, on the other hand, the lack of a strategy and practical model to follow in adopting an effective information security governance. This chapter aims to explore the engagement processes and the practices of organizations involved in a strategy of information security governance via a statistical and econometric analysis of data from a survey of 1000 participants (with a participation rate of 83.67%) from large and medium companies belonging to various industries. Based on the results of the survey regarding practices of information security management and governance, a practical maturity framework for the information security governance and management in organizations is presented.

The escalating sophistication of cyber threats necessitates advanced threat detection in Security Operation Centers (SOCs). This study aims to enhance the capabilities of Wazuh, an open-source Security Information and Event Management (SIEM) system, by addressing its primary limitation: high false positive rates in rule-based detection. We propose integrating machine learning (ML) to improve detection accuracy and operational efficiency. The approach involves training and evaluating ML models—Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression, and Gaussian Naive Bayes—alongside clustering algorithms (DBSCAN, K-means, Isolation Forest) using 10-fold cross-validation. Results demonstrate that RF achieved the highest performance with an accuracy of 0.972, precision of 0.982, recall of 0.975, and F1-Score of 0.978, while DBSCAN excelled in clustering with a 91.06% accuracy and 0.0821 false positive rate. This integration significantly reduced false positives, enhancing alert management and enabling efficient real-time threat detection. The study contributes to cybersecurity by demonstrating that ML integration with Wazuh markedly improves threat detection, reduces operational overhead in SOCs, and establishes a more adaptive security framework.

Backdoor attacks pose a serious threat to small language models (SLMs) because compromised models can behave normally on benign inputs while producing attacker-specified outputs when a hidden trigger is activated. Existing defenses commonly require model retraining, operate only before deployment, rely on input-level signals, or incur excessive inference overhead. This paper presents AEGIS (Activation Evaluation and Guardrail for Inference Security), a non-invasive runtime framework for detecting backdoor-induced anomalies in transformer representations. AEGIS dynamically monitors equidistant internal layers, mean-pools and concatenates their hidden states, and compresses the resulting high-dimensional representation into a 64-dimensional latent space. A hybrid compression mechanism uses zero-shot principal component analysis for predominantly linear text representations and a lightweight autoencoder for nonlinear or multimodal representations. Detection is then performed entirely on the GPU using cosine distance from a centroid calibrated on clean data, without modifying or retraining the protected model. We evaluate AEGIS across Mistral-7B, Qwen2.5-7B, a 4-bit QLoRA-backdoored Qwen2.5-1.5B model, and a BadNets-backdoored Vision Transformer, covering simulated latent and PEFT-style attacks, genuine fine-tuned backdoors, FP16 inference, and 4-bit NF4 quantization. Across the main optimized shield-overhead experiments, AEGIS achieves AUROC values between 0.95 and 1.00, true-positive rates between 86% and 100%, and prefill-stage detection latency between 0.47 and 2.40 ms. In the dedicated T4 deployment stress tests, which measure the full guardrail overhead under edge and real QLoRA settings, latency ranges from 5.20 to 7.77 ms with combined model-plus-guardrail memory between 2.59 and 4.02 GB. AEGIS obtains AUROC = 1.00 and 100% true-positive rate against the genuinely fine-tuned QLoRA backdoor, while achieving AUROC = 0.997 against the fine-tuned visual BadNets attack. Under 4-bit quantization, it retains an AUROC = 0.95 with a 4.02 GB memory footprint. These results show that a compact GPU-native implementation of activation-space monitoring can provide effective, retraining-free backdoor detection for real-time transformer inference.

Backdoor attacks pose a serious threat to small language models (SLMs) because compromised models can behave normally on benign inputs while producing attacker-specified outputs when a hidden trigger is activated. Existing defenses commonly require model retraining, operate only before deployment, rely on input-level signals, or incur excessive inference overhead. This paper presents AEGIS (Activation Evaluation and Guardrail for Inference Security), a non-invasive runtime framework for detecting backdoor-induced anomalies in transformer representations. AEGIS dynamically monitors equidistant internal layers, mean-pools and concatenates their hidden states, and compresses the resulting high-dimensional representation into a 64-dimensional latent space. A hybrid compression mechanism uses zero-shot principal component analysis for predominantly linear text representations and a lightweight autoencoder for nonlinear or multimodal representations. Detection is then performed entirely on the GPU using cosine distance from a centroid calibrated on clean data, without modifying or retraining the protected model. We evaluate AEGIS across Mistral-7B, Qwen2.5-7B, a 4-bit QLoRA-backdoored Qwen2.5-1.5B model, and a BadNets-backdoored Vision Transformer, covering simulated latent and PEFT-style attacks, genuine fine-tuned backdoors, FP16 inference, and 4-bit NF4 quantization. Across seven experimental scenarios, AEGIS achieves AUROC values between 0.95 and 1.00, true-positive rates between 86% and 100%, and detection latency between 0.47 and 2.40 ms. It obtains AUROC = 1.00 and 100% true-positive rate against the genuinely fine-tuned QLoRA backdoor, while achieving AUROC=0.997 against the fine-tuned visual BadNets attack. Under 4-bit quantization, it retains AUROC=0.95 with 1.50 ms latency and a 4.52 GB memory footprint. These results demonstrate that GPU-native latent-space monitoring can provide effective, retraining-free backdoor detection for real-time transformer inference.