As today’s cybersecurity environment is becoming increasingly complex, it is crucial to analyse threats quickly and effectively. A delayed response or lack of foresight can lead to data loss, reputational damage, and operational disruptions. Therefore, developing methods that can rapidly extract valuable threat intelligence is a critical need to strengthen defence strategies and minimise potential damage. This paper presents an innovative approach that integrates knowledge graphs and a fine-tuned BERT-based model to analyse cyber threat intelligence (CTI) data. The proposed system extracts cyber entities such as threat actors, malware, campaigns, and targets from unstructured threat reports and establishes their relationships using an ontology-driven framework. A named entity recognition dataset was created and a BERT-based model was trained. To address the class imbalance, oversampling and a focal loss function were applied, achieving an F1 score of 96%. The extracted entities and relationships were visualised and analysed using knowledge graphs, enabling the advanced threat analysis and prediction of potential attack targets. This approach enhances cyber-attack prediction and prevention through knowledge graphs.

Large Language Model (LLM)-based agents automate complex tasks by integrating external tools such as web browsers, e-mail clients, file readers, and APIs, but this same integration exposes them to indirect prompt injection (IPI) attacks, in which malicious instructions hidden in tool content hijack the agent. A central but often overlooked question is how defending against such attacks affects the LLM and its own task performance and computational efficiency. In this study, we design a comprehensive testbed and a layered defense, Spotlight-Guard, that combines spotlighting-based input isolation, an LLM detection-and-quarantine pipeline, and instruction integrity based on a Hash-based Message Authentication Code (HMAC) into a single framework, and we evaluate it jointly along two axes: security and LLM performance. Experiments on locally hosted 7B-class open-weight models (Qwen-2.5-7B, Mistral-7B, and DeepSeek-Coder) use Attack Success Rate (ASR) for security and benign-task success rate together with confusion-matrix-based metrics (precision, recall, and F1) for task performance, all with bootstrap 95% confidence intervals. Across a stratified, fixed-seed benchmark of 250 adversarial and 250 benign cases per configuration, the full system reduces the ASR from 36.0% to 17.2% while preserving a 97.2% benign-task success rate and raising the detection F1 from 0.749 to 0.892, demonstrating that strong protection need not degrade the model’s task performance. A component ablation isolates each layer’s contribution, an adaptive-attack evaluation confirms a low ASR (6.7%) under attacks crafted to target the pipeline, and an analysis of computational cost (model invocations per request) quantifies the efficiency overhead, characterizing the security–performance trade-off of layered defenses on open-weight LLMs.

The rapid development of the IoT (Internet of Things) ecosystem leads to the creation of big data environments that require real-time analysis. In this comprehensive data ecosystem, anomaly detection and data security emerge as critical requirements. This paper presents a comprehensive approach that integrates a deep learning model developed for anomaly detection in IoT network traffic and a blockchain-based data storage structure designed to ensure data integrity. In the research, network traffic data of a sample device from the N-BaIoT dataset is used. The developed deep learning model was able to classify attack and normal traffic patterns with high accuracy. Data security is ensured with Fernet encryption algorithm, while data integrity is protected using blockchain technology. Experimental results show that the proposed system achieves significant performance metrics in terms of both anomaly detection accuracy and data security verification. The proposed framework contributes to the development of more secure and reliable IoT systems by providing an innovative solution to anomaly detection and data security challenges in IoT environments.

Accurate prediction of pest density and population levels in almond production is of great importance for crop yield, the prevention of quality losses, and the effectiveness of integrated pest management (IPM) strategies. Complex feature interactions in agricultural field data can affect both the predictive performance and interpretability of machine learning models. In this study, an explainable hybrid machine learning framework combining Mutated Particle Swarm Optimization (MPSO), XGBoost classification, and rule extraction was developed to classify pest insect density levels. The proposed approach was evaluated using two complementary validation settings. First, it was compared with 16 classification algorithms, including ensemble, deep learning, and rule-based methods, under stratified 5-fold cross-validation, achieving a mean accuracy of 79.63% and a mean Weighted F1-Score of 79.79%. In addition, nested stratified 5-fold cross-validation was employed to separate MPSO-based hyperparameter optimization from outer-fold performance evaluation. Under this more rigorous evaluation, the proposed approach achieved a mean accuracy of 79.27 ± 1.66% and a mean Weighted F1-Score of 79.29 ± 1.38%. Furthermore, interpretable IF–THEN decision rules were derived from the trained XGBoost decision structures to provide a transparent representation of the feature–threshold combinations associated with the classification decisions.