Abstract Internet of Things (IoT) networks are increasingly targeted by advanced botnet attacks, posing serious risks to security and system stability. However, many existing intrusion detection systems (IDS) struggle to balance detection accuracy, real-time efficiency, and interpretability—especially in resource-constrained environments. In this paper, we introduce GELAX, a novel detection framework that combines Graph Neural Networks (GNNs), Dynamic Graph Pruning, and Anchored Explainable AI to address these challenges. GELAX dynamically simplifies graph structures to reduce computational load, while still capturing meaningful device interactions. Its integrated explainability component highlights key features driving detection decisions with minimal overhead, supporting analyst trust and model transparency. Evaluations on two benchmark datasets—N-BaIoT and UNSW Bot-IoT—demonstrate that GELAX achieves high detection accuracy (95.9% and 96.9%), reduces CPU and memory usage by over 45%, and improves explanation alignment (faithfulness) by 22.5%. These results highlight GELAX as a robust, efficient, and interpretable solution for securing modern IoT systems.

Ambient temperature estimation plays a vital role in various domains, including environmental monitoring, smart cities, and energy‐efficient systems. Traditional sensor‐based methods suffer from high deployment costs and limited scalability, while centralized machine learning approaches raise significant privacy concerns. Recent crowdsensing‐based systems leverage smartphone sensor data but face two major challenges: user privacy protection and unreliable participant contributions. To address these issues, this study proposes a privacy‐preserving federated learning framework that integrates differential privacy with the exponential mechanism to ensure user anonymity during decentralized training. Furthermore, a novel utility‐based filtering mechanism is employed to detect and exclude low‐quality or adversarial data, enhancing model reliability. Advanced deep learning models, including long short–term memory (LSTM) and ensemble learning, are integrated to improve prediction accuracy in temporal and noisy environments. The dataset consists of mobile sensor data, including battery temperature, CPU usage, and environmental temperature measurements, collected from participants in real‐world settings. The framework achieved high accuracy, with the LSTM model outperforming others (federated MAE: 1.292, MAPE: 0.0511) and performing comparably to centralized models (MAE: 1.179, MAPE: 0.0462) while ensuring privacy. The proposed framework showed comparable performance to centralized models while ensuring strong privacy guarantees. The integration of privacy‐preserving mechanisms and robust data filtering enables a scalable and reliable solution suitable for practical deployment in large‐scale ambient temperature estimation tasks.

REST Web Services is a lightweight, maintainable, and scalable service accelerating client application development. The antipatterns of these services are inadequate and counter-productive design solutions. They have caused many qualitative problems in the maintenance and evolution of REST web services. This paper proposes an automated approach toward antipattern detection of the REST web services using Genetic Programming (GP). Three sets of generic, REST-specific and code-level metrics are considered. Twelve types of antipatterns are examined. The results are compared with the manual rule-based approach. The statistical analysis indicates that the proposed method has an average precision and recall scores of 98% (95% CI, 92.8% to 100%) and 82% (95% CI, 79.3% to 84.7%) and effectively detects REST antipatterns.