Md. Alamgir Hossain

Abdullah al Nomaan Nafi, Md. Alamgir Hossain, Rakib Hossain Rifat et al.

Data scarcity in medical imaging poses significant challenges due to privacy concerns. Diffusion models, a recent generative modeling technique, offer a potential solution by generating synthetic and realistic data. However, questions remain about the performance of convolutional neural network (CNN) models on original and synthetic datasets. If diffusion-generated samples can help CNN models perform comparably to those trained on original datasets, reliance on patient-specific data for training CNNs might be reduced. In this study, we investigated the effectiveness of diffusion models for generating synthetic medical images to train CNNs in three domains: Brain Tumor MRI, Acute Lymphoblastic Leukemia (ALL), and SARS-CoV-2 CT scans. A diffusion model was trained to generate synthetic datasets for each domain. Pre-trained CNN architectures were then trained on these synthetic datasets and evaluated on unseen real data. All three datasets achieved promising classification performance using CNNs trained on synthetic data. Local Interpretable Model-Agnostic Explanations (LIME) analysis revealed that the models focused on relevant image features for classification. This study demonstrates the potential of diffusion models to generate synthetic medical images for training CNNs in medical image analysis.

Waqas Ishtiaq, Ashrafun Zannat, A. H. M. Shahariar Parvez et al.

The rapid expansion of the Internet of Things (IoT) has revolutionized modern industries by enabling smart automation and real time connectivity. However, this evolution has also introduced complex cybersecurity challenges due to the heterogeneous, resource constrained, and distributed nature of these environments. To address these challenges, this research presents CST AFNet, a novel dual attention based deep learning framework specifically designed for robust intrusion detection in IoT networks. The model integrates multi scale Convolutional Neural Networks (CNNs) for spatial feature extraction, Bidirectional Gated Recurrent Units (BiGRUs) for capturing temporal dependencies, and a dual attention mechanism, channel and temporal attention, to enhance focus on critical patterns in the data. The proposed method was trained and evaluated on the Edge IIoTset dataset, a comprehensive and realistic benchmark containing more than 2.2 million labeled instances spanning 15 attack types and benign traffic, collected from a seven layer industrial testbed. Our proposed model achieves outstanding accuracy for both 15 attack types and benign traffic. CST AFNet achieves 99.97 percent accuracy. Moreover, this model demonstrates exceptional performance with macro averaged precision, recall, and F1 score all above 99.3 percent. Experimental results show that CST AFNet achieves superior detection accuracy, significantly outperforming traditional deep learning models. The findings confirm that CST AFNet is a powerful and scalable solution for real time cyber threat detection in complex IoT and IIoT environments, paving the way for more secure, intelligent, and adaptive cyber physical systems.

Hikmat A. M. Abdeljaber, Md. Alamgir Hossain, Sultan Ahmad et al.

The rapid expansion of Internet of Things (IoT) devices has transformed industries and daily life by enabling widespread connectivity and data exchange. However, this increased interconnection has introduced serious security vulnerabilities, making IoT systems more exposed to sophisticated cyber attacks. This study presents a novel ensemble learning architecture designed to improve IoT attack detection. The proposed approach applies advanced machine learning techniques, specifically the Extra Trees Classifier, along with thorough preprocessing and hyperparameter optimization. It is evaluated on several benchmark datasets including CICIoT2023, IoTID20, BotNeTIoT L01, ToN IoT, N BaIoT, and BoT IoT. The results show excellent performance, achieving high recall, accuracy, and precision with very low error rates. These outcomes demonstrate the model efficiency and superiority compared to existing approaches, providing an effective and scalable method for securing IoT environments. This research establishes a solid foundation for future progress in protecting connected devices from evolving cyber threats.

Tarun Kumar Biswas, Ashrafun Zannat, Waqas Ishtiaq et al.

The growing integration of drones across commercial, industrial, and civilian domains has introduced significant cybersecurity challenges, particularly due to the susceptibility of drone networks to a wide range of cyberattacks. Existing intrusion detection mechanisms often lack the adaptability, efficiency, and generalizability required for the dynamic and resource constrained environments in which drones operate. This paper proposes TSLT-Net, a novel lightweight and unified Temporal Spatial Transformer based intrusion detection system tailored specifically for drone networks. By leveraging self attention mechanisms, TSLT-Net effectively models both temporal patterns and spatial dependencies in network traffic, enabling accurate detection of diverse intrusion types. The framework includes a streamlined preprocessing pipeline and supports both multiclass attack classification and binary anomaly detection within a single architecture. Extensive experiments conducted on the ISOT Drone Anomaly Detection Dataset, consisting of more than 2.3 million labeled records, demonstrate the superior performance of TSLT-Net with 99.99 percent accuracy in multiclass detection and 100 percent in binary anomaly detection, while maintaining a minimal memory footprint of only 0.04 MB and 9722 trainable parameters. These results establish TSLT-Net as an effective and scalable solution for real time drone cybersecurity, particularly suitable for deployment on edge devices in mission critical UAV systems.

Md. Alamgir Hossain, Waqas Ishtiaq, Md. Samiul Islam

The growing integration of drones into civilian, commercial, and defense sectors introduces significant cybersecurity concerns, particularly with the increased risk of network-based intrusions targeting drone communication protocols. Detecting and classifying these intrusions is inherently challenging due to the dynamic nature of drone traffic and the presence of multiple sophisticated attack vectors such as spoofing, injection, replay, and man-in-the-middle (MITM) attacks. This research aims to develop a robust and interpretable intrusion detection framework tailored for drone networks, with a focus on handling multi-class classification and model explainability. We present a comparative analysis of ensemble-based machine learning models, namely Random Forest, Extra Trees, AdaBoost, CatBoost, and XGBoost, trained on a labeled dataset comprising benign traffic and nine distinct intrusion types. Comprehensive data preprocessing was performed, including missing value imputation, scaling, and categorical encoding, followed by model training and extensive evaluation using metrics such as macro F1-score, ROC AUC, Matthews Correlation Coefficient, and Log Loss. Random Forest achieved the highest performance with a macro F1-score of 0.9998 and ROC AUC of 1.0000. To validate the superiority of the models, statistical tests, including Friedmans test, the Wilcoxon signed-rank test with Holm correction, and bootstrapped confidence intervals, were applied. Furthermore, explainable AI methods, SHAP and LIME, were integrated to interpret both global and local feature importance, enhancing model transparency and decision trustworthiness. The proposed approach not only delivers near-perfect accuracy but also ensures interpretability, making it highly suitable for real-time and safety-critical drone operations.