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1. Malware Detection Using Machine Learning: A Comparative Analysis

   Mohammed, Sameeruddin; Zhang, Fan; Brishti, Faria; Chen, Baiyun Chen; Wu, Fan (July 2025, IARIA Board and IARIA Press)

   To address the growing challenges posed by Cyber threats, anti-malware organizations have increasingly turned to Machine Learning (ML). In recent years, machine learning algorithms have become indispensable for solving complex classification problems, outperforming traditional statistical methods by capturing intricate patterns in high dimensional data. However, selecting the optimal model requires rigorous evaluation in multiple performance metrics while ensuring stability across different data splits. In this study, we conducted a comprehensive assessment of eight machine learning algorithms. Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes, Light Gradient Boosting Machine (LightGBM), Decision Tree (DT), and k-Nearest Neighbors (KNN) using stratified 5-fold cross-validation. Our results reveal that RF, LightGBM, DT, and KNN achieve exceptional performance, with identical near-perfect scores in accuracy (0.9918), precision (0.9920), recall (0.9918), F1 score (0.9918) and Area Under the Receiver Operation Characteristic Curve (AUC-ROC) (0.9998), along with remarkably low variance (10−6 to 10−8), demonstrating unparalleled robustness. The study highlights the superiority of tree-based ensembles and KNN in achieving high predictive power and stability, whereas classical algorithms such as logistic regression and naive Bayes lag. Despite XGBoost’s reputation, its performance here is eclipsed by simpler tree-based methods. Our analysis underscores the importance of considering variance when evaluating model selection, particularly for critical applications where stability is paramount, and provides actionable insights for practitioners seeking reliable, high-accuracy classifiers. 

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2. Malware Detection Using Machine Learning: A Comparative Analysis

   Mohammed, Sameeruddin; Zhang, Fan; Brishti, Faria; Chen, Baiyun; Wu, Fan (August 2025, Courtesy of IARIA Board and IARIA Press)

   To address the growing challenges posed by Cyber threats, anti-malware organizations have increasingly turned to Machine Learning (ML). In recent years, machine learning algorithms have become indispensable for solving complex classification problems, outperforming traditional statistical methods by capturing intricate patterns in high dimensional data. However, selecting the optimal model requires rigorous evaluation in multiple performance metrics while ensuring stability across different data splits. In this study, we conducted a comprehensive assessment of eight machine learning algorithms. Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes, Light Gradient Boosting Machine (LightGBM), Decision Tree (DT), and k-Nearest Neighbors (KNN) using stratified 5-fold cross-validation. Our results reveal that RF, LightGBM, DT, and KNN achieve exceptional performance, with identical near-perfect scores in accuracy (0.9918), precision (0.9920), recall (0.9918), F1 score (0.9918) and Area Under the Receiver Operation Characteristic Curve (AUC-ROC) (0.9998), along with remarkably low variance (10−6 to 10−8), demonstrating unparalleled robustness. The study highlights the superiority of tree-based ensembles and KNN in achieving high predictive power and stability, whereas classical algorithms such as logistic regression and naive Bayes lag. Despite XGBoost’s reputation, its performance here is eclipsed by simpler tree-based methods. Our analysis underscores the importance of considering variance when evaluating model selection, particularly for critical applications where stability is paramount, and provides actionable insights for practitioners seeking reliable, high-accuracy classifiers. 

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3. Enhanced trust in IoT environments: utilizing perfect Bayesian equilibrium, exponential smoothing, and machine learning

   https://doi.org/10.1007/s10586-024-05050-w  

   Namdari, Himan; Morales_Avalos, Victor; Alshehri, Amal; Tunc, Cihan; Dantu, Ram (October 2025, Cluster Computing)

   In the Internet of Things (IoT) context, traditional static security models such as role-based or attribute-based access controls prove inadequate due to IoT connections’ dynamic and opportunistic nature. To address these challenges, we propose a trust evaluation methodology by incorporating game theory with Perfect Bayesian Equilibrium (PBE) and a smoothing function (SF). Our enhanced PBE-ES model dynamically adjusts the trustworthiness of IoT nodes based on observation and Bayesian updates, incorporating node power, distance, and latency to improve trust evaluation accuracy. Our experimental study demonstrates significant improvements in detecting malicious nodes in a network, achieving up to 99% accuracy across various scenarios with different noise levels and network sizes. Additionally, we used the PBE-SF algorithm to generate synthetic data for training classification models like Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Neural Networks (NN). Random Forest (RF) performed best, achieving an RMSE of 0.01 in evaluating the utility of IoT nodes, which improves the accuracy of detecting malicious nodes. Our results demonstrate the PBE-ES model’s effectiveness in enhancing trust evaluation and addressing security challenges in IoT ecosystems. 

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4. Leveraging Cross-Domain Transfer Learning for Enhanced Multi-Protocol Network Intrusion Detection

   https://doi.org/10.3390/computers15060376  

   Oladejo, Oluwaseyi; Ahmed, Ahmed Abdelmoamen (June 2026, Computers)

   The exponential growth of cyber threats in modern digital infrastructure demands advanced detection systems that adapt to evolving attack patterns. Traditional cybersecurity approaches struggle with dynamic threats, requiring extensive labeled datasets and retraining for each new category. This paper presents a comprehensive transfer learning framework for cybersecurity threat detection, leveraging the CICIoMT dataset as a benchmark to enhance detection capabilities across heterogeneous cybersecurity environments. We propose a machine learning (ML)-enabled framework that employs systematic feature alignment, hybrid class balancing, and multi-algorithm evaluation using machine learning models, including Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting, and XGBoost. The proposed approach addresses the critical challenges of data scarcity and domain heterogeneity in cybersecurity by enhancing feature engineering with cybersecurity-specific features, statistical aggregations, and PCA embeddings. Extensive experimental evaluation across two target datasets (CICIoT and IoT-23) demonstrates both the exceptional successes and critical limitations of cross-domain transfer learning in cybersecurity. The framework achieved outstanding performance on domain-compatible datasets, with RF reaching 99.0% accuracy on CICIoT, Gradient Boosting achieving 98.9%, and XGBoost delivering 98.4%, demonstrating exceptional knowledge transfer from medical IoT to smart home IoT environments. However, transfer learning to IoT-23 was unsuccessful (50% accuracy, equivalent to random guessing), revealing that feature domain difference, where identical attack labels encode fundamentally different behavioral patterns, prevents effective knowledge transfer despite nominal class overlap. This research makes significant advances in adaptive cybersecurity systems by providing a rigorous evaluation of both the successes and limitations of transfer learning. This work demonstrates that ensemble methods (RF, XGBoost, and Gradient Boosting) achieve superior cross-domain performance compared with neural networks on compatible domains, while also revealing fundamental challenges when the source and target domains differ in their feature spaces. 

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5. Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice

   https://doi.org/10.3390/s20072027  

   Vishwanath, Manoj; Jafarlou, Salar; Shin, Ikhwan; Lim, Miranda M.; Dutt, Nikil; Rahmani, Amir M.; Cao, Hung (April 2020, Sensors)

   null (Ed.)

   Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today’s world, robust detection of TBI has become more significant than ever. In this work, we investigate several machine learning approaches to assess their performance in classifying electroencephalogram (EEG) data of TBI in a mouse model. Algorithms such as decision trees (DT), random forest (RF), neural network (NN), support vector machine (SVM), K-nearest neighbors (KNN) and convolutional neural network (CNN) were analyzed based on their performance to classify mild TBI (mTBI) data from those of the control group in wake stages for different epoch lengths. Average power in different frequency sub-bands and alpha:theta power ratio in EEG were used as input features for machine learning approaches. Results in this mouse model were promising, suggesting similar approaches may be applicable to detect TBI in humans in practical scenarios. 

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