Industrial Control Systems (ICS) and Modbus-enabled networks are facing escalating threats from sophisticated cyber-attacks, while current Intrusion Detection Systems (IDS) struggle to identify intricate and adaptive attacks. This study envisions an ensemble learning-based IDS for Modbus-enabled industrial networks using a real-like Modbus 2023 dataset for industrial networks. The proposed IDS combines four base classifiers, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and Adaptive Boosting (AdaBoost), using the stack ensemble framework, where Logistic Regression acts as the meta-classifier. Preprocessing involved PCAP capture and attack log synchronization, feature normalization, and one-hot encoding for balanced and accurate model training. Experimental evaluation demonstrated that the ensemble model has a 99.78% detection accuracy while outperforming the base individual models in terms of precision, recall, and F1-score. The results indicate the efficiency of ensemble learning for enhanced accuracy detection and false-positive reduction for Modbus networks. Future research will consider real-time testing, feature elimination, and explainable AI for higher operational deployment and scalability. 

Rapid progress in generative artificial intelligence (AI) technologies has brought forth stupendous challenges in differentiating AI-written text from human text. The Naturalness Score, a composite measure that considers lexical diversity, syntactic complexity, sentiment variability, and grammatical faults, is a new idea that emerged from this study. The Naturalness score is part of a larger machine learning framework, although it does have an individual classifier called the Naturalness-Based Logistic Regression Classifier or NLRC. The NLRC model was analyzed against a large, diverse corpus of nearly 45,000 text samples, most of which were student essays, articles, and web-scraped content. The proposed model outperformed all existing baseline models with an accuracy of 96.41%, precision of 0.98, recall of 0.95, and F1 score of 0.96. The high areas under the receiver operating characteristic curve (AUC=1.00) and precision-recall curve (AUC-PR) also indicate the effectiveness of the model in differentiating AI generated from human-written text. The proposed approach offers several advantages including increased detection accuracy, resilience against AI-generated content, cross-domain applicability, and interpretability. The research has implications for applying such models in schools, although it also calls for future research on the implications of the rapidly changing landscape of AI-generated content which it states. It emphasizes the importance of these findings in developing robust and adaptive detection systems to ensure the integrity of academic assessments, thereby preventing the misuse of AI tools.