ABSTRACT The increasing complexity and interconnectivity of industrial cyber‐physical systems (ICPSs), while enhancing operational security and reliability, have also introduced significant cybersecurity challenges. Software‐defined networking (SDN), a transformative technology for centralized and dynamic resource management, is particularly vulnerable as centralized control planes can become single points of failure. The integration of Digital Twin technology, which creates virtual replicas of physical systems for real‐time monitoring and prediction, further exacerbates security risks. To address these issues, we present TwinSec‐IDS, an advanced intrusion detection framework designed for SDN‐Digital‐Twin‐based ICPS. TwinSec‐IDS provides comprehensive and proactive intrusion detection, thereby enhancing the resilience of industrial networks. This paper introduces an ensemble approach, leveraging hybrid deep learning models—such as Bi‐GRU‐CNN, Bi‐GRU‐LSTM, and Bi‐GRU‐LSTM‐CNN—integrated with ensemble‐based feature selection techniques. The system employs weighted majority voting to combine predictions from multiple models, improving detection accuracy. To ensure optimal feature selection, the framework incorporates explainable AI and multiple filter methods, including mutual information, chi‐square tests, and correlation coefficients, aggregated through a voting mechanism. TwinSec‐IDS demonstrates high accuracy in detecting and categorizing anomalies and effectively responds to potential threats. Extensive evaluations show that TwinSec‐IDS significantly improves the security and resilience of SDN‐Digital‐Twin‐based ICPS, addressing critical cybersecurity concerns and making industrial processes safer and more reliable.

Abstract Cloud computing security is the most critical factor for providers, cloud users, and organizations. The various novel approaches apply host‐based or network‐based methods to increase cloud security performance and detection rate. However, due to the virtual and distributed environment of the cloud, conventional network intrusion detection systems (NIDS) have been unreliable in handling these security attacks. Therefore, we design a methodology that incorporates feature selection and classification using ensemble techniques to provide efficient and accurate intrusion detection to address these problems. This proposed model combines the three most effective feature selection techniques (gain‐ratio, chi‐squared, and information gain) to offer a qualifying result and four top classifiers (SVM, LR, NB, and DT) using enhanced weighted majority voting. Moreover, we proposed an experimental technique using a new dataset called Honeypot. All experiments utilized three datasets: Honeypots, Kyoto, and NSL: KDD. In addition, the results of this experimental study were compared with other approaches and performed the statistical significance analysis. Finally, the results reveal that the proposed intrusion detection based on the Honeypot dataset was better and more efficient than other methods because we have an accuracy of 98.29%, FAR of 0.012%, DR of 97.9%, and AUC = 0.9921.

Abstract Diabetes mellitus is a chronic disorder disease in which a person's body fails to adhere insulin produced by their pancreas or unable to segregate enough insulin due to harmonic imbalance. Diabetic people are suffering from eye disorders like diabetic retinopathy (DR), glaucoma and various diseases such as neuropathy, nephropathy, cardiomyopathy over long intervals. One of the most prevalent diabetic consequence is DR. Detecting the morphological variations in retina is difficult and requires an effective automated detection system. DR can be predicted in earlier stage using tremendous development of deep learning models and image processing techniques. Recently, many research articles have been published in DR diagnosis system. This article shows a comprehensive review of automated diagnostic methods for DR detection and other related eye disorders from several points: Causes for DR, publicly available datasets, image preprocessing, segmentation of various DR lesions, feature optimization, various deep learning models, and open research challenges. The study offers a thorough overview of DR detection techniques, which delivers valuable information for researchers, medical professionals, and DR affected patients.

Summary As Network traffic rises and attacks become more widespread and complicated, we must come across Innovative ways to enrich Intrusion Detection Systems in Cloud Computing. This paper proposes the Ensemble approaches for Network Intrusion Detection and Classification in Cloud. The major aids of the Ensemble Learning to improve the outcome of each Machine Learning Algorithms and to get a robust Classifier. Real Time Malicious Network Streams Samples were collected using Honeynet, which is deployed on cloud environment. We use supervised learning and Unsupervised learning algorithms for classifying the known malicious network streams and unknown malicious streams. Network related attacks can be segregated into four classes, namely, Denial of service (DOS), User to root (U2 R), Remote to local (R2L), and probe, and the vital constraints that must be overcome with the end goal to build efficient Intelligent Intrusion Detection. The motivation behind the proposed work is to enhance the accuracy rate with response time. The outcome obtained from the Ensemble method has better accuracy rate compared to the SVM, Naive Bayes, and Logistic regression method.