Intrusion Detection Systems (IDS) play a major part in protecting security threats and networks from attacks. Due to the rapid development of the internet of things (IoT), more cyber‐attacks are attacking these devices. Various security challenges still occur on IoT devices since most of them have limited security mechanisms. Hence, this paper introduces a combination of linear and non‐linear space transformation models for IDS. Independent component analysis (ICA) is employed for linear transformation to obtain an orthogonal space, and a dual‐phase distance metric learning method (D‐DML) is utilized to obtain an optimal distance metric. The Gaussian radial basis function (GRBF) model is employed for non‐linear transformation. Then these features of linear and non‐linear models are integrated and classified by capsule auto encoder with a hybrid kernel function (HKCAE) which classifies normal and malicious attacks. Guidance of the capuchin search algorithm (CSA) is employed to optimize the HKCAE parameters during prediction attack prediction. The performance of the implemented approach is compared with the other approaches with some measures like precision, accuracy, sensitivity, F‐score, and specificity benchamrked through UNSW‐15 and BoT‐IoT datasets. The accuracy of the developed scheme is 0.9973 and 0.999 on UNSW‐15 and BoT‐IoT datasets, respectively.

Micro, Small and Medium Enterprises (MSME) sector plays a substantial role in the overall economic development and employment generation of a country. The Covid-19 pandemic has impacted adversely, and it is inevitably necessary to consider the influence of the pandemic on MSME, which will assist the policymakers in helping in the repurposing operations of the sector. Because of its size, scale of operations, and availability of financial resources, the MSMEs sector has been one of the most susceptible sectors post-Covid-19. Many academics have explored the constraints to MSMEs' development in the past, but limited research has been done using Total Interpretive Structural Modelling (TISM) technique for the factors impacting MSMEs' repurposing operations during the Covid-19 emergency. This research seeks to "identify," "analyze," and "categorize" the elements impacting MSMEs' repurposing operations during the Covid-19 pandemic. Literature review and experts’ comment from various MSMEs resulted in identification of 7 enablers. The TISM and MICMAC approach was employed in this study. The findings shows that occupational health and safety, logistics, and government rules and regulations are the key factors affecting repurposing operations in MSMEs during the Covid-19 emergency. This research helps the top-executives of MSME to look into the factors affecting repurposing operations in MSMEs during the Covid-19 emergency. This research examines factors affecting repurposing operations in MSMEs during the Covid-19 emergency. It is the first study to analyze the factors affecting the repurposing operations in MSME during the Covid-19 using TISM technique.

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The tremendous growth in online activity and the Internet of Things (IoT) led to an increase in cyberattacks. Malware infiltrated at least one device in almost every household. Various malware detection methods that use shallow or deep IoT techniques were discovered in recent years. Deep learning models with a visualization method are the most commonly and popularly used strategy in most works. This method has the benefit of automatically extracting features, requiring less technical expertise, and using fewer resources during data processing. Training deep learning models that generalize effectively without overfitting is not feasible or appropriate with large datasets and complex architectures. In this paper, a novel ensemble model, Stacked Ensemble—autoencoder, GRU, and MLP or SE-AGM, composed of three light-weight neural network models—autoencoder, GRU, and MLP—that is trained on the 25 essential and encoded extracted features of the benchmark MalImg dataset for classification was proposed. The GRU model was tested for its suitability in malware detection due to its lesser usage in this domain. The proposed model used a concise set of malware features for training and classifying the malware classes, which reduced the time and resource consumption in comparison to other existing models. The novelty lies in the stacked ensemble method where the output of one intermediate model works as input for the next model, thereby refining the features as compared to the general notion of an ensemble approach. Inspiration was drawn from earlier image-based malware detection works and transfer learning ideas. To extract features from the MalImg dataset, a CNN-based transfer learning model that was trained from scratch on domain data was used. Data augmentation was an important step in the image processing stage to investigate its effect on classifying grayscale malware images in the MalImg dataset. SE-AGM outperformed existing approaches on the benchmark MalImg dataset with an average accuracy of 99.43%, demonstrating that our method was on par with or even surpassed them.