The rapid proliferation of IoT devices and the growing need for real-time processing have enhanced the quality of life, but also introduced significant security vulnerabilities. While various security solutions exist to counter malicious activities, many fail to adequately address evolving threats. Consequently, there is a clear need for an intelligent system capable of simultaneously adapting to dynamic cyber risks and improving defensive performance. Keeping this in mind, this study developed an imperative hybrid IDS framework called CNN-MGOA by integrating a convolutional neural network (CNN) and multi-objective grasshopper algorithm (MGOA) to help identify intruders in IoT-based networks. Additionally, to more comprehensively capture the important features of network intrusion and improve the detection performance, the multi-objective grasshopper optimization algorithm (MGOA) is utilized in conjunction with a multi-class support vector machine. In this work, comprehensive experiments are conducted on three up-to-date benchmark datasets, including ToN-IoT, CIDD, and NSL-KDD. Experimental results show that our model achieves high detection accuracy of 99.89%, 99.98%, and 99.86%, in NSL-KDD, ToN-IoT, and CIDD datasets, respectively, while obtaining low False Positive Rates of 0.012, 0.049, and 0.041. In addition, it achieved a better effect on anomaly detection than existing state-of-the-art anomaly detection systems. Despite its promising performance, our ability to detect novel risks can be constrained by the presumption of static traffic patterns and the failure to integrate behavioural characteristics. In the near future, we can employ dynamic context-aware analysis to enhance anomaly identification in dynamic IoT scenarios, thereby overcoming existing limitations.

Computational-In-Memory (CIM) is an energy-efficient paradigm that integrates computation directly within memory arrays, reducing the bottleneck associated with data transfer. This approach is beneficial for Artificial Intelligence (AI) applications that require on-chip learning for real-time processing. However, implementing on-chip learning in CIM architectures remains challenging due to limited throughput and energy-efficiency during both online training and inference. In conventional architectures, weight updates necessitate the inference process to halt to avoid unintended computation outcomes. To overcome this limitation, this paper presents a novel Spin-Orbit Torque (SOT)-based CIM architecture tailored for continuous on-chip learning applications, which enable weight updates without interrupting the inference. The proposed SOT bit-cell utilizes two read ports and one write port (2R1W) configuration, where one read port (1R) is dedicated to inference and one read and one write (1R1W) for on-chip learning that enables concurrent read and write operations. Our proposed architecture is evaluated at the system-level using the Generic-PDK 45 nm technology node, demonstrating 2.4× improvement in energy-efficiency and 5.4× improvement in throughput compared to state-of-the-art solutions, with minimal overhead.

ABSTRACT In recent centuries, the fast growth of Internet of Things (IoT) networks has posed serious security vulnerabilities due to heterogeneous devices, evolving attack patterns, and privacy concerns. To encounter security measures, several intrusion detection solutions have been explored based on machine learning, which are reasonably effective in static environments to protect against malicious attacks. In order to defend against modern attacks, unfortunately, fundamental machine learning techniques have not considered accumulation, reuse of knowledge, and sensitivity of the detection model in dynamic environments. Keeping in attention to encounter the aforementioned limitations, in this study, we developed a security model using the grasshopper optimization algorithm (GOA), adding quantum effect to enhance the power of IoT security networks, called QMGOA. Additionally, by integration of quantum effects, the GOA approach improves exploitation capabilities, search efficiency in the feature space, and bridges the gap between exploration and exploitation processes. Furthermore, a multi‐population strategy is employed to strengthen QGOA for making more diverse solutions. Moreover, differential evolution (DE) is used in QMGOA to refine further solution quality. The proposed approach is evaluated on three datasets, such as NSL‐KDD, BoT‐IoT, and UNSW‐NB15, to determine noteworthy features and its efficacy in IoT environments. The experimental analysis further reveals that the proposed method generates better balance solutions with less execution time and outperforms state‐of‐the‐art approaches.

The ever-increasing demand for non-volatile memories with high density, low power consumption, and resistance to radiation has led to the emergence of Double Data Rate Magnetic Random Access Memory (DDR-MRAM) as a promising technology. However, the reliability of DDR-MRAM in harsh radiation environments remains a challenge. This paper presents a novel radiation-hardened pre-discharge sense amplifier specifically designed for DDR-MRAM application. The paper investigates the effects of radiation on the unhardened pre-discharge sense amplifier (PDSA) circuit and proposes solutions at both the device and circuit levels. At the circuit level, two distinct circuits have been proposed: one involves the duplication technique (PDSA-D) and the other employs a feedback circuitry (RT-PDSA) to harden sensitive transistors in the PDSA. The proposed radiation tolerant PDSA (RT-PDSA) circuit demonstrates significant improvements in terms of single event upset (SEU) as well as double node upset (DNU) mitigation by considering minimum sensitive nodes layout area separation concept and enhanced compared to unhardened PDSA circuit. The overall performance of the proposed circuit has been analyzed in terms of the figure of merit (FOM), which includes critical charge tolerability, the number of sensitive nodes, recovery time, and area. The proposed RT-PDSA circuit has 11.95$\times$and 8.7$\times$higher FOM compared to the hardened PDSA at the device level, and PDSA-D circuit, respectively. Moreover, the proposed circuit exhibits robustness against process variations, making it a promising solution for enhancing DDR-MRAM’s reliability in radiation-intensive environments.

Abstract Over the last few decades, computer and internet security has become a vital area because of eye‐opening numbers of data breaches, intruders on perilous infrastructure and malware attacks that are increasing day by day. In order to monitor abnormal activities and identify unusual attacks, several security solutions have been proposed in the recent past years. To protect computer system, intrusion detection system (IDS) opens up great opportunities to determine vulnerabilities and detect anomalies in security system. For detecting new attacks or building security solutions, in this study we present a new wrapper technique for security specialists that can help to detect more complicated attacks by combining teaching learning‐based optimization with opposition learning scheme and simulated annealing method. In order to address advance attack, firstly opposition learning strategy is used to update population generation of teaching learning‐based optimization that affect the robustness of the model after that simulated annealing is integrated into the teaching learning‐based optimization. In proposed method to choose the relevant features, we have used support vector machine as a fitness function that can be helped to recognize attacks precisely. The proposed algorithm is evaluated on three popular datasets namely NSL‐KDD, ISCX 2012 and UNSW‐NB15. Experimental results show that the proposed method is superior to other existing wrapper algorithms in terms of detection rate, accuracy and false alarm rates.

Abstract Teaching learning‐based optimization (TLBO) is a popular stochastic algorithm that has recently been widely applied in a variety of optimization problems since its start. In TLBO algorithm, the concept of chaos not only shows a vital effect in its convergence but also plays a substantial role to balance of exploration and exploitation through evolution. However, TLBO is quickly trapped in local optima and premature convergence seems when applied to sophisticated complex functions. To handle these problems, we introduced an improved TLBO algorithm using chaotic concept. To achieve ability to search for exploration and exploitation, new phase called chaotic phase is added in original TLBO algorithm. The proposed method is thoroughly evaluated on benchmark test suites. The numerical result show that proposed method is relatively effective in adapting the chaotic value regarding original TLBO in terms of solution quality and convergence rate. In addition, performance of proposed method is evaluated on benchmark KDD Cup 99 intrusion dataset. The experimental results demonstrate that proposed method achieves higher predictive accuracy, detection rate, false alarm rate, and provided more significant features as compared with other wrapper techniques.

Abstract Feature selection is an essential task to predict clinical risk and biomarkers from the gene expression data. For practical matters, to choose the significant genes, researchers have been addressed several classical feature selection problems over the past decades for subsequent classification of genomics datasets with large ambient dimensionality but a small number of observations. To overcome high dimensionality and overfitting issues, in this paper, we developed a new gene selection technique by combination of minimum redundancy maximum relevance (mRMR) and teaching learning‐based optimization for accurate cancer prediction. Firstly, in the proposed approach, mRMR is applied to find the most discriminative genes from the original feature sets, and then a precise teaching learning‐based optimization with opposition‐based learning approach further refines the reduced feature set that can contribute to identifying the type of cancers. In addition, a new activation function is also investigated for effective gene selection, which is applied to convert continuous to binary search space. Support vector machine (SVM) is used as a fitness function in the proposed method to select relevant features that can help to estimate the predictive accuracy and classify cancer accurately. Attempts have made to increase the performance of SVM classifier by tuning penalty factor, kernel parameter, and tube size parameter with the help of proposed method. In order to testify computational efficiency of proposed algorithm, we have collected six gene expression datasets. Experimental results demonstrated that proposed method by utilizing SVM with Radial Basis Function kernel function is able to significantly reduce the irrelevant genes and outperform the conventional wrapper methods in terms of accuracy and model interpretation.

Abstract Nowadays, microarray gene expression data plays a vital role in tumor classification. However, due to the accessibility of a limited number of tissues compared to large number of genes in genomic data, various existing methods have failed to identify a small subset of discriminative genes. To overcome this limitation, in this paper, we developed a new hybrid technique for gene selection, called ensemble multipopulation adaptive genetic algorithm (EMPAGA) that can overlook the irrelevant genes and classify cancer accurately. The proposed hybrid gene selection algorithm comprises of two phase. In the first phase, an ensemble gene selection (EGS) method used to filter the noisy and redundant genes in high‐dimensional datasets by combining multilayer and F‐score approaches. Then, an adaptive genetic algorithm based on multipopulation strategy with support vector machine and naïve Bayes (NB) classifiers as a fitness function is applied for gene selection to select the extremely sensible genes from the reduced datasets. The performance of the proposed method is estimated on 10 microarray datasets of numerous tumor. The comprehensive results and various comparisons disclose that EGS has a remarkable impact on the efficacy of the adaptive genetic algorithm with multipopulation strategy and enhance the capability of the proposed approach in terms of convergence rate and solution quality. The experiments results demonstrate the superiority of the proposed method when compared to other standard wrappers regarding classification accuracy and optimal number of genes.

The explosion of the high-dimensional dataset in the scientific repository has been encouraging interdisciplinary research on data mining, pattern recognition and bioinformatics. The fundamental problem of the individual Feature Selection (FS) method is extracting informative features for classification model and to seek for the malignant disease at low computational cost. In addition, existing FS approaches overlook the fact that for a given cardinality, there can be several subsets with similar information. This paper introduces a novel hybrid FS algorithm, called Filter-Wrapper Feature Selection (FWFS) for a classification problem and also addresses the limitations of existing methods. In the proposed model, the front-end filter ranking method as Conditional Mutual Information Maximization (CMIM) selects the high ranked feature subset while the succeeding method as Binary Genetic Algorithm (BGA) accelerates the search in identifying the significant feature subsets. One of the merits of the proposed method is that, unlike an exhaustive method, it speeds up the FS procedure without lancing of classification accuracy on reduced dataset when a learning model is applied to the selected subsets of features. The efficacy of the proposed (FWFS) method is examined by Naive Bayes (NB) classifier which works as a fitness function. The effectiveness of the selected feature subset is evaluated using numerous classifiers on five biological datasets and five UCI datasets of a varied dimensionality and number of instances. The experimental results emphasize that the proposed method provides additional support to the significant reduction of the features and outperforms the existing methods. For microarray data-sets, we found the lowest classification accuracy is 61.24% on SRBCT dataset and highest accuracy is 99.32% on Diffuse large B-cell lymphoma (DLBCL). In UCI datasets, the lowest classification accuracy is 40.04% on the Lymphography using k-nearest neighbor (k-NN) and highest classification accuracy is 99.05% on the ionosphere using support vector machine (SVM).

The duplicate and insignificant features present in the data set to cause a long-term problem in the classification of network or web traffic. The insignificant features not only decrease the classification performance but also prevent a classifier from making accurate decisions, exclusively when substantial volumes of data are managed. In this article, the author introduced an ensemble feature selection (EFS) technique, where multiple homogeneous feature selection (FS) methods are combined to choose the optimal subset of relevant and non-redundant features. An intrusion detection system, named support vector machine-based IDS (SVM-IDS), is prompted using the feature selected by the proposed method. The SVM-IDS performance is evaluated using two benchmark datasets of intrusion detection, including KDD Cup 99 and NSL-KDD. Our proposed method provided more significant features for SVM-IDS and compared with the other state-of-the-art methods. The experimental results demonstrate that proposed method achieves a maximum accuracy as 98.95% in KDD Cup 99 data set and 98.12% in the NSL-KDD data set.