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RMDNet-Deep Learning Paradigms for Effective Malware Detection and classification

IEEE ACCESS 2024年 12卷 82622-82635页

作者： Puneeth, S. Lal, Shyam Pratap Singh, Mahendra Raghavendra, B. S. Natl Inst Technol Karnataka NITK Surathkal Dept Elect & Commun Engn Surathkal 575025 India Natl Inst Engn Dept Elect & Commun Engn Mysuru 570008 India Natl Inst Technol Karnataka NITK Surathkal Dept Comp Sci & Engn Surathkal 575025 India

Malware analysis and detection are still essential for maintaining the security of networks and computer systems, even as the threat landscape shifts. Traditional approaches are insufficient to keep pace with the rapidly evolving nature of malware. Artificial Intelligence (AI) assumes a significant role in propelling its design to unprecedented levels. Various Machine Learning (ML) based malware detection systems have been developed to combat the ever-changing characteristics of malware. Consequently, there is a growing interest in exploring advanced techniques that leverage the power of Deep Learning (DL) to effectively analyze and detect malicious software. DL models demonstrate enhanced capabilities for analyzing extensive sequences of system calls. This paper proposes a Robust Malware Detection Network (RMDNet) for effective malware detection and classification. The proposed RMDNet model branches the input and performs depth-wise convolution and concatenation operations. The experimental results of the proposed RMDNet and existing DL models are evaluated on 48240 malware and binary visualization image dataset with RGB format. Also on the multi-class malimg and dumpware-10 datasets with grayscale format. The experimental results on each of these datasets demonstrate that the proposed RMDNet model can effectively and accurately categorize malware, outperforming the most recent benchmark DL algorithms.

关键词： Malware Security Feature extraction Static analysis Codes Convolutional neural networks Analytical models Binary sequences classification algorithms Computer security Deep learning Binary classification concatenation convolution cyber security deep learning depthwise convolution malware multiclass classification

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Toward Interpretable Sleep Stage classification Using Cross-Modal Transformers

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2024年 32卷 2893-2904页

作者： Pradeepkumar, Jathurshan Anandakumar, Mithunjha Kugathasan, Vinith Suntharalingham, Dhinesh Kappel, Simon L. De Silva, Anjula C. Edussooriya, Chamira U. S. Univ Moratuwa Dept Elect & Telecommun Engn Moratuwa 10400 Sri Lanka Univ Illinois Dept Comp Sci Champaign IL 61801 USA Univ Illinois Dept Bioengn Champaign IL 61801 USA Univ Maryland Inst Syst Res College Pk MD 20742 USA Aarhus Univ Dept Elect & Comp Engn DK-8200 Aarhus Denmark Ear Sci Inst Australia Perth WA 6009 Australia Florida Int Univ Dept Elect & Comp Engn Miami FL 33174 USA

Accurate sleep stage classification is significant for sleep health assessment. In recent years, several machine-learning based sleep staging algorithms have been developed, and in particular, deep-learning based algorithms have achieved performance on par with human annotation. Despite improved performance, a limitation of most deep-learning based algorithms is their black-box behavior, which have limited their use in clinical settings. Here, we propose a cross-modal transformer, which is a transformer-based method for sleep stage classification. The proposed cross-modal transformer consists of a cross-modal transformer encoder architecture along with a multi-scale one-dimensional convolutional neural network for automatic representation learning. The performance of our method is on-par with the state-of-the-art methods and eliminates the black-box behavior of deep-learning models by utilizing the interpretability aspect of the attention modules. Furthermore, our method provides considerable reductions in the number of parameters and training time compared to the state-of-the-art methods. Our code is available at https://***/Jathurshan0330/Cross-Modal-Transformer. A demo of our work can be found at https://***/Cross_modal_transformer_demo.

关键词： Transformers Sleep Brain modeling Electroencephalography classification algorithms Task analysis Electrooculography Automatic sleep stage classification interpretable deep learning transformers deep neural networks

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An Optimal Random Projection k Nearest Neighbors Ensemble via Extended Neighborhood Rule for Binary classification

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IEEE ACCESS 2024年 12卷 61401-61409页

作者： Ali, Amjad Khan, Zardad Khan, Dost Muhammad Aldahmani, Saeed Abdul Wali Khan Univ Mardan Dept Stat Mardan 23200 Pakistan United Arab Emirates Univ Dept Stat & Business Analyt Al Ain U Arab Emirates

This paper presents an ensemble method for binary classification, where each base model is based on an extended neighbourhood rule (ExNRule). The ExNRule identifies the neighbours of an unseen observation in a stepwise manner. This rule first selects the sample point closest to the experimental observation and then selects the second observation nearest to the previously chosen one. To find the required data points in the neighbourhood, this search is repeated up to k steps. The test sample point is predicted using majority voting in the class labels of the k chosen neighbours. In the proposed method, a large number of ExNRule based models are constructed on randomly projected bootstrap samples. The error rates of these models are computed using out-of-bag data points. The models are then ranked according to their out-of-bag errors, and a proportion of the most accurate models are selected. The final ensemble is constructed by combining the selected models. The proposed method is compared with other classical procedures on 15 benchmark datasets in terms of classification accuracy, Kohen's kappa and Brier score (BS) as performance metrics. Boxplots of the results are also constructed. The proposed ensemble is outperforming the existing methods on almost all the benchmark datasets. For further evaluation, the proposed method is compared with other kNN based classifiers on 3 datasets using different k values. Furthermore, the performance of the proposed method is also evaluated using simulated data under different scenarios.

关键词： Computational modeling Data models Training data Training Predictive models Mathematical models Ensemble learning classification algorithms classification kNN extended neighborhood rule ensemble learning bootstrapping random projection

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Advanced Fault Detection, classification, and Localization in Transmission Lines: A Comparative Study of ANFIS, Neural Networks, and Hybrid Methods

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IEEE ACCESS 2024年 12卷 49017-49033页

作者： Kanwal, Shazia Jiriwibhakorn, Somchat King Mongkuts Inst Technol Ladkrabang KMITL Sch Engn Bangkok 10520 Thailand

Electric systems are getting more complex with time, and primitive protection methods such as traveling wave and impedance-based methods face limitations and shortcomings. This paper incorporates and presents the applications of an adaptive neuro-fuzzy inference system and compares it with a back propagation neural network, self-organizing map, and hybrid method of discrete wavelet with adaptive neuro-fuzzy inference system for fault detections, classification, and localization in transmission lines. These methods, in comparison with primitive methods, could be capable of detecting, identifying, and predicting the location of the faults more accurately. The IEEE 9-bus system is utilized to obtain data from one end of the transmission line to develop an ANFIS-based model. This system is simulated in MATLAB/Simulink for different fault cases at various locations. The three-phase voltage and current at one end of IEEE 9-bus number seven are taken for training. Three ANFIS models are developed for fault detection, classification, and localization and compared with other models. For verification of the models, mean square error, mean absolute error, and regression analysis have been computed and compared for all the models. All four techniques have performed well for fault classification, detection, and location. However, the percentage error for the ANFIS-based fault model is less compared to backpropagation, self-organizing map, and discrete wavelet transform with ANFIS. Therefore, the proposed ANFIS models can be implemented for deploying in real-time-based protection systems.

关键词： Power transmission lines Circuit faults Fault detection Mathematical models Adaptive systems Discrete wavelet transforms Computational modeling classification algorithms ANFIS SOM DWT fault detection classification and location IEEE 9-bus system transmission line

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SI-GAT: Enhancing Side-Scan Sonar Image classification Based on Graph Structure

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IEEE SENSORS JOURNAL 2024年第15期24卷 24388-24404页

作者： Lei, Can Wang, Huigang Lei, Juan Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen 518057 Peoples R China

Underwater target classification based on side-scan sonar (SSS) imagery holds significant military and civilian applications. However, challenges, such as low sonar image resolution and limited target features, pose substantial difficulties in the classification of SSS images. The existing deep-learning-based methods for SSS image classification primarily focus on the bright areas of targets, often neglecting information from acoustic shadow regions, global features, and spatial context features. To address these challenges and bolster the correlation among regions containing valuable information, thus enhancing the classification accuracy of SSS images, we propose the SI-GAT model, a graph-based classification network tailored for SSS images. Initially, simultaneous feature extraction is performed on both target bright areas and shadow regions to obtain more comprehensive target features. Subsequently, a weighting function and a metric function are defined to calculate feature distances and correlation matrices, capturing the spatial relationships of targets. The introduction of the K-nearest neighbors (KNN) algorithm and attention mechanism adaptively allocates aggregation coefficients within the neighborhood, promoting the aggregation and propagation of features. Finally, the features of all nodes in the aggregated graph are combined to extract global features. Through testing on a real SSS dataset and comparing the convergence and classification performance with state-of-the-art CNN and GNN models, the results validate the effectiveness of our proposed method.

关键词： Feature extraction Sonar Image classification Side-scan sonar Convolutional neural networks Graph neural networks classification algorithms Acoustic shadow region attention mechanism graph structure image classification side-scan sonar (SSS)

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Actuator Fault Tolerant Control in Switched Systems: A Comprehensive Approach Integrating Clustering, classification, and LMI-Based Compensation

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IEEE ACCESS 2025年 13卷 44090-44106页

作者： Yahia, Salwa Bedoui, Saida Abderrahim, Kamel Univ Gabes Lab Numer Control Ind Proc Tunis 1053 Tunisia

This paper presents a new strategy to address the challenges associated with the design of fault tolerant tolerant control (FTC) in stochastic switched systems. In fact, the problem of predicting the switched mode in discrete behavior is based on a set of input-output database. To address this, the proposed methodologies incorporate a combination of clustering and classification techniques to determine the number of sub-models. The estimated actuator fault is then compensated by controller generated for each sub model. The controller gains are given by solving the Linear Matrix Inequality (LMIs), which is derived based on the general Lyapunov function. The fast convergence of the actuator fault, which is estimated using the proposed data-driven method, minimizes the instability time of the switched system after fault occurrence and attenuates the disturbance. Finally, the effectiveness of the proposed method against actuator faults is validated through two numerical analyses. The first example shows a faulty stochastic switched system with two modes and the second example shows a practical application of a double cart system with elastic coupling.

关键词： Switches Control systems Fault tolerant systems Fault tolerance Switched systems Noise Estimation Switching systems Switching circuits classification algorithms Switched system classification fault-tolerant control actuator fault switching mode Lyapunov

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Applying the Deep Learning Techniques to Solve classification Tasks Using Gene Expression Data

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IEEE ACCESS 2024年 12卷 28437-28448页

作者： Babichev, Sergii Liakh, Igor Kalinina, Irina Kherson State Univ Dept Phys UK-73008 Kherson Ukraine Jan Evangelista Purkyne Univ Usti Nad Labem Dept Informat Usti Nad Labem 40096 Czech Republic Uzhgorod Natl Univ Dept Informat Sci & Phys & Math Disciplines Uzhgorod 88016 Ukraine Petro Mohyla Black Sea Natl Univ Dept Intelligent Informat Syst Mykolaiv 54000 Ukraine

This manuscript explores the application of deep learning (DL) techniques for classifying gene expression data. A key aspect of our research is the comparative analysis of various DL neural network architectures, including Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) Recurrent Neural Networks (RNN), as well as hybrid models that combine these networks. We applied the Bayesian optimization algorithm using 5-fold cross-validation for optimal hyperparameter tuning, which is crucial for DL algorithm performance. Significantly, we have advanced the methods for applying RNNs in processing gene expression data, particularly focusing on LSTM and GRU types. Our study introduces also a novel hybrid quality criterion for data classification, calculated as a weighted sum of partial quality criteria, incorporating an integrated F1-score derived through the Harrington desirability method. Furthermore, we investigate hybrid models that leverage various DL methods, enhancing decision-making objectivity in sample identification. This model uses a step-by-step information processing procedure, initially applying different DL models to gene expression data and subsequently processing these through a CART-based classifier for final decision-making. Our experiments, performed on gene expression data from patients with eight cancer types and one subset with normal samples (without cancer), demonstrated that GRU-RNN-based models, particularly a two-layer GRU-RNN, achieved the highest classification efficacy, with an accuracy of 97.8% on the test dataset. The performance of this model exceeded that of other models, whose accuracy varied between 96.6% and 97.3%. Comparative analysis with other studies in this field suggests that the proposed techniques demonstrate higher efficacy compared to similar research regarding the application of DL models for cancer-type diagnosis.

关键词： Gene expression Bayes methods Data models Recurrent neural networks classification algorithms Bioinformatics Convolutional neural networks Cancer Quality assessment Convolution neural network LSTM recurrent neural network GRU recurrent neural network gene expression data classification hybrid model classification quality criteria cancer disease

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Cross-View Representation Learning: A Superior ContextIB Method for Logo classification

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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 536-540页

作者： Wang, Jing Zheng, Yuanjie Han, Zeyu Lv, Mei Hou, Sujuan Shandong Normal Univ Sch Informat Sci & Engn Jinan 250300 Peoples R China Shandong Univ Sch Math & Stat Jinan 264209 Peoples R China Shandong Womens Univ Sch Phys Educ Dept Jinan 250300 Peoples R China

Logo classification systems have become increasingly important in various industries for tasks, such as infringement detection and industrial production. However, challenges still exist in logo classification due to real-world image background interference, the high similarity between classes, labeling difficulties, and the insufficient representation of occlusion in single-view logos. Many existing algorithms fail to consider the data characteristics and the intrinsic information of multiple views, which limits their performance. To overcome these limitations, we developed a novel Cross-View Information Awareness Network (CVIA-Net) for logo classification. To differentiate between similar logo categories, the CVIA-Net novel learns context-shared features of the same category via a self-supervised way without labeled, which solves the problem of insufficient features due to occlusion. For single-view images, CVIA-Net establishes a "bottleneck" representation to address background interference. Extensive experiments on three datasets demonstrate that it outperforms state-of-the-art methods. The method is expected to advance the development of cross-view representation learning.

关键词： Feature extraction Representation learning Image coding Data mining Task analysis Interference classification algorithms Artificial intelligence computer vision informat- ion bottleneck logo classification representation learning

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A Multi-Tree Genetic Programming-Based Ensemble Approach to Image classification With Limited Training Data

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IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 2024年第4期19卷 47-62页

作者： Fan, Qinglan Bi, Ying Xue, Bing Zhang, Mengjie Shandong Univ Finance & Econ Jinan Peoples R China Zhengzhou Univ Zhengzhou Peoples R China Victoria Univ Wellington Wellington New Zealand

Large variations across images make image classification a challenging task;limited training data further increases its difficulty. Genetic programming (GP) has been considerably applied to image classification. However, most GP methods tend to directly evolve a single classifier or depend on a predefined classification algorithm, which typically does not lead to ideal generalization performance when only a few training instances are available. Applying ensemble learning to classification often outperforms employing a single classifier. However, single-tree representation (each individual contains a single tree) is widely employed in GP. Training multiple diverse and accurate base learners/classifiers based on single-tree GP is challenging. Therefore, this article proposes a new ensemble construction method based on multi-tree GP (each individual contains multiple trees) for image classification. A single individual forms an ensemble, and its multiple trees constitute base learners. To find the best individual in which multiple trees are diverse and effectively cooperate, i.e., the nth tree can correct the errors of the previous n-1 trees, the new method assigns different weights to multiple trees using the idea of AdaBoost and performs classification via weighted majority voting. Furthermore, a new tree representation is developed to evolve diverse and accurate base learners that extract useful features and conduct classification simultaneously. The new approach achieves significantly better performance than almost all benchmark methods on eight datasets. Additional analyses highlight the effectiveness of the new ensembles and tree representation, demonstrating the potential for providing valuable interpretability in ensemble trees.

关键词： Training data Accuracy Gray-scale Feature extraction Computational efficiency classification algorithms Ensemble learning Image classification Genetic programming Representation learning Benchmark testing

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Comparative Analysis of Machine Learning algorithms and Ensemble Techniques for Diverse Image classification Tasks

Comparative Analysis of Machine Learning Algorithms and Ense...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Yulita Salim Athar Fathana Rakasyah Herdianti Darwis Harlinda Irawati Abdul Rachman Manga Department of Computer Science Universitas Muslim Indonesia Makassar Indonesia

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

In the era of burgeoning image data, the demand for robust image classification algorithms has never been more pressing. This research article delves into the realm of image classification, encompassing a comprehensive analysis of diverse datasets and machine learning algorithms, While simultaneously studying the efficiency of ensemble approaches in improving classification performance. The study employs five distinct datasets, spanning medical images, everyday objects, and natural scenery, ensuring a broad spectrum of classification challenges. These datasets include the Skin Cancer ISIC dataset, CIFAR-10, Flowers, Apparel Image, and Brain Tumor dataset. The image classes within these data sets vary significantly, presenting an ideal testbed for assessing algorithmic versatility. Our investigation scrutinizes five machine learning algorithms: Support Vector Classifier (SVC), Random Forest Classifier (RFC), Gradient Boosting Classifier (GBC), K-Nearest Neighbors (KNN), and Gaussian Naive Bayes (GNB). Each algorithm is evaluated individually, followed by a comprehensive ensemble approach involving a Voting Classifier. The results unveil nuanced performance variations across diverse datasets. Notably, RFC and GBC exhibit remarkable accuracy in brain tumor image classification, while KNN demonstrates strengths in classifying apparel images. Ensemble techniques, embodied by the Voting Classifier, harmonize these algorithms, yielding competitive and balanced performance across the datasets. This article contributes valuable insights into the realm of image classification, shedding light on algorithmic strengths and limitations, the efficacy of ensemble techniques, and their applicability to diverse image datasets. These findings hold significance for fields ranging from medical diagnostics to everyday object recognition, paving the way for more precise and versatile image classification solution

关键词： Machine learning algorithms Accuracy Heuristic algorithms Brain tumors Nearest neighbor methods classification algorithms Complexity theory Ensemble learning Random forests Image classification

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