In today's cybersecurity landscape, software security companies encounter a significant challenge in detecting new and unknown malware. Despite the introduction of various machine learning and deep learning tools ...
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In today's cybersecurity landscape, software security companies encounter a significant challenge in detecting new and unknown malware. Despite the introduction of various machine learning and deep learning tools designed to identify malicious software based on static and dynamic features, achieving the desired level of accuracy remains elusive. This challenge is exacerbated by factors such as encryption, packing, limited distribution, and uneven allocation of malware samples across different families. Moreover, deep learning techniques demand substantial time, computational resources (specifically GPUs), and expertise from data scientists for practical malware analysis. In response to these challenges, we propose a novel GPU-free approach called Imagebased Malware Classification using Broad Learning (IMCBL) to address these issues. Our method integrates visualization, feature decomposition, and broad learning architecture to enhance malware detection and classification. We convert raw malware binaries into images, reducing the necessity for extensive feature engineering. These images transform using truncated Singular Value Decomposition (SVD) to reduce the feature vector size, expediting the training process while mitigating model overfitting. The transformed feature vector is then input into our proposed Broad Learning (BL) system, which facilitates malware detection and classification. The BL architecture, structured as a flat network mapping original inputs to feature nodes and expanding the structure in enhancement nodes, ensures efficient and effective classification without the need for retraining. This dynamic and incremental learning capability sets IMCBL apart, making it superior to existing deep learning architectures. To validate our approach, we conducted extensive experiments using five benchmark malware datasets, including the Microsoft Windows malware challenge dataset, the Malimg Windows malware dataset, the IoT-Android mobile malware dataset, the
This paper introduces a novel approach to learn multi-task regression models with constrained architecture complexity. The proposed model, named RFF-BLR, consists of a randomised feedforward neural network with two fu...
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This paper introduces a novel approach to learn multi-task regression models with constrained architecture complexity. The proposed model, named RFF-BLR, consists of a randomised feedforward neural network with two fundamental characteristics: a single hidden layer whose units implement the random Fourier features that approximate an RBF kernel, and a Bayesian formulation that optimises the weights connecting the hidden and output layers. The RFF-based hidden layer inherits the robustness of kernel methods. The Bayesian formulation enables promoting multioutput sparsity: all tasks interplay during the optimisation to select a compact subset of the hidden layer units that serve as common non-linear mapping for every tasks. The experimental results show that the RFF-BLR framework can lead to significant performance improvements compared to the state-of-the-art methods in multitask nonlinear regression, especially in small-sized training dataset scenarios.
As the production quality index of grinding processes,particle size(PS) is hard to be measured in real *** achieve the PS estimation,this paper proposes a novel random vector functional link networks(RVFLN),namely,rob...
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As the production quality index of grinding processes,particle size(PS) is hard to be measured in real *** achieve the PS estimation,this paper proposes a novel random vector functional link networks(RVFLN),namely,robust regularized *** incorporating the weighted least squares(WLS) and regularization techniques into the original RVFLN and further adopting a nonparametric kernel density estimation(NKDE) method to choose the weighted term,the generalization and robustness of network have been *** order to ensure the quality and computational load of network in online application,the different online learning versions are presented according to the various time scales of data sampling,*** experimental studies are first carried out based on the UCI and Statlib standard data *** last,the actual industrial grinding operation data are used to verify the effectiveness of the proposed method in term of PS estimation.
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