5/4/2023 0 Comments Svm benefits overview![]() The proposed algorithm experimented on Malimg dataset. proposed an MC method based on a combination of the first-order and grey-level co-occurrence matrix (GLCM)-based second-order statistical texture features. In the classification scenario, SVM is the classifier applied. proposed an MC method based on Gabor Wavelet, GIST, and Discrete wavelet Transform. In the classification scenario, Nearest Neighbor (KNN) and Support Vector Machine (SVM) are the two classifiers applied. proposed an MC method based on the D-SIFT and GIST feature extraction methods. In the classification scenario, random forest is the classifier applied. ![]() Kosmidis and Kalloniatis proposed an MC method based on the GIST feature extraction technique. In the classification scenario, the nearest neighbor is the classifier that is applied. proposed an MC method based on global features using the principal component analysis (PCA). In the classification scenario, the K-nearest neighbor is the classifier that is applied. ![]() proposed an MC method based on GIST features from the visualization grayscale images. The proposed method’s accuracy rate was outperformed both the Hand-crafted feature and Deep Feature techniques, at 95.42 and 96.84 percent. The accuracy rate of the proposed method was extremely high, making it the most efficient option available. The proposed method is tested on a standard Malimg unbalanced dataset. Furthermore, the proposed malware classification (MC) method consists of the following five steps: (i) Dataset preparation: 2D malware images are created from the malware binary files (ii) Visualized Malware Pre-processing: the visual malware images need to be scaled to fit the CNN model’s input size (iii) Feature extraction: both hand-engineering (Tamura) and deep learning (GoogLeNet) techniques are used to extract the features in this step (iv) Classification: to perform malware classification, we employed k-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Extreme Learning Machine (ELM). Second, a data imbalance that makes it challenging to classify and correctly identify malware. First, Finding and developing accurate features requires highly specialized domain expertise. There are two main reasons why the most popular MC techniques have a low classification rate. Malware Classification (MC) entails labeling a class of malware to a specific sample, while malware detection merely entails finding malware without identifying which kind of malware it is. Malware developers continually find new ways to circumvent security research’s ongoing efforts to guard against malware attacks. Malware development has significantly increased recently, posing a serious security risk to both consumers and businesses.
0 Comments
Leave a Reply. |