Malware is one of the most common security threats experienced by a user when browsing webpages. A good understanding of the features of webpages (e.g., internet protocol, port, URL, Google index, and page rank) is required to analyze and mitigate the behavior of malware in webpages. This main objective of this paper is to analyze the key features of webpages and to mitigate the behavior of malware in webpages. To this end, we conducted an empirical study to identify the features that are most vulnerable to malware attacks and its results are reported. To improve the feature selection accuracy, a machine learning technique called bagging is employed using the Weka program. To analyze these behaviors, phishing and botnet data were obtained from the University of California Irvine machine learning repository. We validate our research findings by applying honeypot infrastructure using the Modern Honeypot Network (MHN) setup in a Linode Server. As the data suffer from high variance in terms of the type of data in each row, bagging is chosen because it can classify binary classes, date classes, missing values, nominal classes, numeric classes, unary classes and empty classes. As a base classifier of bagging, random tree was applied because it can handle similar types of data such as bagging, but better than other classifiers because it is faster and more accurate. Random tree had 88.22% test accuracy with the lowest run time (0.2 sec) and a receiver operating characteristic curve of 0.946. Results show that all features in the botnet dataset are equally important to identify the malicious behavior, as all scored more than 97%, with the exception of TCP and UDP. The accuracy of phishing and botnet datasets is more than 89% on average in both cross validation and test analysis. Recommendations are made for the best practice that can assist in future malware identification.
Indoor localization methods can help many sectors, such as healthcare centers, smart homes, museums, warehouses, and retail malls, improve their service areas. As a result, it is crucial to look for low-cost methods that can provide exact localization in indoor locations. In this context, imagebased localization methods can play an important role in estimating both the position and the orientation of cameras regarding an object. Image-based localization faces many issues, such as image scale and rotation variance. Also, image-based localization's accuracy and speed (latency) are two critical factors. This paper proposes an efficient 6-DoF deep-learning model for image-based localization. This model incorporates the channel attention module and the Scale Pyramid Module (SPM). It not only enhances accuracy but also ensures the model's real-time performance. In complex scenes, a channel attention module is employed to distinguish between the textures of the foregrounds and backgrounds. Our model adapted an SPM, a feature pyramid module for dealing with image scale and rotation variance issues. Furthermore, the proposed model employs two regressions (two fully connected layers), one for position and the other for orientation, which increases outcome accuracy. Experiments on standard indoor and outdoor datasets show that the proposed model has a significantly lower Mean Squared Error (MSE) for both position and orientation. On the indoor 7-Scenes dataset, the MSE for the position is reduced to 0.19 m and 6.25°for the orientation. Furthermore, on the outdoor Cambridge landmarks dataset, the MSE for the position is reduced to 0.63 m and 2.03°for the orientation. According to the findings, the proposed approach is superior and more successful than the baseline methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.