MMWD: An efficient mobile malicious webpage detection framework based on deep learning and edge cloud

Liu, Yizhi; Zhu, Chaoqun; Wu, Yadi; Xu, Heng; Song, Jun

doi:10.1002/cpe.6191

Cited by 7 publications

(3 citation statements)

References 30 publications

(66 reference statements)

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“…The first step discussed above i.e., attaining the feature indication in which fruitful information regarding the URL is saved in a vector so that the ML methods can be implied to it. Several kinds of features have been assumed earlier such as content features, lexical features, popular features, and host-based features [9,10]. However, lexical features are the most widely used features as they have proved to yield superior outcomes and are comparatively simple to attain [11].…”

Section: Introductionmentioning

confidence: 99%

Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

Hilal¹,

Hashim²,

Mohamed³

et al. 2023

Computers, Materials &Amp; Continua

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Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era. Malicious Uniform Resource Locators (URLs) can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their systems. This may result in compromised security of the systems, scams, and other such cyberattacks. These attacks hijack huge quantities of the available data, incurring heavy financial loss. At the same time, Machine Learning (ML) and Deep Learning (DL) models paved the way for designing models that can detect malicious URLs accurately and classify them. With this motivation, the current article develops an Artificial Fish Swarm Algorithm (AFSA) with Deep Learning Enabled Malicious URL Detection and Classification (AFSADL-MURLC) model. The presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine URLs. To attain this, AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding technique. In addition, the created vector model is then passed onto Gated Recurrent Unit (GRU) classification to recognize the malicious URLs. Finally, AFSA is applied to the proposed model to enhance the efficiency of GRU model. The proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle repository. The simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.

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Section: Introductionmentioning

confidence: 99%

Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

Hilal¹,

Hashim²,

Mohamed³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Ertam (2018) used web scraping to collect classified news headlines and summaries from a news agency website and classified the test data using vector learning and deep learning methods. Yizhi Liu et al (2021) developed an efficient mobile malicious webpage detection framework based on deep learning and edge clouds, which applies the ideas of edge computing and multi-device load optimization to MMWD, which can optimally deploy multiple device resources and detect mobile malicious web pages more effectively. Sajedi (2019) used an integrated algorithm to assign weights to weak classifiers.…”

Section: Introductionmentioning

confidence: 99%

An Unhealthy Webpage Discovery System Based on Convolutional Neural Network

Cai

Tan

Zhang

et al. 2022

International Journal of Digital Crime and Forensics

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Currently, with the popularity of the internet, people are surrounded by a large number of unhealthy pages which have a serious impact on the physical and mental health of visitors. To protect the legitimate rights and interests of internet users from infringement and maintain the harmonious and stable development of society, a new unhealthy webpage discovery system is needed. First, this paper proposed the knowledge of unhealthy webpages and web crawlers, and then the whole system's plan and design were introduced. The test results show that the unhealthy webpage discovery system can meet the needs of users. This experiment uses a CNN algorithm to classify the text and completes the collection and classification of unhealthy information through URL acquisition and URL filtering. The experimental results show that the unhealthy webpage discovery system based on a convolutional neural network can greatly improve the accuracy of unhealthy webpage discovery and reduce the omission rate, which can meet the needs of users for unhealthy webpage discovery.

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“…The network end user may be redirected to a local fake webpage or service, and tricked into revealing sensitive information to the attacker. Liu et al present a model, 3 where mobile computing devices may detect nefarious websites in order to avoid compromise. The detection system is deployed on mobile terminals, edge nodes, and servers, and is based on convolutional neural networks.…”

mentioning

confidence: 99%