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A Malicious Webpage Detection Algorithm Based on Image Semantics
2020
Traitement du signal
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In the era of the Internet, malicious attacks have put user information at risk. Many malicious webpages use images as the carrier of malicious codes. ...
If extracted accurately, the features of these images will help to improve the detection of malicious webpages. ...
The image/video features on webpages can be effectively extracted through deep learning, laying a good basis for malicious webpage detection. Dé rian et al. ...
doi:10.18280/ts.370115
fatcat:s4qwlv4x2rctba4ue4fs7epjeu
A Malicious Webpage Detection Method Based on Graph Convolutional Network
2022
Mathematics
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In this paper, we propose a GCN-based malicious webpage detection method (GMWD), which constructs a text graph to describe and then a GCN model to learn the syntactic and semantic correlations within and ...
In addition, we use the URL links appearing in the source code as auxiliary detection information to further improve the detection accuracy. ...
Many studies have shown that deep learning performs well in malicious webpage detection. The studies of [12, 20, 21] detected malicious webpages using neural networks such as CNNs. ...
doi:10.3390/math10193496
fatcat:rfmjunxxcjfufjdytevkv3yqwm
CYBER THREAT DETECTION
2022
International Research Journal of Modernization in Engineering Technology and Science
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The proposed technique converts multitude of collected security events to individual event profiles and use a deep learning based detection method for enhanced cyber-threat detection. ...
One of the major challenges in cybersecurity is the provision of an automated and effective cyber-threats detection technique. ...
INTRODUCTION Check suspicious links by using a mixture of blacklists and deep machine learning by IPQS. ...
doi:10.56726/irjmets31438
fatcat:cm4bmn2vwrcijb3vqm4wjcgqum
Malicious and Benign Webpages Dataset
2020
Data in Brief
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This dataset consists of data from approximately 1.5 million webpages, which makes it suitable for deep learning algorithms. ...
The dataset described in this manuscript is meant for such machine learning based analysis of malicious and benign webpages. ...
Acknowledgments I thankfully acknowledge the help, support and guidance of Dr Navneet Goyal, Advanced Data Analytics & Parallel Technologies Lab (ADAPT Lab), Department of Computer Science & Information ...
doi:10.1016/j.dib.2020.106304
pmid:33204771
pmcid:PMC7648114
fatcat:qxc6dg7ucze7nc2a5qm3sbm4km
Comparisons of machine learning techniques for detecting malicious webpages
2015
Expert systems with applications
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In this paper therefore alternative and novel approaches are used by applying machine learning algorithms to detect malicious webpages. ...
This paper compares machine learning techniques for detecting malicious webpages. ...
Acknowledgement The authors would like to thank Technology Strategy Board -KTP, UK for their generous support for part of the research [Grant No. ktp006367]. ...
doi:10.1016/j.eswa.2014.08.046
fatcat:6vviokijvbf7hocz7iteebdv3u
CNN based Malicious Website Detection by Invalidating Multiple Web Spams
2020
IEEE Access
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The proposed detection method uses a Convolutional Neural Network, which is a class of deep neural networks, as a classification algorithm. ...
We then present a new detection method that adopts the perspective of users and takes screenshots of malicious webpages to invalidate Web spams. ...
DEEP LEARNING TECHNIQUES Nowadays more researchers start to adopt deep learning methods to detect malicious websites. ...
doi:10.1109/access.2020.2995157
fatcat:zzaapkrs6bcwfae6iojzexw7eq
Malicious SPAM Injection Attack Detection on Social Webpage Posts
[chapter]
2020
Advances in Parallel Computing
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The suggested version Supervised SD-LVQ used to detect malicious firmware on various social media sites. ...
In the meantime, 28 percent of US internet users between 18 and 55 years of age said their aim is to buy via social media during holidays. ...
Delineation of Supervised Deep Learning Vector Quantization to Detect IoTMalwareIR Learning Vector Quantization (LVQ) is used to detect malicious attacks on different social media networks. ...
doi:10.3233/apc200187
fatcat:id6c64zy5zf63m6ruahkdgtjf4
Hybrid Optimization Driven Technique for Malicious Javascript Detection Based on Deep Learning Classifier
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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In this paper, a deep learning framework for detecting malicious JavaScript code is proposed combing the optimization power of Bird Swarm Algorithm. ...
There exist many machine learningbased malicious script detection approaches, but majority of them follow a shallow discriminating models where manual definition of features are constructed with artificial ...
CONCLUSION This paper proposes a new method on deep learning methodology based on Bird Swarm Algorithm which will produce a significant improvement on detecting malicious JavaScript on webpages. ...
doi:10.35940/ijitee.b1121.1292s219
fatcat:m6qmarcaljgjxhjjju3dlbtpr4
Deep Learning for Phishing Detection
2018
2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom)
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This paper proposes a light-weight deep learning algorithm to detect the malicious URLs and enable a real-time and energy-saving phishing detection sensor. ...
With the recent rapid development of deep learning techniques, many deep-learning-based recognition methods have also been explored to improve classification performance. ...
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations The following abbreviations are used in this manuscript: ...
doi:10.1109/bdcloud.2018.00099
dblp:conf/ispa/YaoD018a
fatcat:hawrzoyi2jcgte2lzlfvkjj5fy
A Deep-Learning-Driven Light-Weight Phishing Detection Sensor
2019
Sensors
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This paper proposes a light-weight deep learning algorithm to detect the malicious URLs and enable a real-time and energy-saving phishing detection sensor. ...
With the recent rapid development of deep learning techniques, many deep-learning-based recognition methods have also been explored to improve classification performance. ...
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations The following abbreviations are used in this manuscript: ...
doi:10.3390/s19194258
fatcat:25ac374mvzbrrnokgzddzztysa
PhishTransformer: A Novel Approach to Detect Phishing Attacks Using URL Collection and Transformer
2023
Electronics
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To address this problem, we propose PhishTransformer, a deep-learning model that can detect phishing attacks by analyzing URLs and page content. ...
We propose only using URLs embedded within a webpage, such as hyperlinks and JFrames, to train PhishTransformer. ...
The details of the datasets are explained in Sections 3.1 and 4.2.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/electronics13010030
fatcat:lxgvrm5huzafze263sebq34rcu
MLPXSS: An Integrated XSS-Based Attack Detection Scheme in Web Applications Using Multilayer Perceptron Technique
2019
IEEE Access
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experimentation, and achieved promising and state-of-the-art results with accuracy, detection probabilities, false positive rate, and AUC-ROC scores of 99.32%, 98.35 %, 0.3%, and 99.02%, respectively. ...
One of the common high-risk cyber-attack of web application vulnerabilities is cross-site scripting (XSS). ...
Furthermore, deep learning schemes are also used to overcome the limitations of the classical machine learning approaches as they can produce high-level representations of features and can even predict ...
doi:10.1109/access.2019.2927417
fatcat:mvh3kwu3b5httdt2eqzlpv6piu
A Deep Learning Technique for Web Phishing Detection Combined URL Features and Visual Similarity
2020
International Journal of Computer Networks & Communications
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Our model uses webpage URLs and images to detect a phishing attack using convolution neural networks (CNNs) to extract the most important features of website images and URLs and then classifies them into ...
The accuracy rate of the results of the experiment was 99.67%, proving the effectiveness of the proposed model in detecting a web phishing attack. ...
The CNN is one of the most popular types of deep learning mechanisms in particular for high dimensional data, such as videos and images. ...
doi:10.5121/ijcnc.2020.12503
fatcat:uh6oa47vqbbuhobirzgmplswje
Intelligent Detection Technique for Malicious Websites Based on Deep Neural Network Classifier
2022
International Journal of Education and Management Engineering
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This study proposes a deep learning method using radial basis function neural network (RBFN), to classify abnormal URLs which are the main sources of malicious websites. ...
We used publicly available datasets to evaluate our model. We then trained and assessed the results of our model against conventional machine learning classifiers. ...
Classification models are then built using these features. The authors in [11, 12, 14] have successfully applied deep learning methods to detect malicious webpages in recent years. ...
doi:10.5815/ijeme.2022.06.05
fatcat:skz3dnz5ozaxjlpnm5gquwfzfe
A Survey on Web Phishing Detection Techniques
2021
International journal for electronic crime investigation
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Web-phishing is used to deceive users, normally carried out through sending links using spoofed emails, instant messages etc. However, web-phishing detection is a challenging task. ...
A number of techniques and mechanisms has been proposed for the detection of web-phishing. The aim of this study is to analyze different web-phishing detection techniques. ...
The aim of this study is to conduct a deep analysis of web-phishing detection techniques. ...
doi:10.54692/ijeci.2021.050279
fatcat:kgplt6gdo5e35jqsszrl7wnqxy
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