Malicious URL Detection Based on a Parallel Neural Joint Model

Yuan, JianTing; Chen, Guanxin; Tian, Shengwei; Pei, Xinjun

doi:10.1109/access.2021.3049625

Cited by 38 publications

(23 citation statements)

References 29 publications

(30 reference statements)

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“…The attacker usually builds a website identical to the target or embeds the exploit code of browser vulnerabilities on the webpage. Then, it tricks the victim into clicking on these links to obtain the victim's information or control the victim's computer [1]. In many circumstances, people do not check the complete website URL, and the attacker can obtain essential and personal information once they visit a malicious website [2].…”

Section: Introductionmentioning

confidence: 99%

Malicious URL Detection Using Decision Tree-based Lexical Features Selection and Multilayer Perceptron Model

Ahmed

Jameel

2022

UHD J SCI TECH

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Network information security risks multiply and become more dangerous. Hackers today generally target end-to-end technology and take advantage of human weaknesses. Furthermore, hackers take advantage of technology weaknesses by applying various methods to attack. Nowadays, one of the greatest dangers to the modern digital world is malicious URLs, and stopping them is one of the biggest challenges in the field of cyber security. Detecting harmful URLs using machine learning and deep learning algorithms have been the subject of various academic papers. However, time and accuracy are the two biggest challenges of these tools. This paper proposes a multilayer perceptron (MLP) model that utilizes two significant aspects to make it more practical, lightweight, and fast: Using only lexical features and a decision tree (DT) algorithm to select the best relevant subset of features. The effectiveness of the experimental outcomes is evaluated in terms of time, accuracy, and error reduction. The results show that a MLP model using 35 features could achieve an accuracy of 94.51% utilizing only URL lexical features. Furthermore, the model is improved in time after applying the DT as feature selection with a slight improvement in accuracy and loss.

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Section: Introductionmentioning

confidence: 99%

Malicious URL Detection Using Decision Tree-based Lexical Features Selection and Multilayer Perceptron Model

Ahmed

Jameel

2022

UHD J SCI TECH

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“…However, Molah et al [18] achieved better accuracy of 97.36% using RF in an intelligent system for detecting phishing websites using different ML techniques. Their dataset was adopted from the University of California Irvine Machine Learning Repository (UCI-ML) [19]. A total of 30 lexical, network, and contentbased features were extracted to classify the URLs.…”

Section: ) Lexical Content-based and Network-based Features Studiesmentioning

confidence: 99%

“…In order to detect and categorize malicious URLs Selvaganapathy et al [30].proposed a methodology based on a stacked restricted Boltzmann machine for feature selection with deep NN. The dataset was formed from MalwareDomainList; UCI-ML Repository: Spambase Dataset [31]; UCI-ML Repository: Phishing Dataset [19]; DMOZ [32]; and Alexa [16]. A total of 98 features were extracted.…”

Section: ) Lexical Content-based and Network-based Features Studiesmentioning

confidence: 99%

Detecting Malicious URLs Using Machine Learning Techniques: Review and Research Directions

et al. 2022

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In recent years, the digital world has advanced significantly, particularly on the Internet, which is critical given that many of our activities are now conducted online. As a result of attackers' inventive techniques, the risk of a cyberattack is rising rapidly. One of the most critical attacks is the malicious URL intended to extract unsolicited information by mainly tricking inexperienced end users, resulting in compromising the user's system and causing losses of billions of dollars each year. As a result, securing websites is becoming more critical. In this paper, we provide an extensive literature review highlighting the main techniques used to detect malicious URLs that are based on machine learning models, taking into consideration the limitations in the literature, detection technologies, feature types, and the datasets used. Moreover, due to the lack of studies related to malicious Arabic website detection, we highlight the directions of studies in this context. Finally, as a result of the analysis that we conducted on the selected studies, we present challenges that might degrade the quality of malicious URL detectors, along with possible solutions.

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“…For the purpose of detecting harmful URLs, Yuan et al [106] introduced a parallel neural joint model approach. The semantic and text features were included in the method by integrating a parallel joint neural network incorporating capsule network (CapsNet) and independent RNN (IndRNN) to improve the detection accuracy.…”

Section: Malicious Traffic In a Cloud Environmentmentioning

confidence: 99%

Intelligent Techniques for Detecting Network Attacks: Review and Research Directions

Aljabri

Aljameel

Mohammad

et al. 2021

Sensors

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The significant growth in the use of the Internet and the rapid development of network technologies are associated with an increased risk of network attacks. Network attacks refer to all types of unauthorized access to a network including any attempts to damage and disrupt the network, often leading to serious consequences. Network attack detection is an active area of research in the community of cybersecurity. In the literature, there are various descriptions of network attack detection systems involving various intelligent-based techniques including machine learning (ML) and deep learning (DL) models. However, although such techniques have proved useful within specific domains, no technique has proved useful in mitigating all kinds of network attacks. This is because some intelligent-based approaches lack essential capabilities that render them reliable systems that are able to confront different types of network attacks. This was the main motivation behind this research, which evaluates contemporary intelligent-based research directions to address the gap that still exists in the field. The main components of any intelligent-based system are the training datasets, the algorithms, and the evaluation metrics; these were the main benchmark criteria used to assess the intelligent-based systems included in this research article. This research provides a rich source of references for scholars seeking to determine their scope of research in this field. Furthermore, although the paper does present a set of suggestions about future inductive directions, it leaves the reader free to derive additional insights about how to develop intelligent-based systems to counter current and future network attacks.

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Malicious URL Detection Based on a Parallel Neural Joint Model

Cited by 38 publications

References 29 publications

Malicious URL Detection Using Decision Tree-based Lexical Features Selection and Multilayer Perceptron Model

Malicious URL Detection Using Decision Tree-based Lexical Features Selection and Multilayer Perceptron Model

Detecting Malicious URLs Using Machine Learning Techniques: Review and Research Directions

Intelligent Techniques for Detecting Network Attacks: Review and Research Directions

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