Machine Learning &amp; Concept Drift based Approach for Malicious Website Detection

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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“…[25] f14 urlRedirection Boolean There exists a slash "//" in the link path [8], [19], [22], [23], [27] f15…”

Section: Url Lexical Feature Extractionmentioning

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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“…Once these functions are known to malicious website designers, it is easy to bypass these security settings. Singhal et al [21] used machine learning to classify malicious websites and proposed concept drift detection to find the difference in data distribution between the feature vectors of the old training dataset and the newly collected dataset.…”

Section: Machine Learningmentioning

confidence: 99%

A Novel Approach for Malicious URL Detection Based on the Joint Model

Yuan

Liu

2021

Security and Communication Networks

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The number of malicious websites is increasing yearly, and many companies and individuals worldwide have suffered losses. Therefore, the detection of malicious websites is a task that needs continuous development. In this study, a joint neural network algorithm model combining the attention mechanism, bidirectional independent recurrent neural network (Bi-IndRNN), and capsule network (CapsNet) is proposed. The word vector tool word2vec trains the character- and word-level uniform resource locator (URL) static embedding vector features. At the same time, the algorithm will also extract texture fingerprint features that can compare the content differences of different malicious web URL binary files. Then, the extracted features are fused and input into the joint neural network algorithm model. First, the multihead attention mechanism is used to extract contextual semantic features by adjusting weights and Bi-IndRNN. Second, CapsNet with dynamic routing is used to extract deep semantic information. Finally, the sigmoid classifier is used for classification. This study uses different methods from different angles to extract more comprehensive features. From the experimental results, the method proposed in this study improves the classification accuracy of malicious web page detection compared with other researchers.

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“…Among the most common techniques in this field are the Support Vector Machines (SVM) [28]- [32], Logistic Regression (LR) [31], [33]- [35], Naïve Bayes (NB) [34]- [37], and Decision Tree [31], [38], [39]. In [40] a set of ML models have been evaluated for classifying malicious websites given their URL as input. In addition, a ML method based on SVM to classify malicious websites by using only domain names has been proposed [41].…”

Section: ML For Risky Websites Detectionmentioning

confidence: 99%

Knowledge-Based Approach to Detect Potentially Risky Websites

et al. 2021

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Nowadays, fraudulent and malicious websites are emerging as a harmful and very common problem on the Internet. It causes huge money losses and irreparable damage for both companies and particulars. To face this situation, governments have approved multiple law projects. This way, the legality on the Internet is being enforced and sanctions to those offenders who develop illegal or malicious activities are being imposed. However, governments still need a way to simplify the classification of websites into risky or non-risky, since most of this work is manual. This paper presents the DOmains Classifier based on RIsky Websites (DOCRIW ) framework to detect domains that contain possible fraud or malicious content. It is based on two main components. The first component is a previously built knowledge base containing information from risky websites. The second one complements the system with a binary classifier able to label a website (as risky or not) considering just its domain. The system makes use of web information sources and includes host-based variables. It also applies similarity measures, supervised learning algorithms and optimization methods to enhance its performance. The presented work is experimental, rendering promising outcomes.INDEX TERMS Risky website detection, malware alerts, knowledge-based systems, similarity metrics, combination of information.

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Machine Learning & Concept Drift based Approach for Malicious Website Detection

Cited by 33 publications

References 8 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

A Novel Approach for Malicious URL Detection Based on the Joint Model

Knowledge-Based Approach to Detect Potentially Risky Websites

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