Efficient Detection of Phishing Websites Using Multilayer Perceptron

Odeh, Ammar; Keshta, Ismail; Abdelfattah, Eman

doi:10.3991/ijim.v14i11.13903

Cited by 25 publications

(12 citation statements)

References 21 publications

(22 reference statements)

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“…It consists of three or more layers ( i.e., input and an output layer with one or more hidden layers) of nonlinearlyactivating nodes. This algorithm employs the backpropagation supervised learning method and works through determining the most proper synaptic weight in classifying patterns in the training dataset [27].…”

Section: Multilayer Perceptron Artificial Neural Network (Mlp-ann)mentioning

confidence: 99%

Secure Real-Time Computational Intelligence System Against Malicious QR Code Links

Wahsheh

Al-Zahrani

2021

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Web attackers aim to propagate malicious links using various techniques to deceive users. They attempt to control victims’ devices or obtain their passwords remotely, thereby acquiring access to bank accounts, financial transactions, or private and sensitive information they trade via the Internet. QR codes are accessible, free, easy to use, and can be scanned through several free apps on smartphones. As there is no standard structure or authentication phase in QR code generation, such codes are vulnerable to suspicious online content embedding, i.e., phishing, Cross-Site Scripting (XSS), and malware. Many studies have highlighted the attacks that may be perpetrated using barcodes, and there are some security countermeasures. Several of these solutions are limited to malicious link detection methods or require knowledge of cryptographic techniques. This study’s main objective is to detect malicious URLs embedded in QR codes. A dataset of 90 000 benign and malicious URLs was collected from various resources, and their lexical properties were extracted. Two computational intelligence models, fuzzy logic and multilayer perceptron artificial neural network (MLP-ANN), were applied and compared. An MLP-ANN was identified as the best classifier for detecting malicious URLs, and a proactive, secure, real-time computational intelligence barcode scanner implementation (BarCI ) against malicious QR code links was proposed based on this classifier. The results demonstrate that this approach enables efficient real-time attack detection with 82.9% accuracy

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Section: Multilayer Perceptron Artificial Neural Network (Mlp-ann)mentioning

confidence: 99%

Secure Real-Time Computational Intelligence System Against Malicious QR Code Links

Wahsheh

Al-Zahrani

2021

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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“…In [9] In [4] Ammar Odeh et al designed a Multi-Layer Perceptron by considering the URL features. On the extracted features, performed selection, combination of ranking and single attribute evaluation is performed.…”

Section: Related Workmentioning

confidence: 99%

Feature Engineering Framework to detect Phishing Websites using URL Analysis

Goud¹,

Mathur²

2021

IJACSA

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Phishing is a most popular and dangerous cyberattack in the world of internet. One of the most common attacks in cyber security is to access the personal information of internet users through "Phishing Website". The major element through which hacker can do this job is through URL. Hacker creates an almost replica of original URL in which there is a very small difference, generally not revealed without keen observation. By pipelining various machine learning algorithms, the proposed model aims to recognize the important features to classify the URL using a recursive feature elimination process. In this work the data set of various URL records has been collected with 112 features including one target value. In this work a Machine Learning based model is proposed to identify the significant features, used to classify a URL, the wrapper method recursive feature elimination compares different bagging and boosting machine learning approaches .Ensemble algorithms, Bootstrap Aggregation Algorithms, Boosting and stacking algorithms are used for feature selection. The proposed work has five sections: work on the pre-processing phase, finding the relation between the features of the dataset, automatic selection of number of features using Extra Tree Classifier, comparison of the various ensemble algorithm and finally generates the best features for URL analysis. This paper, designs meta learner with XG BOOST classifier as base classifier and achieved an accuracy of 93% Out of 112 features, this model has performed an extensive comparative study on feature selection and identified 29 features as core features by performing URL analysis.

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“…Jain et al (2011) publication assessed the problem with remaining silent especially on the issues related to the project being developed. This empirical study conducted reviewed the prior research, which suggested that there is no communication between the client managers and the vendor's employees and this can lead to poor quality services that are offered to clients (Goby, 2015;Hossain et al, 2009;Odeh et al, 2020). Cross-cultural teams are also facing the challenge of understanding the views and demands of the employees and this can sometimes result to conflict in the decision-making process.…”

Section: A Rq1 -Barriers Identified Using Slrmentioning

confidence: 99%

Critical Barriers in Software Outsourcing Vendor Organizations and Their Impacts on Software Outsourcing Clients: A Systematic Literature Review

Keshta¹,

Odeh²

2020

Journal of Computer Science

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Globalization and technological advancement have driven up the demand for software as many enterprises seek to gain a competitive edge through efficient business operations. Whereas software provides this capacity, the resources involved in software development sometimes provide a stumbling block, especially for startups and smaller businesses. Software development outsourcing or offshore software development offers a viable alternative for these enterprises. Software development outsourcing is a contractual agreement between a client and vendor organization (s), who then provide part or all of the software development related services at an agreed fee paid by the client. Despite the apparent importance of offshore software development, there is limited research literature that explores the general practices in software development outsourcing. Besides, identifying barriers that often present a significant challenge for client organizations has received a little interest from researchers. This paper presents a Systematic Literature Review (SLR) of 68 research articles to identify the critical barriers for offshore software development outsourcing vendors. In order to increase the internal validity of the reported findings, the articles selected for the review were taken from three continents; Africa, Asia and Europe. The critical barriers established from the study included communication problems, cultural barriers and incompatibility with the client's requirements. From the findings, it is recommended that vendors address these barriers for an improved relationship between themselves and the clients. Moreover, addressing these barriers offer the vendors with an opportunity to position themselves strategically within the offshore outsourcing industry. Furthermore, the findings provide meaningful literature in the continuum of Software Development Outsourcing for Software project managers, researchers and other participants in the field. More importantly, the presented literature offers a starting point for generalizing the reported findings within the context of Software Development Outsourcing.

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Efficient Detection of Phishing Websites Using Multilayer Perceptron

Cited by 25 publications

References 21 publications

Secure Real-Time Computational Intelligence System Against Malicious QR Code Links

Secure Real-Time Computational Intelligence System Against Malicious QR Code Links

Feature Engineering Framework to detect Phishing Websites using URL Analysis

Critical Barriers in Software Outsourcing Vendor Organizations and Their Impacts on Software Outsourcing Clients: A Systematic Literature Review

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