Phishing attacks have evolved in recent years due to high-tech-enabled economic growth worldwide. The rise in all types of fraud loss in 2019 has been attributed to the increase in deception scams and impersonation, as well as to sophisticated online attacks such as phishing. The global impact of phishing attacks will continue to intensify, and thus, a more efficient phishing detection method is required to protect online user activities. To address this need, this study focussed on the design and development of a deep learning-based phishing detection solution that leveraged the universal resource locator and website content such as images, text and frames.,Deep learning techniques are efficient for natural language and image classification. In this study, the convolutional neural network (CNN) and the long short-term memory (LSTM) algorithm were used to build a hybrid classification model named the intelligent phishing detection system (IPDS). To build the proposed model, the CNN and LSTM classifier were trained by using 1m universal resource locators and over 10,000 images. Then, the sensitivity of the proposed model was determined by considering various factors such as the type of feature, number of misclassifications and split issues.,An extensive experimental analysis was conducted to evaluate and compare the effectiveness of the IPDS in detecting phishing web pages and phishing attacks when applied to large data sets. The results showed that the model achieved an accuracy rate of 93.28% and an average detection time of 25 s.,The hybrid approach using deep learning algorithm of both the CNN and LSTM methods was used in this research work. On the one hand, the combination of both CNN and LSTM was used to resolve the problem of a large data set and higher classifier prediction performance. Hence, combining the two methods leads to a better result with less training time for LSTM and CNN architecture, while using the image, frame and text features as a hybrid for our model detection. The hybrid features and IPDS classifier for phishing detection were the novelty of this study to the best of the authors' knowledge.

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Intelligent phishing detection scheme using deep learning algorithms

When detecting phishing websites, both humans and computers rely on aspects of the website (features) to aid in their decision making. In this work, we conduct a review of URL-based phishing features that appear in publications targeting human-facing and automated anti-phishing approaches. We focus on both humans and computers to obtain a more comprehensive feature list and create a cross-community foundation for future research. We reviewed 94 papers and categorise their features into: lexical, host, rank, redirection, certificate, search engine, and black\textbackslash white lists. We find that research on automation has used all feature categories but several, such as host-based features (e.g. DNS), are minimally explored in human-facing anti-phishing research.

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A Review of Human- and Computer-Facing URL Phishing Features

Phishing is the major problem of the internet era. In this era of internet the security of our data in web is gaining an increasing importance. Phishing is one of the most harmful ways to unknowingly access the credential information like username, password or account number from the users. Users are not aware of this type of attack and later they will also become a part of the phishing attacks. It may be the losses of financial found, personal information, reputation of brand name or trust of brand. So the detection of phishing site is necessary. In this paper we design a framework of phishing detection using URL.

A Machine Learning Approach for URL Based Web Phishing Using Fuzzy Logic as Classifier

Phishing is a technique under Social Engineering attacks which is most widely used to get user sensitive information, such as login credentials and credit and debit card information, etc. It is carried out by a person masquerading as an authentic individual. To protect web users from these attacks, various anti-phishing techniques are developed, but they fail to protect the user from these attacks in various ways. In this paper, we propose a novel technique to identify phishing websites effortlessly on the client side by proposing a novel browser architecture. In this system, we use the rule of extraction framework to extract the properties or features of a website using the URL only. This list consists of 30 different properties of a URL, which will later be used by the Random Forest Classification machine learning model to detect the authenticity of the website. A dataset consisting of 11,055 tuples is used to train the model. These processes are carried out on the client-side with the help of a redesigned browser architecture. Today Researches have come up with machine learning frameworks to detect phishing sites, but they are not in a state to be used by individuals having no technical knowledge. To make sure that these tools are accessible to every individual, we have improvised and introduced detection methods into the browser architecture named as ‘Embedded Phishing Detection Browser’ (EPDB), which is a novel method to preserve the existing user experience while improving the security. The newly designed browser architecture introduces a special segment to perform phishing detection operations in real-time. We have prototyped this technique to ensure maximum security, better accuracy of 99.36% in the identification of phishing websites in real-time.

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Development of anti-phishing browser based on random forest and rule of extraction framework

IT technologies related to Industry 4.0 facilitate the implementation of the framework for sustainable manufacturing. At the same time, Industry 4.0 integrates IT processes and systems of production companies with IT solutions of cooperating companies that support a complete manufactured product life cycle. Thus, the implementation of sustainable manufacturing implies a rapid increase in interfaces between IT solutions of cooperating companies. This, in turn, raises concerns about security among manufacturing company executives. The lack of a recognized methodology supporting the decision-making process of choosing the right methods and means of cybersecurity is, in effect, a significant barrier to the development of sustainable manufacturing. As a result, the propagation of technologies in Industry 4.0 and the implementation of the sustainable manufacturing framework in companies are slowing down significantly. The main novelty of this article, addressing the above deficiencies, is the creation, using the combined DEMATEL and ANP (DANP) and PROMETHEE II methods, of a ranking of the proposed three groups of measures, seven dimensions and twenty criteria to be implemented in companies to ensure cybersecurity in Industry 4.0 and facilitate the implementation of the sustainable production principles. The contribution of Industry 4.0 components and the proposed cybersecurity scheme to achieve the Sustainable Development goals, reducing the carbon footprint of companies and introducing circular economy elements was also indicated. Using DANP and PROMETHEE II, it can be concluded that: (i) the major criterion of cybersecurity in companies is validation and maintaining electronic signatures and seals; (ii) the most crucial area of cybersecurity is network security; (iii) the most significant group of measures in this regard are technological measures.

https://www.mdpi.com/2071-1050/13/16/8833/pdf

A Hybrid MCDM Model Combining DANP and PROMETHEE II Methods for the Assessment of Cybersecurity in Industry 4.0

Recently, many online attacks have increased due to the growing use of the Internet, Among these the most well-known attack is phishing. Phishing is a demonstration of getting delicate data by persuading the users to uncover their own data by pretending as a trusty source in the web transaction. Most phishing attacks work by sending a forged email that contains a URL which leads you to the fake website by clicking on it. In most of the cases, phisher chooses http URLs for the attack. Along these lines, we are proposing a novel algorithm which will detect whether a given http URL is of phishing site or not. Our algorithm performs Google's updated blacklist check, utilizes Google search engine results, Alexa Ranking and no of URL-based features, for detecting phishing URLs. It displays alert message if the URL found as possible phishing, otherwise it displays safe message. This algorithm enhances the performance when dealing with known/old phishing URLs.

A novel algorithm to detect phishing URLs

Risk Rating System of X.509 Certificates

Now a days, a lot of people and groups are using X.509 certificates to represent their identity, during online trade, so the level of purity and trustworthiness of these certificates becomes dubious. Hence, we have suggested a model which calculates the risk associated with X.509 certificates by evaluating Certificate Practice statement (CPS) document and by using certain trust criteria. For evaluating CPS document we have proposed a novel algorithm which locates certain attributes, in the CPS document. We are referring to these attributes from the prior paper of Omar and Lindsay. Our model categorizes risk in three levels-High, Medium and Low risk. It has application in detecting phishing websites which contain Https URL.

Risk assessment of X.509 certificate by evaluating Certification Practice Statements

X.509 certificates empower to reveal the unique identity of the parties participating in the conversation. Right now, during online exchanges, many people and groups are using X.509 certificates to represent their identity, so the level of excellence and reliability of these certificates become dubious. Hence, we introduced a framework which computes risk associated with X.509 certificates with the assistance of certain trust criteria and attributes. For assessing risk related with certificate, we utilized Random Forest ensemble machine learning algorithm, which categorizes risk in three levels- High, Medium and Low. User needs to input the certificate and the system will predict the risk associated with that certificate. If predicted risk is high or medium, system will specify the parameter due to which it triggers risk. Our framework can be applied in browser-server communication and identifying real-time phishing websites which have Https URLs.

Varsharani Hawanna

Papers

A novel algorithm to detect phishing URLs

Risk Rating System of X.509 Certificates

Risk assessment of X.509 certificate by evaluating Certification Practice Statements

Risk Evaluation of X.509 Certificates – A Machine Learning Application