Simulating Human Detection of Phishing Websites: An Investigation into the Applicability of the ACT-R Cognitive Behaviour Architecture Model

Williams, Nick; Li, Shujun

doi:10.1109/cybconf.2017.7985810

Cited by 9 publications

(5 citation statements)

References 9 publications

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“…As these types of attacks are now common these days, end-users must be trained with some basic knowledge about these attacks in order to avoid any loss. An model was proposed by Williams and Li [6] for evaluating the cognitive behavior of ACT-R. Based on the HTTP padlock's security indications, the authenticity of the web page is determined. Afroz and Greenstadt [7] designed a technology called "PhishZoo", in which the performance of the website before it loads is observed and then the profile of the website is also taken into the consideration while detecting the sites.…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting Phishing sites Without Visiting them

Pagadala¹

2022

Preprint

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Now-a-days, cyberattacks are increasing at an unprecedented rate. Phishing is a social engineering attack which has a massive global impact, destroying the financial and economic value of corporations, government sectors and individuals. In phishing, attackers steal users personal information such as username, passwords, debit card information and so on. In order to detect zero-hour attacks and protect end-users from these attacks, various anti-phishing techniques are developed, but the end-users have to visit the websites to know whether they are safe or not, which may lead to infecting their system. In this paper, we propose a method where end-users can detect the genuineness of the sites without visiting them. The proposed method collects legitimate and phishing URLs and extract features from them. The extracted features are given as input to six different classifiers for training and constructing the model. The classifiers used are Naive-Bayes, Logistic Regression, Random Forest,CatBoost, XGBoost and Multilayer perceptron. The method is tested by developing into an extension so that the end-users can use it when browsing. In the browser extension when the user takes the cursor over any link, a pop-up appears showing the nature of the website i.e., safe site or deceptive site and then a confirm box shows up asking the user whether they want to visit or not. The performance of the approach is tested using a dataset consisting of 2000 phishing and legitimate website URLs and the method is able to detect the sites correctly in very little time. Random-Forest is chosen for constructing the model as it gives the highest accuracy of 95%.

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Section: Literature Reviewmentioning

confidence: 99%

Detecting Phishing sites Without Visiting them

Pagadala¹

2022

Preprint

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“…User needs to be aware of various kinds of phishing-attacks and prior knowledge is essential in identifying these webpages in real-time. Williams and Li (2017) proposed an architectural model that evaluates ACT-R cognitive behavior. This is carried out by analyzing the authenticity of webpages based on the HTTP padlock security indicator.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Detecting and blocking a phishing attack is extremely important to preserve the security and confidentiality of an individual over the internet. Researches have come up with various approaches (Armano et al 2016;Hu et al 2016;Ma et al 2009;Roy et al 2013;Sahingoz et al 2019;Williams and Li 2017) to solve this prominent problem. However, they fail in some way to be easily used by every individual.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

Cybersecur

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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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“…Architectures, such as ACT-R [Anderson et al, 2004] and SOAR [Laird et al, 1987], have long been a part of HCI research to simulate users interacting with an interface. For example, ACT-R models are used for usability testing of menus [Byrne, 2001], modeling how users detect phishing websites [Williams and Li, 2017], and detecting situations with high cognitive load when using a smartphone [Wirzberger and Russwinkel, 2015]. Cognitive architectures have been used with physiological data, such as eye tracking information and fMRI, to map observed behavior the underlying mental states and brain regions [Tamborello andByrne, 2007, Borst andAnderson, 2015].…”

Section: Zzz ?!?mentioning

confidence: 99%

On Interactive Machine Learning and the Potential of Cognitive Feedback

Michael¹,

Acklin²,

Scheuerman³

2020

Preprint

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In order to increase productivity, capability, and data exploitation, numerous defense applications are experiencing an integration of state-of-theart machine learning and AI into their architectures. Especially for defense applications, having a human analyst in the loop is of high interest due to quality control, accountability, and complex subject matter expertise not readily automated or replicated by AI. However, many applications are suffering from a very slow transition. This may be in large part due to lack of trust, usability, and productivity, especially when adapting to unforeseen classes and changes in mission context. Interactive machine learning is a newly emerging field in which machine learning implementations are trained, optimized, evaluated, and exploited through an intuitive humancomputer interface. In this paper, we introduce interactive machine learning and explain its advantages and limitations within the context of defense applications. Furthermore, we address several of the shortcomings of interactive machine learning by discussing how cognitive feed-back may inform features, data, and results in the state of the art. We define the three techniques by which cognitive feedback may be employed: self reporting, implicit cognitive feedback, and modeled cognitive feedback. The advantages and disadvantages of each technique are discussed.DISTRIBUTION STATEMENT A. Approved for public release: distribution unlimited.

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Simulating Human Detection of Phishing Websites: An Investigation into the Applicability of the ACT-R Cognitive Behaviour Architecture Model

Cited by 9 publications

References 9 publications

Detecting Phishing sites Without Visiting them

Detecting Phishing sites Without Visiting them

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

On Interactive Machine Learning and the Potential of Cognitive Feedback

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