Real time detection of phishing websites

Ahmed, Abdulghani Ali; Abdullah, Nurul Amirah

doi:10.1109/iemcon.2016.7746247

Cited by 51 publications

(21 citation statements)

References 5 publications

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“…Given that we wanted to limit our scope to heuristics that could be programmed using JavaScript and would give the desired results, CyberWarner implemented the following heuristics based on our literature review of URL heuristics [61][62][63][64][65][66][67][68]:…”

Section: Security Threat/cue: Potential Phishing Attacksmentioning

confidence: 99%

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Warning users about cyber threats through sounds

et al. 2021

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This paper reports a formative evaluation of auditory representations of cyber security threat indicators and cues, referred to as sonifications, to warn users about cyber threats. Most Internet browsers provide visual cues and textual warnings to help users identify when they are at risk. Although these alarming mechanisms are very effective in informing users, there are certain situations and circumstances where these alarming techniques are unsuccessful in drawing the user’s attention: (1) security warnings and features (e.g., blocking out malicious Websites) might overwhelm a typical Internet user and thus the users may overlook or ignore visual and textual warnings and, as a result, they might be targeted, (2) these visual cues are inaccessible to certain users such as those with visual impairments. This work is motivated by our previous work of the use of sonification of security warnings to users who are visually impaired. To investigate the usefulness of sonification in general security settings, this work uses real Websites instead of simulated Web applications with sighted participants. The study targets sonification for three different types of security threats: (1) phishing, (2) malware downloading, and (3) form filling. The results show that on average 58% of the participants were able to correctly remember what the sonification conveyed. Additionally, about 73% of the participants were able to correctly identify the threat that the sonification represented while performing tasks using real Websites. Furthermore, the paper introduces “CyberWarner”, a sonification sandbox that can be installed on the Google Chrome browser to enable auditory representations of certain security threats and cues that are designed based on several URL heuristics. Article highlights It is feasible to develop sonified cyber security threat indicators that users intuitively understand with minimal experience and training. Users are more cautious about malicious activities in general. However, when navigating real Websites, they are less informed. This might be due to the appearance of the navigating Websites or the overwhelming issues when performing tasks. Participants’ qualitative responses indicate that even when they did not remember what the sonification conveyed, the sonification was able to capture the user’s attention and take safe actions in response.

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Section: Security Threat/cue: Potential Phishing Attacksmentioning

confidence: 99%

“…Length of the URL URL length of more than 75 characters is considered to be suspicious. According to a study, normal length of the URL should not exceed 54 characters [61]. In order to reduce the number of possible false positives, we consider 75 characters.…”

Section: Security Threat/cue: Potential Phishing Attacksmentioning

confidence: 99%

Warning users about cyber threats through sounds

et al. 2021

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“…In [27] a comprehensive survey and a structural understanding of malicious URL detection techniques using ML is presented. 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.…”

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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“…RESULTS This paper [15] focuses on detecting phishing website URLs with domain name features. Web spoofing attack categories content-based, heuristic-based and blacklist-based approaches [8] [17] are explained and the proposed model PhishChecker is developed with the help of Microsoft Visual Studio Express 2013 and C# language [15]. Dataset used from Phishtank and Yahoo directory set and obtained an accuracy of 96%.…”

Section: Literary Reviewmentioning

confidence: 99%

Phishing Websites Detection using Machine Learning

2019

IJRTE

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Phishing is a common attack on credulous people by making them to disclose their unique information using counterfeit websites. The objective of phishing website URLs is to purloin the personal information like user name, passwords and online banking transactions. Phishers use the websites which are visually and semantically similar to those real websites. As technology continues to grow, phishing techniques started to progress rapidly and this needs to be prevented by using anti-phishing mechanisms to detect phishing. Machine learning is a powerful tool used to strive against phishing attacks. This paper surveys the features used for detection and detection techniques using machine learning

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Real time detection of phishing websites

Cited by 51 publications

References 5 publications

Warning users about cyber threats through sounds

Warning users about cyber threats through sounds

Knowledge-Based Approach to Detect Potentially Risky Websites

Phishing Websites Detection using Machine Learning

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