A Review of Client-Side Toolbars as a User-Oriented Anti-Phishing Solution

Al-Daeef, Melad Mohamed; Basir, Nurlida; Saudi, Madihah Mohd

doi:10.1007/978-3-319-24584-3_36

Cited by 6 publications

(8 citation statements)

References 12 publications

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“…This is also shown in Tab. 6 where we see that features from f 2 and f 3 have the lowest average weight. On the other hand, features form f 4 and f 5 have the highest impact on classification with an average weight of 0.0084 and 0.0139 per feature respectively.…”

Section: Feature Analysismentioning

confidence: 92%

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Off-the-Hook: An Efficient and Usable Client-Side Phishing Prevention Application

Marchal

Armano²,

Gröndahl

et al. 2017

IEEE Trans. Comput.

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Abstract-Phishing is a major problem on the Web. Despite the significant attention it has received over the years, there has been no definitive solution. While the state-of-the-art solutions have reasonably good performance, they suffer from several drawbacks including potential to compromise user privacy, difficulty of detecting phishing websites whose content change dynamically, and reliance on features that are too dependent on the training data. To address these limitations we present a new approach for detecting phishing webpages in real-time as they are visited by a browser. It relies on modeling inherent phisher limitations stemming from the constraints they face while building a webpage. Consequently, the implementation of our approach, Off-the-Hook, exhibits several notable properties including high accuracy, brand-independence and good language-independence, speed of decision, resilience to dynamic phish and resilience to evolution in phishing techniques. Off-the-Hook is implemented as a fully-client-side browser add-on, which preserves user privacy. In addition, Off-the-Hook identifies the target website that a phishing webpage is attempting to mimic and includes this target in its warning. We evaluated Off-the-Hook in two different user studies. Our results show that users prefer Off-the-Hook warnings to Firefox warnings.

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Section: Feature Analysismentioning

confidence: 92%

“…In addition, users must share their browsing history with these centralized services thereby compromising their privacy. These concerns are partially addressed by real-time client-side solutions, but existing clientside solutions typically have low detection accuracy [6].…”

Section: Introductionmentioning

confidence: 99%

Off-the-Hook: An Efficient and Usable Client-Side Phishing Prevention Application

Marchal

Armano²,

Gröndahl

et al. 2017

IEEE Trans. Comput.

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“…Detection of phishing website using C4.5 Data mining algorithm [18].In this paper analysis of C4.5 (J48) data mining algorithm was implemented through WEKA tool. The two Anti-phishing techniques which were used are [19]. 1.…”

Section: IImentioning

confidence: 99%

“…There were around 300 websites for testing among them 200 were phishing websites 154 which were predicted as phishing and amongst 100 legitimate 94 were detected. The success rate was obtained to be 0.826 with an error of 0.173 and the accuracy which was trained with 750 instances was found to be 82.6%.Different systems in heuristic based approach such as PhishZoo [22], PhishNet and LinkGuard [19] were proposed to detect phishing websites. Phishing websites classification using Association classification (PWCAC) [23].In this research new algorithm was created named PWCAC Phishing website classification using association classification for the detection of phishing websites.…”

Section: Learning Phase 2 Prediction Phasementioning

confidence: 99%

A Survey on Phishing Detection and The Importance of Feature Selection In Data Mining Classification Algorithms

2020

JST

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: In this era of Internet, the issue of security of information is at its peak. One of the main threats in this cyber world is phishing attacks which is an email or website fraud method that targets the genuine webpage or an email and hacks it without the consent of the end user. There are various techniques which help to classify whether the website or an email is legitimate or fake. The major contributors in the process of detection of these phishing frauds include the classification algorithms, feature selection techniques or dataset preparation methods and the feature extraction that plays an important role in detection as well as in prevention of these attacks. This Survey Paper studies the effect of all these contributors and the approaches that are applied in the study conducted on the recent papers. Some of the classification algorithms that are implemented includes Decision tree, Random Forest , Support Vector Machines, Logistic Regression , Lazy K Star, Naive Bayes and J48 etc.

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“…Blacklist and whitelist techniques have also been employed to no avail, because attackers keep on changing mobile numbers every now and then. Furthermore, blacklist and whitelist datasets are incapable of detecting zero-hour attacks and quickly become overpopulated and obsolete [26]. User awareness programs on security good practice have not produced the desired results and are unlikely to reduce this vulnerability to zero [27].…”

Section: Introductionmentioning

confidence: 99%

Classifying Swahili Smishing Attacks for Mobile Money Users: A Machine-Learning Approach

2022

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A Review of Client-Side Toolbars as a User-Oriented Anti-Phishing Solution

Cited by 6 publications

References 12 publications

Off-the-Hook: An Efficient and Usable Client-Side Phishing Prevention Application

Off-the-Hook: An Efficient and Usable Client-Side Phishing Prevention Application

A Survey on Phishing Detection and The Importance of Feature Selection In Data Mining Classification Algorithms

Classifying Swahili Smishing Attacks for Mobile Money Users: A Machine-Learning Approach

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