AuntieTuna: Personalized Content-based Phishing Detection

Ardi, Calvin; Howse, John

doi:10.14722/usec.2016.23012

Cited by 22 publications

(7 citation statements)

References 19 publications

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“…If the number of chunks that matches the hashed blacklist content is greater than a threshold, it flags the webpage as phishing. This method provides good performance and zero false positive rate [7]. However, an attacker can simply use homographs (look-alike characters) or replace the content with images to bypass the detection method [7].…”

Section: Related Work a Mitigating Phishingmentioning

confidence: 99%

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Scam Pandemic: How Attackers Exploit Public Fear through Phishing

Bitaab¹,

Cho²,

Oest³

et al. 2021

Preprint

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As the COVID-19 pandemic started triggering widespread lockdowns across the globe, cybercriminals did not hesitate to take advantage of users' increased usage of the Internet and their reliance on it. In this paper, we carry out a comprehensive measurement study of online social engineering attacks in the early months of the pandemic. By collecting, synthesizing, and analyzing DNS records, TLS certificates, phishing URLs, phishing website source code, phishing emails, web traffic to phishing websites, news articles, and government announcements, we track trends of phishing activity between January and May 2020 and seek to understand the key implications of the underlying trends.We find that phishing attack traffic in March and April 2020 skyrocketed up to 220% of its pre-COVID-19 rate, far exceeding typical seasonal spikes. Attackers exploited victims' uncertainty and fear related to the pandemic through a variety of highly targeted scams, including emerging scam types against which current defenses are not sufficient as well as traditional phishing which outpaced the ecosystem's collective response.• Record-breaking attack volume. We observed that traffic to phishing websites reached record levels in March

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Section: Related Work a Mitigating Phishingmentioning

confidence: 99%

“…This method provides good performance and zero false positive rate [7]. However, an attacker can simply use homographs (look-alike characters) or replace the content with images to bypass the detection method [7].…”

Section: Related Work a Mitigating Phishingmentioning

confidence: 99%

Scam Pandemic: How Attackers Exploit Public Fear through Phishing

Bitaab¹,

Cho²,

Oest³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…(1) Based on type of approach used to mitigate phishing: There are four major approaches to mitigate phishing namely, content-based approaches (Zhang et al, 2007;Wardman and Warner, 2008;Wardman et al, 2011;Afroz and Greenstadt, 2011;Ardi and Heidemann, 2015;Jain and Gupta, 2017), heuristic approaches (Gastellier-Prevost et al, 2011;Nguyen et al, 2013;Rao and Ali, 2015), list-based approaches including black and white lists (Cao et al, 2008a;Sheng et al, , 2009;Prakash et al, , 2010;DNSBL: Spam Database Lookup) and machine learning approaches. Machine learning approaches have congregated wide popularity as they acquire prior knowledge to envisage the manifestations of phishing outbreaks in future.…”

Section: Related Workmentioning

confidence: 99%

Detection of phishing websites using a novel twofold ensemble model

Nagaraj¹,

Bhattacharjee

Sridhar³

et al. 2018

JSIT

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Purpose Phishing is one of the major threats affecting businesses worldwide in current times. Organizations and customers face the hazards arising out of phishing attacks because of anonymous access to vulnerable details. Such attacks often result in substantial financial losses. Thus, there is a need for effective intrusion detection techniques to identify and possibly nullify the effects of phishing. Classifying phishing and non-phishing web content is a critical task in information security protocols, and full-proof mechanisms have yet to be implemented in practice. The purpose of the current study is to present an ensemble machine learning model for classifying phishing websites. Design/methodology/approach A publicly available data set comprising 10,068 instances of phishing and legitimate websites was used to build the classifier model. Feature extraction was performed by deploying a group of methods, and relevant features extracted were used for building the model. A twofold ensemble learner was developed by integrating results from random forest (RF) classifier, fed into a feedforward neural network (NN). Performance of the ensemble classifier was validated using k-fold cross-validation. The twofold ensemble learner was implemented as a user-friendly, interactive decision support system for classifying websites as phishing or legitimate ones. Findings Experimental simulations were performed to access and compare the performance of the ensemble classifiers. The statistical tests estimated that RF_NN model gave superior performance with an accuracy of 93.41 per cent and minimal mean squared error of 0.000026. Research limitations/implications The research data set used in this study is publically available and easy to analyze. Comparative analysis with other real-time data sets of recent origin must be performed to ensure generalization of the model against various security breaches. Different variants of phishing threats must be detected rather than focusing particularly toward phishing website detection. Originality/value The twofold ensemble model is not applied for classification of phishing websites in any previous studies as per the knowledge of authors.

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“…The researchers in [28] developed an open-source plugin for the Chrome browser which is called AuntieTuna. The novel technique can automatically create personalized lists of candidate sites and check whether the sites are browsed by users.…”

Section: Anti Phishing Techniquementioning

confidence: 99%

Towards Evaluating Web Spam Threats and Countermeasures

Abuwardih¹

2018

ijacsa

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Web spam is a deceiving technique that aims to get high ranks for the retrieved web pages at the top Search Engine Result Pages (SERPs). This paper provides an evaluation for the web spam threats and countermeasures. It is started by presenting the different types of web spam threats that aim to deceive the users with incorrect information, distribute phishing and propagate malware. Then, presenting a detailed description for the proposed anti web spam tools, systems and countermeasures, and conducting a comparison between them. The results indicate that online real time tools are highly recommended solutions against web spam threats.

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AuntieTuna: Personalized Content-based Phishing Detection

Cited by 22 publications

References 19 publications

Scam Pandemic: How Attackers Exploit Public Fear through Phishing

Scam Pandemic: How Attackers Exploit Public Fear through Phishing

Detection of phishing websites using a novel twofold ensemble model

Towards Evaluating Web Spam Threats and Countermeasures

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