Suspicious URL Filtering Based on Logistic Regression with Multi-view Analysis

Su, Kuan-Wei; Wu, Ke; Lee, Hahn-Ming; Wei, Te-En

doi:10.1109/asiajcis.2013.19

Cited by 11 publications

(17 citation statements)

References 10 publications

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“…, [29], [67], [70], [77], [78], [85] ≥4 5 [21], [41], [49], [89], [68] We next describe the different evaluation metrics used in phishing URL detection papers.…”

Section: ) Dataset Sources and Availability: Inmentioning

confidence: 99%

“…Recall F-score 20 [30], [33]- [35], [47], [58]- [60], [69], [86] [22], [29], [50]- [52], [62]- [64], [70], [89] Error Rate 13 [21], [28], [34], [39], [40], [43], [49] [38], [65], [67], [73], [85], [89] Confusion Matrix a 20 [35], [43]- [46], [48], [57], [69], [76], [80], [82] [29], [50], [51], [58], [66], [78], [79], [88], [89] Area Under Curve 4 [33], [35], [50], [51] Others b 6 [42], [57],…”

Section: ] Precisionmentioning

confidence: 99%

“…b Balanced Success Rate [38], Root Mean Square Error [42], effective rules/patterns [54], Cost/Sum of classification [80], False Alarm rate [78] [30], [34], [41], [64], [77], [79] [21], [35], [44], [49]- [52], [59], [62] Bayesian Classifier 1 [34] Logistic Regression 11 [35], [43], [44], [47], [67], [79] [21], [28], [49], [63], [81] Naïve Bayes 11 [35], [43], [44], [52], [63], [77] [21], [30], [41], [49], [62] Support Vector Machines 12 [21], [40], [43], [49]- [51], [77] [28], [29], [41], [63], [66] RandomForest 14 [21], [41]<...>…”

Section: ] Precisionmentioning

confidence: 99%

“…Frequency of Tokens 12 U,W [28], [35], [36], [38], [45], [46], [52], [65], [66], [80], [81], uW: [94] Longest Token 6 U [35], [36], [44], [45], [66], [ [34], [35], [76], [80], [85], uW: [127], [132] Port presence 18 U,W,E [36], [38], [40], [49], [58], [67], [70] uW: [96], [126], [144], [152], [154], [158], uE: [167], [168], [170], [178], [180] HTTPS or HTTP 15 U,W [38], [54], [65], uW: [96], [98], [102], [105],…”

Section: Dns Basedmentioning

confidence: 99%

See 3 more Smart Citations

SoK: A Comprehensive Reexamination of Phishing Research From the Security Perspective

Das

Baki

Aassal

et al. 2020

IEEE Commun. Surv. Tutorials

101

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Phishing and spear phishing are typical examples of masquerade attacks since trust is built up through impersonation for the attack to succeed. Given the prevalence of these attacks, considerable research has been conducted on these problems along multiple dimensions. We reexamine the existing research on phishing and spear phishing from the perspective of the unique needs of the security domain, which we call security challenges: real-time detection, active attacker, dataset quality and baserate fallacy. We explain these challenges and then survey the existing phishing/spear phishing solutions in their light. This viewpoint consolidates the literature and illuminates several opportunities for improving existing solutions. We organize the existing literature based on detection techniques for different attack vectors (e.g., URLs, websites, emails) along with studies on user awareness. For detection techniques we examine properties of the dataset, feature extraction, detection algorithms used, and performance evaluation metrics. This work can help guide the development of more effective defenses for phishing, spear phishing and email masquerade attacks of the future, as well as provide a framework for a thorough evaluation and comparison.

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“…, [29], [67], [70], [77], [78], [85] ≥4 5 [21], [41], [49], [89], [68] We next describe the different evaluation metrics used in phishing URL detection papers.…”

Section: ) Dataset Sources and Availability: Inmentioning

confidence: 99%

Section: ] Precisionmentioning

confidence: 99%

Section: ] Precisionmentioning

confidence: 99%

Section: Dns Basedmentioning

confidence: 99%

See 2 more Smart Citations

SoK: A Comprehensive Reexamination of Phishing Research From the Security Perspective

Das

Baki

Aassal

et al. 2020

IEEE Commun. Surv. Tutorials

101

View full text Add to dashboard Cite

show abstract

“…Su et al described a method that intends to learn the properties from multiple portions of URLs to assign the suspicion level of each portion. After the adjustment of suspicion threshold of each portion, the system would choose the most suspicious URL [11].…”

Section: Existing Approachesmentioning

confidence: 99%

Phishing URL Detection: A Machine Learning and Web Mining-based Approach

Sananse¹,

Sarode²

2015

IJCA

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There has been an abrupt development and use of online transactions over the past decade. The increased sophistication of cyber criminals has lead to proliferation of phishing attacks. The continuous expansion of World Wide Web has led to the rapid spread of phishing, malware and spamming. This paper proposes a feature based approach to classify URLs into phishing or non-phishing category. The usage of a variety of URL features is done by studying the anatomy of URLs. For classification of URLs, two different algorithms have been used. Random Forest machine learning algorithm is used to build an efficient classifier which would decide whether a given URL is phishing or not. In addition, a novel scheme has been proposed to detect phishing URLs by mining the publicly available content on the URLs.

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