PhishTackle—a web services architecture for anti-phishing

Gowtham, R.; Krishnamurthi, Ilango

doi:10.1007/s10586-013-0320-5

Cited by 14 publications

(15 citation statements)

References 12 publications

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“…Real time 3 [118], [129], [148] 1 17 [98], [102], [106], [155]- [157], [121] [94], [99], [105], [132], [145]- [149], [ , [123], [140], [142] commonly used metrics, which are more suitable for balanced datasets. Among these two papers, only [136] actually used an unbalanced dataset.…”

Section: E Evaluation Metricsmentioning

confidence: 99%

“…Of the papers that use an unbalanced dataset, several did not use the appropriate metrics [93], [104], [118], [123], [125], [136], [148], [157]. The following nine reported the Confusion Matrix [88], [93], [94], [119], [121], [122], [130], [132], [156]. One reported AUC [104], and three reported both AUC and Confusion Matrix [98], [107], [113].…”

Section: E Evaluation Metricsmentioning

confidence: 99%

“…mentioned in the paper [80] [100], [112]- [114], [120]- [123], [156] [96], [132], [135], [141], [157], [158] [106], [144], [150], [152], [155] Naïve Bayes 12 [93], [101], [112], [141], [154], [156] [106], [118], [136], [148]- [150] Logistic Regression 14 [93], [96], [101], [112], [141], [150], [154]- [156] [106], [118], [120], [127], [129] Random Tree 2 [93], [151] Random Forest 7 [93], [101], [102], [106], [120], [148], [150] Decision Tree 14 [93], [101], [105],…”

Section: ] Precisionmentioning

confidence: 99%

“…Misspelled/Bad domain name 8 U [36], [42], [43], [56], [69], [72], [80], [89] Top Level Domain features 22 U,W,E [36], [38], [43], [46], [65], [69], [70], [78], [85], uW: [105], [107], [110], [113], [138], [139], [152], [154], [157], uE: [163], [170], [182], [194] TTL c value of DNS 9 U,W,E [28], [43], [47], [70], uW: [93], [102], [132], [144], uE: [186] Age of Domain 13 U,W,E [52], uW: [107], [113], [119], [120], [124], [125], [127], [132], [150],…”

Section: Dns Basedmentioning

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 4 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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