Phishing counter measures and their effectiveness – literature review

Purkait, Swapan

doi:10.1108/09685221211286548

Cited by 103 publications

(97 citation statements)

References 150 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Multi-channel authentication approach is focused on the prime motivation of phishers which is to obtain sensitive credentials of online users (Purkait, 2012). With such information leaked to phishers, online users can suffer damages ranging from financial losses to unhealthy disclosure of their secrets.…”

Section: Literature Reviewmentioning

confidence: 99%

A Lightweight Anti-Phishing Technique for Mobile Phone

Orunsolu¹,

Alaran²,

Adebayo³

et al. 2017

AIP

View full text Add to dashboard Cite

Mobile phones have become an essential device for accessing the web. This is due to the advantages of portability, lower cost and ease. However, the adoption of mobile phones for online activities is now being challenged by myriads of cybercrimes. One of such crimes is phishing attack. In this work, a lightweight anti-phishing technique is proposed to combat phishing attacks on mobile devices. This is necessary because these mobile platforms have increased the attack surface for phishers while diminishing the effectiveness of existing countermeasures. The proposed approach uses a number of URL behavior to determine the status of a website based on frequency analysis of extracted phishing features from PhishTank. To increase the detection power of unknown pattern, a machine learning algorithm called Support Vector Machine is adopted. The results indicated that the approach is very efficient against phishing sites with negligible false negatives.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A Lightweight Anti-Phishing Technique for Mobile Phone

Orunsolu¹,

Alaran²,

Adebayo³

et al. 2017

AIP

View full text Add to dashboard Cite

show abstract

“…However, we also present a brief overview of the other fields to provide background to our research. In [1], Purkait provided a comprehensive phishing detection literature which introduced a complete review about the phishing detection techniques. www.ijacsa.thesai.org Google Safe Browsing [10] utilizes a blacklist phishing detection approach.…”

Section: Related Workmentioning

confidence: 99%

“…Phishing is a sort of semantic attack and attacks are targeting social or financial achievements [1]. In phishing attack, attackers attempt to trick and take money from the users of the internet by sending them e-mails instead of using malware software.…”

Section: Introductionmentioning

confidence: 99%

“…For example, requesting the user to validate his bank account information by providing his bank information to be compromised. The growing complexity of these approaches makes it difficult to protect users from phishing attacks [1]. The Anti-Phishing Working Group (APWG) is a group which gathers the phishing data from several sources, stated that phishing attacks continue spreading, as there were 69,533 unique phishing websites counted in December 2016, 80% of theses phishing attacks were targeting the online payment divisions [2].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Phishing Websites Classification using Hybrid SVM and KNN Approach

Altaher¹

2017

ijacsa

View full text Add to dashboard Cite

Abstract-Phishing is a potential web threat that includes mimicking official websites to trick users by stealing their important information such as username and password related to financial systems. The attackers use social engineering techniques like email, SMS and malware to fraud the users. Due to the potential financial losses caused by phishing, it is essential to find effective approaches for phishing websites detection. This paper proposes a hybrid approach for classifying the websites as Phishing, Legitimate or Suspicious websites, the proposed approach intelligently combines the K-nearest neighbors (KNN) algorithm with the Support Vector Machine (SVM) algorithm in two stages. Firstly, the KNN was utilized as a robust to noisy data and effective classifier. Secondly, the SVM is employed as a powerful classifier. The proposed approach integrates the simplicity of KNN with the effectiveness of SVM. The experimental results show that the proposed hybrid approach achieved the highest accuracy of 90.04% when compared with other approaches.

show abstract

“…Many efforts have been made to overcome the phishing attacks through numerous phishing detecting approaches. Nevertheless, phishing has caused enormous money loss in the cyberspace over the past years, which has motivated researchers to seek effective phishing detection techniques that protect users' digital identity [1][2][3]. In general, phishing detection techniques fall into several categories due to the deployed scenarios of detection.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Feature Selection on Phish Website Detection

Zuhair¹,

Selmat²,

Salleh³

2015

ijacsa

View full text Add to dashboard Cite

Abstract-Recently, limited anti-phishing campaigns have given phishers more possibilities to bypass through their advanced deceptions. Moreover, failure to devise appropriate classification techniques to effectively identify these deceptions has degraded the detection of phishing websites. Consequently, exploiting as new; few; predictive; and effective features as possible has emerged as a key challenge to keep the detection resilient. Thus, some prior works had been carried out to investigate and apply certain selected methods to develop their own classification techniques. However, no study had generally agreed on which feature selection method that could be employed as the best assistant to enhance the classification performance. Hence, this study empirically examined these methods and their effects on classification performance. Furthermore, it recommends some promoting criteria to assess their outcomes and offers contribution on the problem at hand. Hybrid features, low and high dimensional datasets, different feature selection methods, and classification models were examined in this study. As a result, the findings displayed notably improved detection precision with low latency, as well as noteworthy gains in robustness and prediction susceptibilities. Although selecting an ideal feature subset was a challenging task, the findings retrieved from this study had provided the most advantageous feature subset as possible for robust selection and effective classification in the phishing detection domain.

show abstract

Phishing counter measures and their effectiveness – literature review

Cited by 103 publications

References 150 publications

A Lightweight Anti-Phishing Technique for Mobile Phone

A Lightweight Anti-Phishing Technique for Mobile Phone

Phishing Websites Classification using Hybrid SVM and KNN Approach

The Effect of Feature Selection on Phish Website Detection

Contact Info

Product

Resources

About