BaitAlarm: Detecting Phishing Sites Using Similarity in Fundamental Visual Features

Mao, Jian; Li, Pei; Li, Kun; Wei, Tao; Liang, Zhenkai

doi:10.1109/incos.2013.151

Cited by 47 publications

(20 citation statements)

References 9 publications

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“…To control phishing emails successfully, there were various solutions proposed as discussed in [19][20][21][22][23][24][25][26]. Similarly, to control phishing websites attacks, there were also various solutions proposed in [16][17][18][27][28][29][30][31][32][33][34][35][36][37][38][39]. Phishing email is not the only way to fraud, but phishers also uses fake websites and phishing emails have also the link of fake websites.…”

Section: Current Issues and Challengesmentioning

confidence: 99%

Defending against phishing attacks: taxonomy of methods, current issues and future directions

2017

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Internet technology is so pervasive today, for example, from online social networking to online banking, it has made people's lives more comfortable. Due the growth of Internet technology, security threats to systems and networks are relentlessly inventive. One such a serious threat is "phishing", in which, attackers attempt to steal the user's credentials using fake emails or websites or both. It is true that both industry and academia are working hard to develop solutions to combat against phishing threats. It is therefore very important that organisations to pay attention to end-user awareness in phishing threat prevention.Therefore, aim of our paper is twofold. First, we will discuss the history of phishing attacks and the attackers' motivation in details. Then, we will provide taxonomy of various types of phishing attacks. Second, we will provide taxonomy of various solutions proposed in literature to protect users from phishing based on the attacks identified in our taxonomy. We conclude our paper discussing various issues and challenges that still exist in the literature, which are important to fight against with phishing threats.

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Section: Current Issues and Challengesmentioning

confidence: 99%

Defending against phishing attacks: taxonomy of methods, current issues and future directions

2017

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“…BaitAlarm [15] utilizes the visual features contrast to categorize legitimate and phishing websites. Phishing attacks usually employ similar designs to reproduce the layout of the real website so authors employed Cascading Style Sheets (CSS) for classifying phishing websites.…”

Section: Related Workmentioning

confidence: 99%

Phishing Websites Classification using Hybrid SVM and KNN Approach

Altaher¹

2017

ijacsa

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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.

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“…BaitAlarm [17] compares visual features to group legitimate and phishing. Phishers must utilize same styles to mimic the illustrations of legitimate site thus authors considered Cascading Style Sheets (CSS) for identifying phishing sites.…”

Section: Imentioning

confidence: 99%