Designing a Mobile Game for Home Computer Users to Protect Against “Phishing Attacks”

Hirumi, Atsusi

doi:10.20533/ijels.2046.4568.2011.0003

Cited by 32 publications

(12 citation statements)

References 31 publications

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“…Similar study was also conducted at the Indiana University [45]. Arachchilage and Cole [46] designed an educational mobile game for home computers to protect users from phishing attacks. The game was designed to educate users to recognize phishing URLs.…”

Section: User Educationmentioning

confidence: 92%

phishGILLNET—phishing detection methodology using probabilistic latent semantic analysis, AdaBoost, and co-training

Ramanathan

Wechsler

2012

EURASIP J. on Info. Security

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Identity theft is one of the most profitable crimes committed by felons. In the cyber space, this is commonly achieved using phishing. We propose here robust server side methodology to detect phishing attacks, called phishGILLNET, which incorporates the power of natural language processing and machine learning techniques. phishGILLNET is a multi-layered approach to detect phishing attacks. The first layer (phishGILLNET1) employs Probabilistic Latent Semantic Analysis (PLSA) to build a topic model. The topic model handles synonym (multiple words with similar meaning), polysemy (words with multiple meanings), and other linguistic variations found in phishing. Intentional misspelled words found in phishing are handled using Levenshtein editing and Google APIs for correction. Based on term document frequency matrix as input PLSA finds phishing and non-phishing topics using tempered expectation maximization. The performance of phishGILLNET1 is evaluated using PLSA fold in technique and the classification is achieved using Fisher similarity. The second layer of phishGILLNET (phishGILLNET2) employs AdaBoost to build a robust classifier. Using probability distributions of the best PLSA topics as features the classifier is built using AdaBoost. The third layer (phishGILLNET3) further expands phishGILLNET2 by building a classifier from labeled and unlabeled examples by employing Co-Training. Experiments were conducted using one of the largest public corpus of email data containing 400,000 emails. Results show that phishGILLNET3 outperforms state of the art phishing detection methods and achieves F-measure of 100%. Moreover, phishGILLNET3 requires only a small percentage (10%) of data be annotated thus saving significant time, labor, and avoiding errors incurred in human annotation.

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Section: User Educationmentioning

confidence: 92%

phishGILLNET—phishing detection methodology using probabilistic latent semantic analysis, AdaBoost, and co-training

Ramanathan

Wechsler

2012

EURASIP J. on Info. Security

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show abstract

“…All Arachchilage and Cole [11,12] designed a mobile game design prototype as an educational tool to teach home computer users to protect themselves against phishing attacks. Their research proposed a mobile game design for learning, based on a story, which simplifies and exaggerates real life situations.…”

Section: Related Workmentioning

confidence: 99%

“…Once the developer has identified the salient issues, they are faced with second question, what principles should guide the structure of this information. A theoretical model derived from Technology Threat Avoidance Theory (TTAT) was used to address those mobile game design issues and the mobile game design principles were used as a set of guidelines for structuring and presenting information in the mobile game design context [6,11]. The objective of their anti-phishing mobile game design was to teach the user how to identify phishing URLs and emails, which is one of many ways to identify a phishing attack.…”

Section: Related Workmentioning

confidence: 99%

“…Alternatively, when a user is presented with a portion of a phishing email, addressing "Dear Valued Customer" this can also be used to determine whether or not they have understood the concept of phishing email. Therefore, this research attempts to extend Arachchilage and Cole's [11,12] mobile game design by addressing conceptual knowledge of phishing URLs and emails to thwart phishing attacks.…”

Section: Related Workmentioning

confidence: 99%

“…The most significant feature of mobile environment is mobility itself such as mobility of the user, mobility of the device, and mobility of the service [11,22]. It enables users to be in contact while they are outside the reach of traditional communicational spaces.…”

Section: Introductionmentioning

confidence: 99%

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