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Application of Rule based Fuzzy Inference System in Prediction of Internet Phishing

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Shefali Paliwal, Darpan Anand, Sartaj Khan
10.5120/ijca2016911334

Shefali Paliwal, Darpan Anand and Sartaj Khan. Application of Rule based Fuzzy Inference System in Prediction of Internet Phishing. International Journal of Computer Applications 148(14):30-37, August 2016. BibTeX

@article{10.5120/ijca2016911334,
	author = {Shefali Paliwal and Darpan Anand and Sartaj Khan},
	title = {Application of Rule based Fuzzy Inference System in Prediction of Internet Phishing},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2016},
	volume = {148},
	number = {14},
	month = {Aug},
	year = {2016},
	issn = {0975-8887},
	pages = {30-37},
	numpages = {8},
	url = {http://www.ijcaonline.org/archives/volume148/number14/25843-2016911334},
	doi = {10.5120/ijca2016911334},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Over the years many cases of internet frauds have increased and phishing is one of the techniques used by hackers to execute the frauds through internet. Many tools and techniques have been designed to detect phishing attacks and to prevent them. Phishers may have a ton of methodologies and strategies to lead an all-around composed phishing assault and thus cause access the legitimate information. The objectives of the phishing assaults, are principally on-line managing an account customers, banking customers and payment services, etc. the companies indulged in these services are confronting significant money related misfortune and absence of trust in Internet-based administrations.

Keeping in mind the end goal to beat these, there is a critical need to discover answers for battle phishing assaults. Recognizing a phishing site is a very difficult task and thus requires master learning and experience. Thus, there must be some easy ways to deal with phishing attacks. Different arrangements, design and tools have been proposed and created to address the issues of phishing attacks. The majority of these methodologies are not ready to settle on a choice progressively on whether the site is truth be told phished, and thus raising the counts of false positives. This is principally because of the limitations of the beforehand proposed approaches, which includes depending just on black and white list database, missing of human insight and specialists, poor versatility and their opportuneness.

In this work developed an intelligent phishing system by using fuzzy-based fuzzy inference system. It use UCI machine data to test inference system and found satisfactory results further it compares phishing detection system with fuzzy logic with other algorithms like J48, naïve Bayes classifier and Neuro-fuzzy based phishing detection system. Thus, the objective of this work is proposing an efficient non algorithmic anti- phishing system.

References

  1. Abu-Nimeh, S., Nappa, D., Wang, X., and Nair, S. (2007) A comparison of machine learning techniques for phishing detection. In eCrime ’07: Proceedings of the anti-phishing working group’s 2nd annual eCrime researchers summit, (pp. 60–69), New York, NY, USA, ACM. .
  2. APWG, (2014) Phishing Activity Trends Report, http://antiphishing.org/reports/apwg_report_sep2014_final.pdf..
  3. Brooks, J. (2006) Anti-Phishing Best Practices: Keys to aggressively and effectively protecting your organization from Phishing Attacks, White Paper, Cyveillance.
  4. Buckley, J., and Tucker, D. (1989) Second generation fuzzy expert system. Fuzzy Sets and Systems, Vol.31, No.4, (pp. 271-284).
  5. Business Security Guidance, (2006) How to Protect Insiders from Social Engineering Threats, www.microsoft.com/technet/security/default.mspx,
  6. Chandrasekaran, M., Narayanan, K., and Upadhyaya, S. (2006) Phishing email detection based on structural properties. Proceedings of the NYS Cyber Security Conference.
  7. Dhamija, R., and Tygar, J. (2005) The battle against phishing: Dynamic security skins. In Proc. ACM Symposium on Usable Security and Privacy (SOUPS 2005), (pp. 77– 88). Dhamija, R., Tygar, J., and Marti, H. (2006) Why phishing works, In CHI '06: Proceedings of the SIGCHI conference on Human Factors in computing systems, ACM Press, (pp. 581-590), New York, NY, USA.
  8. Dhamija, R., Tygar, J., and Marti, H. (2006) Why phishing works, In CHI '06: Proceedings of the SIGCHI conference on Human Factors in computing systems, ACM Press, (pp. 581-590), New York, NY, USA.
  9. Emigh, A. (2006) Online Identity Theft: PhishingTechnology, Chokepoints andCountermeasures. http://www.antiphishing.org/Phishing-dhs-report.pdf, Access date

Keywords

Fuzzy Logic, Neural network, inference engine