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IRJET- Phishing Website Detection System
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2636 Phishing Website Detection System Shaswat Kumar1, Musquan Karovalia2, Bhagyashree Prajapati3, Shraddha Kadam4, Aabha Patil5 1,2,3,4Student 5Assistant Proffesor 1,2,3,4,5Department of Computer Engineering, Shree L.R Tiwari College of Engineering,Mumbai University, India ----------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Phishing is an unlawful activity wherein people are misled into the wrong sites by using various fraudulent methods. The aim of these phishing websites is to confiscate personal information or other financial details for personal benefits or misuse. As technology advances, the phishing approaches used need to get progressed and there is a dire need for better security and better mechanisms to prevent as well as detect these phishing approaches. Theprimaryfocus of this paper is to put forth a model as a solution to detect using chrome extension. The chrome extension will help to keep the user secured as well as allows to give an alert message to the user. There are two major phases using chrome extension such as comparing the url with the phishtank database and comparison with the Indexed DB to give better results. Key Words: Malicious URL’s, Phishing, Web Security, Indexed DB, Chrome Extension . 1. INTRODUCTION Computer security is a vast fieldconsistingof manydifferent methodologies and technologies.Itdefends againsta variety of attack vectors, both internal and external. Phishing is a significant security threat to the internet, it is an electronic online identity theft in which the attackers use spoofing techniques like fake websites that mimic legal websites to trick users into revealing into their private information. As this global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. Over the years, phishing attacks grew in number and intensity too. Phishing attacks now target users ofonline banking,paymentservices such as PayPal, and online e-commerce sites. A Detection of Phishing Website Systemhasbeendeveloped. Our system uses a chrome extension which fetches the url provided by the user and compares it with the database which will perform a quick scan for existing URLs. If it concludes that the received url is malicious, it will change the color to red. If the tool concludes that the url is legit. It will let the user to open the webpage. This paper is organized into five sections. The first section gives a brief introduction about the system. The second section is the study of related existing systems. The third section details out the implementation of the system. The fourth section provides the results obtained. Finally the conclusion gives the summary and future scope about the system. 2. LITERATURE REVIEW In Literature review, we discuss about the variousaspects of the project by taking reference of the existing projects that are similar to the makers of this current project. [7]Hossein Shirazi, Kyle Haefner, Indrakshi Ray developeda framework, called "Fresh-Phish", for creating current machine learning data for phishing websites. Using 30 different website features using python, they built a large labeled dataset and analyzed several machine learning classifiers against this dataset to determine which is the most accurate. [8]Tara Baniya, Dipesh Gautam and Yoohwan Kim have performed this paperthatproposesacyberspacehasopened a new platform for criminal activitiesbecausepeoplehave to provide the most private information such as user name, password, credit card information, social security number. Paper was surveyed on various methods of cyber attacks, attempts to mitigate them, theirstrength and weakness.Also discussed on genericarchitectureofURLblacklistingthatare being used by various malware detection system. [9]Ankit Kumar Jain and B. B. Gupta Visual similaritiesbased techniques are very useful for detecting phishing websites looks very similar in appearance to its corresponding legitimate website to deceive users into believing that they are browsing the correct website. This utilise the feature set like text content, text format, HTML tags, Cascading Style Sheet (CSS), image, and so forth, to make the decision. These approaches compare the suspicious website with the corresponding legitimate website by using various features and if the similarity is greater than the predefined threshold value then it is declared phishing. Paper presents a comprehensive analysis of phishing attacks some of the recent visual similarity based approaches for phishing detection. Survey provides a better understanding of the problem, current solution space, and scope of future research to deal with phishingattacksefficientlyusingvisual similarity based approaches. [1]A detection technique for phishing websites is proposed by Abdulghani Ali Ahmed et al which examines Uniform Resources Locators (URLs) of suspected web pages as per five extracted features. Phishtank and Yahoo directory datasets are used to assess the accuracy of the results given by proposed solution. The final report thus establishes that the detection mechanism can detect various types of
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2637 phishing attacks without fail. However, there are still chances of receiving false alarms. 3. DATABASE. 3.1 PHISHTANK DATABASE. Phishtank Database is stored on the server side.Phishtank Database consists of all top phishysites.AsPhishtank shares its database by providing API key we have downloaded its data. We update the database frequently. We use this database to compare it with the URL fetched. 3.2 INDEXED DB IndexedDB is an asynchronous, transactional, key-value object store. Asynchronous means that IndexedDB won’t block the user interface. Transactional means that operations in IndexedDB are all-or-nothing. IndexedDB is stored on the client’s side,. A key value object store means that each record is an object, as opposed to a row. In a key- value object store, each record is a self-contained object. It may, but usually doesn’t have a relationship to records in another objectstore. Wecreatedthis databaseby monitoring the activity of the user with thefrequentlyusedwebsites and stored it in this database. The Indexed DB data is stored on the client side. 4. PROPOSED SYSTEM The Phishing Website Detection system notifies the user whether the site he or she is using is a phish ormalicioussite or not depending upon the match found in our database. Fig -1: System Architecture 5. IMPLEMENTATION OF THE SYSTEM Here we will discuss about how we implementedoursystem and is represented in a flowchart manner in Figure 1. Step 1: USER OPENS WEB BROWSER ANS VISITS WEBSITE In Fig. 1we can see that first the user opens web browser and visits any website. Step 2: CHROME EXTENSION We have created a chrome extension which will start its functioning after the user clicks on check button on the chrome extension. If the URL is a malicious URL, the chrome extension will change its color from yellow to red and if the URL is not a malicious URL, the chrome extension will change its color from yellow to green.
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2638 Step 3: FETCHES URL: When the user visits the website the chrome extension will fetch the url. Step 4: PHISH TANK DATABASE After the chrome extension fetches URL, it will be compared with the phish tank database. This database contains URL, creation date, etc of the phish or malicious websites. If the URL is found then the user will be notified by the chrome extension by change in the color from yellow to red. If the URL is not found it will be directed to Step 5. Step 5: INDEXED DB Indexed DB is a database stored at client side. We have created this database by monitoring the activity of the user. The sites which the user frequently uses is stored in this database. If the URL is not found in step 4,it will come to step 5 and check the URL here. If the match is found it will notify the user. 6. RESULTS Following is the list of few Phishy URL’s which we tested URL SYSTEM RESULT NETCRAFT RESULT https://sites.google.com/site/ freehabbocoinsgbbo00/ Detected Not Detected http://themedsmarket.ru/ Detected Detected http://www.tunga9.cl/cp/ Detected Detected http://princestudio.net/tiend a/libraries/joomla/BT/index. php Not Detected Detected http://www.tunga9.cl/cp/ Detected Detected http://www.gkjx168.com/im ages/ Detected Detected http://paypal.com.cg- bin.login.submited.elkklkdgh5 4m54k565vdflgqdfaziou4567 89.begiu.com/ Detected Not Detected http://jppost- afu.com:81/pp.html Detected Detected The above table contains only few URL’s thatwehavetested. We tested total 53 samples and the accuracy and the erroris as follows: Total samples tested =53 correctly classified samples = 49 Incorrect classified samples = 4 Accuracy = correctly classified samples/Total samples = 49/53 = 92.45% Error = Incorrectly classified samples/Total samples = 4/53 = 7.54% 7. CONCLUSION Phishing is the one the major cyber threats nowdays . Through this system we have developed a chromeextension which will fetch the URL and compare it with the existing database of system .The main aim of this system is to detect the phishing website and alert the user bychangingthecolor from yellow to red if it is a phishy website or changing color from yellow to green if it is not a phishy website so as to prevent the user from sharing or providing their sensitive information to the attackers. REFERENCES [1] P. Hayati and V. Potdar, "TowardSpam2.0:AnEvaluation of Web2.0 Anti-Spam Methods "In 7th IEEE International Conference onIndustrial Informatics Cardiff, Wales, 2009. [2] P. Hayati, K. Chai, V. Potdar, and A. Talevski,"HoneySpam 2.0:Profiling Web SpambotBehaviour,"In12thInternational Conference on Principles of Practise in Multi-AgentSystems, Nagoya, Japan, 2009, pp. 335-344.
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2639 [3] P. Szor. "The art of computervirusresearchanddefence." Addison-Wesley Professional, 2005. [4] P.N. Tan and V. Kumar, "Discoveryof WebRobot Sessions Based on their Navigational Patterns", Data Mining and Knowledge Discovery, vol. 6, pp. 9-35, 2002 [1] M. Khonji, Y. Iraqi, and A. Jones, “Phishing detection: literature survey,” IEEE Communications Surveys & Tutorials, vol. 15, no. 4, pp. 2091–2121, 2013. [2] R. Islam and J. Abawajy, “A multi-tier phishing detection and filtering approach,” Journal of Network and Computer Applications, vol. 36, no. 1, pp. 324–335, 2013. [3] A. K. Jain and B. B. Gupta, “Comparative analysis of features based machine learning approaches for phishing detection,” in Proceedings of the 10th INDIA-COM, New Delhi, India, 2016. [4] G. Weaver, A. Furr, and R. Norton, Deception of Phishing: Studying the Techniques of Social Engineering by Analyzing Modern-Day Phishing Attacks on Universities, 2016. [5] Kaspersky Lab, “Spam in January 2012 love, politics an sport,”2013,http://www.kaspersky.com/about/news/spam / 2012/Spam in January 2012 Love Politics and Sport. [6] APWG Q1-Q3 Report, 2015, http://docs.apwg.org/reports/ apwg trends report q1-q3 2015.pdf. [7] Hossein Shirazi, Kyle Haefner, Indrakshi Ray, “Fresh- Phish: A Framework for Auto-Detection of Phishing Websites”, 2017 IEEE International Conference on Information Reuse and Integration 978-1-5386-1562-1/17 $31.00 © 2017 IEEE DOI 10.1109/IRI.2017.40 [8] Tara Baniya, Dipesh Gautam, Yoohwan Kim, “Safeguarding Web Surfing with URL Blacklisting”, 2015 12th International Conference on Information Technology - New Generations 978-1-4799-8828-0/15 $31.00 © 2015 IEEE DOI 10.1109/ITNG.2015.30 [9] Eric Medvet, Engin Kirda, Christopher Kruegel, “Visual- Similarity-Based Phishing Detection”, Copyright 2008 ACM ISBN # 978-1-60558-241-2 ...$5.00 [10] Ahmed, Abdulghani Ali, and Nurul Amirah Abdullah. "Real timedetection of phishing websites." Information Technology, Electronics and Mobile Communication Conference (IEMCON), 7th Annual. IEEE,2016.
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