Phishing Attack Detection Research Articles

Security Services Using Crowdsourcing

Security-as-a-service is an emerging area in cloud computing. Traditionally, security approaches are service provider-centric and provider-driven. In this paper, we propose a model for security-as-a-service using “crowdsourcing”. Though crowdsourcing has been used to provide specific security services like browser security, detecting phishing attacks, detecting cybersecurity threats, there has been no work which provides a unified framework to provide different types of security verification. Dispersed computing power of devices is used to perform security verifications. This is done by subscribers in a collaborative way, using their idle resources, in exchange of certain incentives. Our architecture guarantees anonymity of users who request service and the crowd who contribute in verification by using virtualization concepts and virtual machines. Moreover, we propose an approach for managing these security verification jobs, subscribers in a fault tolerant manner. To the best of our knowledge, we are the first to propose a unified security-as-a-service framework using crowdsourcing, thus introducing a new research problem. We discuss a number of applications, challenges and problems of crowdsourcing in security verification.

Detecting Phishing Attacks In Purchasing Process Through Proactive Approach

A Monitor is a software system that observes and analyzes the behavior of target system determining the quality of interest such as satisfaction of the target system. In the modern technology business processes are open and distributed which may lead to failure. Therefore monitoring is an important task for the services that comprise these processes. We are going to present a framework for multilevel monitoring of these service systems. The main objective of this project is monitoring the customer who purchases items from Merchant. Phishing is an online scam that attempts to defraud people of their personal information such as credit card or bank account information. We are going to detect, locate and remove the phishing E-mail. The customer details will be stored in web registry. We are going to demonstrate how the online business processes can be implemented with multiple scenarios that include monitoring open service policy commitments.

Trustworthiness testing of phishing websites: A behavior model-based approach

Phishing attacks allure website users to visit fake web pages and provide their personal information. However, testing of phishing websites is challenging. Unlike traditional web-based program testing, we do not know the response of form submissions in advance. There exists lack of efforts to help anti-phishing professionals who manually verify a reported phishing site and take further actions. Moreover, current tools cannot detect phishing attacks that leverage vulnerabilities in trusted websites such as cross site scripting. An attacker might generate input forms by injecting script code and steal credentials. To address these challenges, we propose11The preliminary version of this paper has been published in Shahriar and Zulkernine (2010) [44]. testing suspected phishing websites based on trustworthiness testing approach. In a trustworthiness testing, a website is not tested against a set of known inputs and matched the expected outputs with the actual ones. Rather, we check whether the behavior (response) of websites matches with our knowledge of phishing or legitimate website behaviors to decide whether a website is phishing or legitimate. We consider a suspected website as a web-based program and test the program based on a behavior model. The model is described using the notion of Finite State Machine (FSM) that captures the submission of forms with random inputs and the corresponding responses. We then identify a number of heuristics followed by a set of heuristic combination to assist a tester deciding whether websites are phishing or legitimate based on their up-to-date behaviors. We implement a tool named PhishTester to automate the testing process. We evaluate the proposed approach with both phishing and legitimate websites. The results show that the approach incurs zero false negatives and positives in detecting phishing and legitimate websites, respectively. Moreover, our approach can detect advanced XSS-based attacks that many contemporary tools currently fail to detect.