Phishing prediction in e-banking using data mining techniques

Phishing is a form of electronic identity stealing in which a mixture of social engineering and web site spoofing techniques is used to trap a user into useful confidential information with financially viable value in e-banking. In detecting and identifying e banking phishing websites, Classification Data Mining (DM) Techniques can be a very useful tool. In this paper, we considered and implemented six different classification algorithm and techniques to extract the phishing training data sets criteria to classify their legitimacy in e-banking. We also compared their performances, accuracy, number of rules generated and speed. The experimental results demonstrated the feasibility of using Associative Classification techniques in real e-banking applications and its better performance as compared to other traditional classification algorithms. We present a novel approach to overcome the difficulty and complexity in detecting and predicting ebanking phishing website. We proposed an intelligent resilient and effective model that is based on using association and classification Data Mining algorithms. These algorithms were used to characterize and identify all the factors and rules in order to classify the phishing website and the relationship that correlate them with each other. The rules generated from the associative classification model showed the relationship between some important characteristics like URL and Domain Identity and Security and Encryption criteria in the final phishing detection rate

Phishing attack prediction by smart mobile devices

In recent times, phishing is a wide spread technique to steal user’s authentication information, especially password. The key issue is that it is difficult for user to differentiate fake Login User Interface from normal login. This paper presents a unique method to predict phishing by smart device. In our technique, a smart device pre-stores feature information of Login User Interface. Before entering authentication information, a plug-in of Web browser at host side will verify the validation of Login Inter face according to pre-stored Login Interface information. Wi-Fi provides a communication channel between the plug-in and the smart device. Furthermore, the smart device can automatically fill the field of user id and password to the Login User Interface if Login User Interface passes the verification of smart mobile device. Compared with other solutions, this solution cans greatly improve the security of authentication

Dynamic cluster head routing protocol in wireless sensor network

Wireless distributed based microsensor systems will have reliable monitoring in variety of environments for both civil and military applications. In this research work, we look at communication protocols, which can have significant impact on the overall energy dissipation of the networks in these systems. Based on the findings that the conventional protocols of direct transmission, minimum-transmission-energy, multihop routing, and static clustering may not be optimal for sensor networks.Therefore we propose DCHRP (Dynamic Cluster Routing Protocol), a cluster-based protocol that utilizes instance cluster creation to evenly distribute the energy load among the sensors in the network. DCHRP uses instance clusters to enable scalability and strength for dynamic networks. In addition to this, DCHRP is able to reduce the energy wastage evenly among the sensors, and allowing easy dynamicity in WSNs