Parameter optimization for intelligent phishing detection using Adaptive Neuro-Fuzzy

Phishing attacks has been growing rapidly in the past few years. As a result, a number of approaches have been proposed to address the problem. Despite various approaches proposed such as feature-based and blacklist-based via machine learning techniques, there is still a lack of accuracy and real-time solution. Most approaches applying machine learning techniques requires that parameters are tuned to solve a problem, but parameters are difficult to tune to a desirable output. This study presents a parameter tuning framework, using adaptive Neuron-fuzzy inference system with comprehensive data to maximize systems performance. Extensive experiment was conducted. During ten-fold cross-validation, the data is split into training and testing pairs and parameters are set according to desirable output and have achieved 98.74% accuracy. Our results demonstrated higher performance compared to other results in the field. This paper contributes new comprehensive data, novel parameter tuning method and applied a new algorithm in a new field. The implication is that adaptive neuron-fuzzy system with effective data and proper parameter tuning can enhance system performance. The outcome will provide a new knowledge in the field

Predicting Phishing Websites using Neural Network trained with Back-Propagation

Phishing is increasing dramatically with the development of modern technologies and the global worldwide computer networks. This results in the loss of customer’s confidence in e-commerce and online banking, financial damages, and identity theft. Phishing is fraudulent effort aims to acquire sensitive information from users such as credit card credentials, and social security number. In this article, we propose a model for predicting phishing attacks based on Artificial Neural Network (ANN). A Feed Forward Neural Network trained by Back Propagation algorithm is developed to classify websites as phishing or legitimate. The suggested model shows high acceptance ability for noisy data, fault tolerance and high prediction accuracy with respect to false positive and false negative rates

Intelligent phishing detection parameter framework for E-banking transactions based on Neuro-fuzzy

Phishing attacks have become more sophisticated in web-based transactions. As a result, various solutions have been developed to tackle the problem. Such solutions including feature-based and blacklist-based approaches applying machine learning algorithms. However, there is still a lack of accuracy and real-time solution. Most machine learning algorithms are parameter driven, but the parameters are difficult to tune to a desirable output. In line with Jiang and Ma’s findings, this study presents a parameter tuning framework, using Neuron-fuzzy system with comprehensive features in order to maximize systems performance. The neuron-fuzzy system was chosen because it has ability to generate fuzzy rules by given features and to learn new features. Extensive experiments were conducted, using different feature-sets, two cross-validation methods, a hybrid method and different parameters and achieved 98.4% accuracy. Our results demonstrated a high performance compared to other results in the field. As a contribution, we introduced a novel parameter tuning framework based on a neuron-fuzzy with six feature-sets and identified different numbers of membership functions different number of epochs, different sizes of feature-sets on a single platform. Parameter tuning based on neuron-fuzzy system with comprehensive features can enhance system performance in real-time. The outcome will provide guidance to the researchers who are using similar techniques in the field. It will decrease difficulties and increase confidence in the process of tuning parameters on a given problem

An Introduction to Journal Phishings and Their Detection Approach

Nowadays, the most important risk and challenge in online system are online scam and phishing attacks. Phishing attacks have been always used to steal important information of users. In this kind of scam, attacker direct victim to fake pages using social engineering techniques, then, starts stealing users` important information such as passwords. In order to confronting these attacks, numerous techniques have been invented which have the ability to confront different kinds of these attacks. Our goal in this paper is to introducing new kind of phishing attacks which are not identifiable by techniques and methods which have been invented to confronting phishing attacks. Unlike other kinds of phishing attacks which target all kinds of users, researchers are the victims of these kinds of journal phishing attacks. Finally, we`ll introduce an approach based on classification algorithms to identify these kind of journal phishing attacks and then we`ll check our suggested approach in error rate

Detection Of Phishing Websites And Secure Transactions

Phishing is an electronic online identity theft in which the attackers use a combination of social engineering and web site spoofing techniques to trick a user into revealing confidential information. It steals the user’s personal identity data and financial credentials. Most of the phishing attacks emerge as spoofed E-Mails appearing as legitimate ones which makes the users to trust and divulge into them by clicking the link provided in the E-Mail. To detect a Phishing website, human experts compare the claimed identity of a website with features in the website. For example, human experts often compare the domain name in the URL against the claimed identity. Most legitimate websites have domain names that match their identities, while Phishing websites usually have less relevance between their domain names and their claimed (fake) identities. In addition to blacklists, white lists, heuristics, and classifications used in the state-of-the-art systems, we propose to consider websites’ identity claims. To enable secure transactions ,Password hashing has been done with MD5 hashing algorithms that strengthens web password authentication. It is also shown that getting original password from hashed form is not an easy task due to addition of salt value. If the user is valid, get a session key via mobile, through which further access can be don

Application Areas of Data Mining in Indian Retail Banking Sector

Banking systems collect huge amounts of data on day to day basis be it customer information transaction details risk profiles credit card details credit limit and collateral details compliance and Anti Money Laundering AML related information trade finance data SWIFT and telex messages Thousands of decisions are taken in a bank daily These decisions include credit decisions default decisions relationship start up investment decisions AML and Illegal financing related One needs to depend on various reports and drill down tools provided by the banking systems to arrive at these critical decisions But this is a manual process and is error prone and time consuming due to large volume of transactional and historical data Interesting patterns and knowledge can be mined from this huge volume of data that in turn can be used for this decision making process This article explores and reviews various data mining techniques that can be applied in banking areas It provides an overview of data mining techniques and procedures It also provides an insight into how these techniques can be used in banking areas to make the decision making process easier and productiv