Predicting phishing websites based on selfstructuring neural network

Predicting phishing websites based on self structuring neural network

Abstract Internet has become an essential component of our everyday social and financial activities. Nevertheless, internet users may be vulnerable to different types of web threats, which may cause financial damages, identity theft, loss of private information, brand reputation damage and loss of customer’s confidence in e-commerce and online banking. Phishing is considered as a form of web threats that is defined as the art of impersonating a website of an honest enterprise aiming to obtain confidential information such as usernames, passwords and social security number. So far, there is no single solution that can capture every phishing attack. In this article, we proposed an intelligent model for predicting phishing attacks based on artificial neural network particularly self-structuring neural networks. Phishing is a continuous problem where features significant in determining the type of web pages are constantly changing. Thus, we need to constantly improve the network structure in order to cope with these changes. Our model solves this problem by automating the process of structuring the network and shows high acceptance for noisy data, fault tolerance and high prediction accuracy. Several experiments were conducted in our research, and the number of epochs differs in each experiment. From the results, we find that all produced structures have high generalization ability.

Keywords Web threat Phishing Information security Neural network Data mining

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MAC: A Multiclass Associative Classification Algorithm

MAC: A Multiclass Associative Classification Algorithm

Abstract - Associative classification (AC) is a data mining approach that uses association rule discovery methods to build classification systems (classifiers). Several research studies reveal that AC normally generates higher accurate classifiers than classic classification data mining approaches such as rule induction, probabilistic and decision trees. This paper proposes a new multiclass AC algorithm called MAC. The proposed algorithm employs a novel method for building the classifier that normally reduces the resulting classifier size in order to enable end-user to more understand and maintain it. Experimentations against 19 di®erent data sets from the UCI data repository and using di®erent common AC and traditional learning approaches have been conducted with reference to classification accuracy and the number of rules derived. The results show that the proposed algorithm is able to derive higher predictive classifiers than rule induction (RIPPER) and decision tree (C4.5) algorithms and very competitive to a known AC algorithm named MCAR. Furthermore, MAC is also able to produce less number of rules than MCAR in normal circumstances (standard support and confidence thresholds) and in sever circumstances (low support and confidence thresholds) and for most of the data sets considered in the experiments. 

Keywords: Associative classification; associative rule; data mining; rule learning.

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ANALYTICAL MODELS BASED DISCRETE-TIME QUEUEING FOR THE CONGESTED NETWORK

ANALYTICAL MODELS BASED DISCRETE-TIME QUEUEING FOR THE CONGESTED NETWORK

Abstract - Congestion is one of the well-studied problems in computer networks, which occurs when the request for network resources exceeds the buffer capacity. Many active queue management techniques such as BLUE and RED have been proposed in the literature to control congestions in early stages. In this paper, we propose two discrete-time queueing network analytical models to drop the arrival packets in preliminary stages when the network becomes congested. The first model is based on Lambda Decreasing and it drops packets from a probability value to another higher value according to the buffer length. Whereas the second proposed model drops packets linearly based on the current queue length. We compare the performance of both our models with the original BLUE in order to decide which of these methods offers better quality of service. The comparison is done in terms of packet dropping probability, average queue length, throughput ratio, average queueing delay, and packet loss rate.

Keywords: Analytical models; congestion control; discrete-time queueing; simulation.
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