Profiling phishing activity based on hyperlinks extracted from phishing emails
- Authors: Yearwood, John , Mammadov, Musa , Webb, Dean
- Date: 2012
- Type: Text , Journal article
- Relation: Social Network Analysis and Mining Vol. 2, no. 1 (2012), p. 5-16
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- Description: Phishing activity has recently been focused on social networking sites as a more effective way of exploiting not only the technology but also the trust that may exist between members in a social network. In this paper, a novel method for profiling phishing activity from an analysis of phishing emails is proposed. Profiling is useful in determining the activity of an individual or a particular group of phishers. Work in the area of phishing is usually aimed at detection of phishing emails. In this paper, we concentrate on profiling as distinct from detection of phishing emails. We formulate the profiling problem as a multi-label classification problem using the hyperlinks in the phishing emails as features and structural properties of emails along with whois (i.e. DNS) information on hyperlinks as profile classes. Further, we generate profiles based on the classifier predictions. Thus, classes become elements of profiles. We employ a boosting algorithm (AdaBoost) as well as SVM to generate multi-label class predictions on three different datasets created from hyperlink information in phishing emails. These predictions are further utilized to generate complete profiles of these emails. Results show that profiling can be done with quite high accuracy using hyperlink information.
Solving systems of nonlinear equations using a globally convergent optimization algorithm
- Authors: Taheri, Sona , Mammadov, Musa
- Date: 2012
- Type: Text , Journal article
- Relation: Global Journal of Technology & Optimization Vol. 3, no. (2012), p. 132-138
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- Description: Solving systems of nonlinear equations is a relatively complicated problem for which a number of different approaches have been presented. In this paper, a new algorithm is proposed for the solutions of systems of nonlinear equations. This algorithm uses a combination of the gradient and the Newton’s methods. A novel dynamic combinatory is developed to determine the contribution of the methods in the combination. Also, by using some parameters in the proposed algorithm, this contribution is adjusted. We use the gradient method due to its global convergence property, and the Newton’s method to speed up the convergence rate. We consider two different combinations. In the first one, a step length is determined only along the gradient direction. The second one is finding a step length along both the gradient and the Newton’s directions. The performance of the proposed algorithm in comparison to the Newton’s method, the gradient method and an existing combination method is explored on several well known test problems in solving systems of nonlinear equations. The numerical results provide evidence that the proposed combination algorithm is generally more robust and efficient than other mentioned methods on someimportant and difficult problems.
Structure learning of Bayesian networks using a new unrestricted dependency algorithm
- Authors: Taheri, Sona , Mammadov, Musa
- Date: 2012
- Type: Text , Conference proceedings
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- Description: Bayesian Networks have deserved extensive attentions in data mining due to their efficiencies, and reasonable predictive accuracy. A Bayesian Network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency between two variables. Constructing a Bayesian Network from data is the learning process that is divided in two steps: learning structure and learning parameter. In many domains, the structure is not known a priori and must be inferred from data. This paper presents an iterative unrestricted dependency algorithm for learning structure of Bayesian Networks for binary classification problems. Numerical experiments are conducted on several real world data sets, where continuous features are discretized by applying two different methods. The performance of the proposed algorithm is compared with the Naive Bayes, the Tree Augmented Naive Bayes, and the k
The effect of regularization on drug-reaction relationships
- Authors: Mammadov, Musa , Zhao, L. , Zhang, Jianjun
- Date: 2012
- Type: Text , Journal article
- Relation: Optimization Vol. 61, no. 4 (2012), p. 405-422
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- Description: The least-squares method is a standard approach used in data fitting that has important applications in many areas in science and engineering including many finance problems. In the case when the problem under consideration involves large-scale sparse matrices regularization methods are used to obtain more stable solutions by relaxing the data fitting. In this article, a new regularization algorithm is introduced based on the Karush-Kuhn-Tucker conditions and the Fisher-Burmeister function. The Newton method is used for solving corresponding systems of equations. The advantages of the proposed method has been demonstrated in the establishment of drug-reaction relationships based on the Australian Adverse Drug Reaction Advisory Committee database. © 2012 Copyright Taylor and Francis Group, LLC.
Global asymptotic stability in a class of nonlinear differential delay equations
- Authors: Ivanov, Anatoli , Mammadov, Musa
- Date: 2011
- Type: Text , Journal article
- Relation: Discrete and Continuous Dynamical Systems Vol. 2011, no. Supplement 2011 (2011), p.
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- Description: An essentially nonlinear dierential equation with delay serving as a mathematical model of several applied problems is considered. Sufficient conditions for the global asymptotic stability of a unique equilibrium are de- rived. An application to a physiological model by M.C. Mackey is treated in detail.
- Description: 2003009358
Global Optimality Conditions and Optimization Methods for Quadratic Knapsack Problems
- Authors: Wu, Zhiyou , Yang, Y. J. , Bai, Fusheng , Mammadov, Musa
- Date: 2011
- Type: Text , Journal article
- Relation: Journal of Optimization Theory and Applications Vol. 151, no. 2 (2011), p. 241-259
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- Description: The quadratic knapsack problem (QKP) maximizes a quadratic objective function subject to a binary and linear capacity constraint. Due to its simple structure and challenging difficulty, it has been studied intensively during the last two decades. This paper first presents some global optimality conditions for (QKP), which include necessary conditions and sufficient conditions. Then a local optimization method for (QKP) is developed using the necessary global optimality condition. Finally a global optimization method for (QKP) is proposed based on the sufficient global optimality condition, the local optimization method and an auxiliary function. Several numerical examples are given to illustrate the efficiency of the presented optimization methods. © 2011 Springer Science+Business Media, LLC.
Optimality conditions in nonconvex optimization via weak subdifferentials
- Authors: Kasimbeyli, Refail , Mammadov, Musa
- Date: 2011
- Type: Text , Journal article
- Relation: Nonlinear Analysis, Theory, Methods and Applications Vol. 74, no. 7 (2011), p. 2534-2547
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- Description: In this paper we study optimality conditions for optimization problems described by a special class of directionally differentiable functions. The well-known necessary and sufficient optimality condition of nonsmooth convex optimization, given in the form of variational inequality, is generalized to the nonconvex case by using the notion of weak subdifferentials. The equivalent formulation of this condition in terms of weak subdifferentials and augmented normal cones is also presented. © 2011 Elsevier Ltd. All rights reserved.
Optimization of a quarter-car suspension model coupled with the driver biomechanical effects
- Authors: Kuznetsov, Alexey , Mammadov, Musa , Sultan, Ibrahim , Hajilarov, Eldar
- Date: 2011
- Type: Text , Journal article
- Relation: Journal of Sound and Vibration Vol. 330, no. 12 (2011), p. 2937-2946
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- Description: In this paper a Human–Vehicle–Road (HVR) model, comprising a quarter-car and a biomechanical representation of the driver, is employed for the analysis. Differential equations are provided to describe the motions of various masses under the influence of a harmonic road excitation. These equations are, subsequently, solved to obtain a closed form mathematical expression for the steady-state vertical acceleration measurable at the vehicle–human interface. The solution makes it possible to find optimal parameters for the vehicle suspension system with respect to a specified ride comfort level. The quantitative definition given in the ISO 2631 standard for the ride comfort level is adopted in this paper for the optimization procedure. Numerical examples, based on actually measured road profiles, are presented to prove the validity of the proposed approach and its suitability for the problem at hand.
Optimization of improved suspension system with inerter device of the quarter-car model in vibration analysis
- Authors: Kuznetsov, Alexey , Mammadov, Musa , Sultan, Ibrahim , Hajilarov, Eldar
- Date: 2011
- Type: Text , Journal article
- Relation: Archive of Applied Mechanics Vol. 81, no. 10 (2011), p. 1427-1437
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- Description: In this paper, we analyze an improved suspension system with the incorporated inerter device of the quarter-car model to obtain optimal design parameters for maximum comfort level for a driver and passengers. That is achieved by finding the analytical solution for the system of ordinary differential equations, which enables us to generate an optimization problem whose objective function is based on the international standards of admissible acceleration levels (ISO 2631-1, Mechanical Vibration and Shock-Evaluation of Human Exposure to Whole-Body Vibration-Part 1, 1997). The considered approach ensures the highest level of comfort for the driver and passengers due to a favorable reduction in body vibrations. Numerical examples, based on actually measured road profiles, are presented at the end of the paper to prove the validity of the proposed approach and its suitability for the problem at hand. © 2010 Springer-Verlag.
Tree augmented naive bayes based on optimization
- Authors: Taheri, Sona , Mammadov, Musa
- Date: 2011
- Type: Text , Conference paper
- Relation: 42 Annual Iranian Mathematics Conference Vali-a-Asr University of Rasanjan 5th-8th September, 2011 p. 594-598
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- Description: Tree augmented naive Bayes is a semi-naive Bayesian Learning method. It relaxes the naive Bayes attribute independence assumption by employing a tree structure, in which each attribute only depends on the class and one other attribute. A maximum weighted spanning tree that maximizes the likelihood of the training data is used to perform classification.
- Description: 2003009354
A globally optimization algorithm for systems of nonlinear equations
- Authors: Mammadov, Musa , Taheri, Sona
- Date: 2010
- Type: Text , Conference paper
- Relation: Proceedings of PCO 2010, The Third International Conference on Power Control and Optimization 2010 Gold Coast p. 214-234
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- Description: In this paper, a new algorithm is proposed for the solutions of system of nonlinear equations. This algorithm uses a combination of the gradient and Newton's methods. A novel dynamic combinator is developed to determine the contribution of the methods in the combination. Also, by using some parameters in the proposed algorithm, this contribution is adjusted. The efficiency of the algoritms is studied in solving system of nonlinear equations.
A new supervised term ranking method for text categorization
- Authors: Mammadov, Musa , Yearwood, John , Zhao, Lei
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at 23rd Australasian Joint Conference on Artificial Intelligence, AI 2010 Vol. 6464 LNAI, p. 102-111
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- Description: In text categorization, different supervised term weighting methods have been applied to improve classification performance by weighting terms with respect to different categories, for example, Information Gain, χ2 statistic, and Odds Ratio. From the literature there are three term ranking methods to summarize term weights of different categories for multi-class text categorization. They are Summation, Average, and Maximum methods. In this paper we present a new term ranking method to summarize term weights, i.e. Maximum Gap. Using two different methods of information gain and χ2 statistic, we setup controlled experiments for different term ranking methods. Reuter-21578 text corpus is used as the dataset. Two popular classification algorithms SVM and Boostexter are adopted to evaluate the performance of different term ranking methods. Experimental results show that the new term ranking method performs better. © 2010 Springer-Verlag.
A novel approach for predicting trading signals of a stock market index
- Authors: Tilakaratne, Chandima , Mammadov, Musa , Morris, Sidney
- Date: 2010
- Type: Text , Book chapter
- Relation: Forecasting models: Methods and applications p. 145-160
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Application of optimisation-based data mining techniques to tobacco control dataset
- Authors: Dzalilov, Zari , Zhang, J , Bagirov, Adil , Mammadov, Musa
- Date: 2010
- Type: Text , Journal article
- Relation: International Journal of Lean Thinking Vol. 1, no. 1 (2010), p. 27-41
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- Description: Tobacco smoking is one of the leading causes of death around the world. Consequently, control of tobacco use is an important global public health issue. Tobacco control may be aided by development of theoretical and methodological frameworks for describing and understanding complex tobacco control systems. Linear regression and logistic regression are currently very popular statistical techniques for modeling and analyzing complex data in tobacco control systems. However, in tobacco markets, numerous interrelated factors nontrivially interact with tobacco control policies, such that policies and control outcomes are nonlinearly related.
From convex to nonconvex: A loss function analysis for binary classification
- Authors: Zhao, Lei , Mammadov, Musa , Yearwood, John
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at10th IEEE International Conference on Data Mining Workshops, ICDMW 2010 p. 1281-1288
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- Description: Problems of data classification can be studied in the framework of regularization theory as ill-posed problems. In this framework, loss functions play an important role in the application of regularization theory to classification. In this paper, we review some important convex loss functions, including hinge loss, square loss, modified square loss, exponential loss, logistic regression loss, as well as some non-convex loss functions, such as sigmoid loss, ø-loss, ramp loss, normalized sigmoid loss, and the loss function of 2 layer neural network. Based on the analysis of these loss functions, we propose a new differentiable non-convex loss function, called smoothed 0-1 loss function, which is a natural approximation of the 0-1 loss function. To compare the performance of different loss functions, we propose two binary classification algorithms for binary classification, one for convex loss functions, the other for non-convex loss functions. A set of experiments are launched on several binary data sets from the UCI repository. The results show that the proposed smoothed 0-1 loss function is robust, especially for those noisy data sets with many outliers. © 2010 IEEE.
Global stability, periodic solutions and optimal control in a nonlinear differential delay model
- Authors: Ivanov, Anatoli , Mammadov, Musa
- Date: 2010
- Type: Text , Conference proceedings
- Relation: Eighth Mississippi State - UAB Conference on Differential Equations and Computational Simulations, 2010
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- Description: A nonlinear differential equation with delay serving as a mathematical model of several applied problmes is considered. Sufficient conditions for the global asymptotic stability and for the existence of periodic solutions are given. Two particular applications are treated in detail. The first one is a blood cell production model by Mackey, for which new periodicity criteria are derived. The second application is a modified economic model with delay due to Ramsay. An optimization problem for a maximal consumption is stated and solved for the latter.
Improving Naive Bayes classifier using conditional probabilities
- Authors: Taheri, Sona , Mammadov, Musa , Bagirov, Adil
- Date: 2010
- Type: Text , Conference proceedings
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- Description: Naive Bayes classifier is the simplest among Bayesian Network classifiers. It has shown to be very efficient on a variety of data classification problems. However, the strong assumption that all features are conditionally independent given the class is often violated on many real world applications. Therefore, improvement of the Naive Bayes classifier by alleviating the feature independence assumption has attracted much attention. In this paper, we develop a new version of the Naive Bayes classifier without assuming independence of features. The proposed algorithm approximates the interactions between features by using conditional probabilities. We present results of numerical experiments on several real world data sets, where continuous features are discretized by applying two different methods. These results demonstrate that the proposed algorithm significantly improve the performance of the Naive Bayes classifier, yet at the same time maintains its robustness. © 2011, Australian Computer Society, Inc.
- Description: 2003009505
Investment decision model via an improved BP neural network
- Authors: Shen, Jihong , Zhang, Canxin , Lian, Chunbo , Hu, Hao , Mammadov, Musa
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at 2010 IEEE International Conference on Information and Automation, ICIA 2010, Harbin, Heilongjiang 20th-23rd June 2010 p. 2092-2096
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- Description: In macro investment, an investment decision model is established by using an improved back propagation (BP) artificial neural network (ANN). In this paper, the relations between elements of investment and output of products are determined, and then the optimal distribution of investment is determined by adjusting the distributions rationally. This model can reflect the highly nonlinear mapping relations among each element of investment by using nonlinear utility functions to improve the architecture of artificial neural network, which can be widely applied in investment problems. ©2010 IEEE.
Profiling phishing emails based on hyperlink information
- Authors: Yearwood, John , Mammadov, Musa , Banerjee, Arunava
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at 2010 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2010, Odense : 9th-11th August 2010 p. 120-127
- Full Text:
- Description: In this paper, a novel method for profiling phishing activity from an analysis of phishing emails is proposed. Profiling is useful in determining the activity of an individual or a particular group of phishers. Work in the area of phishing is usually aimed at detection of phishing emails. In this paper, we concentrate on profiling as distinct from detection of phishing emails. We formulate the profiling problem as a multi-label classification problem using the hyperlinks in the phishing emails as features and structural properties of emails along with whois (i.e.DNS) information on hyperlinks as profile classes. Further, we generate profiles based on classifier predictions. Thus, classes become elements of profiles. We employ a boosting algorithm (AdaBoost) as well as SVM to generate multi-label class predictions on three different datasets created from hyperlink information in phishing emails. These predictions are further utilized to generate complete profiles of these emails. Results show that profiling can be done with quite high accuracy using hyperlink information. © 2010 Crown Copyright.
Regularization methods in the study of drug reaction relationships
- Authors: Mammadov, Musa , Zhao, Lei , Zhang, Jianjun
- Date: 2010
- Type: Text , Conference proceedings
- Full Text: false