Aladdin Ayesh

Phishing detection based Associative Classification data mining

Abstract Website phishing is considered one of the crucial security challenges for the online community due to the massive numbers of online transactions performed on a daily basis. Website phishing can be described as mimicking a trusted website to obtain sensitive information from online users such as usernames and passwords. Black lists, white lists and the utilisation of search methods are examples of solutions to minimise the risk of this problem. One intelligent approach based on data mining called Associative Classification (AC) seems a potential solution that may effectively detect phishing websites with high accuracy. According to experimental studies, AC often extracts classifiers containing simple “If-Then” rules with a high degree of predictive accuracy. In this paper, we investigate the problem of website phishing using a developed AC method called Multi-label Classifier based Associative Classification (MCAC) to seek its applicability to the phishing problem. We also want to identify features that distinguish phishing websites from legitimate ones. In addition, we survey intelligent approaches used to handle the phishing problem. Experimental results using real data collected from different sources show that AC particularly MCAC detects phishing websites with higher accuracy than other intelligent algorithms. Further, MCAC generates new hidden knowledge (rules) that other algorithms are unable to find and this has improved its classifiers predictive performance.

Access network selection based on fuzzy logic and genetic algorithms

In the next generation of heterogeneous wireless networks (HWNs), a large number of different radio access technologies (RATs) will be integrated into a common network. In this type of networks, selecting the most optimal and promising access network (AN) is an important consideration for overall networks stability, resource utilization, user satisfaction, and quality of service (QoS) provisioning. This paper proposes a general scheme to solve the access network selection (ANS) problem in the HWN. The proposed scheme has been used to present and design a general multicriteria software assistant (SA) that can consider the user, operator, and/or the QoS view points. Combined fuzzy logic (FL) and genetic algorithms (GAs) have been used to give the proposed scheme the required scalability, flexibility, and simplicity. The simulation results show that the proposed scheme and SA have better and more robust performance over the random-based selection.

Development of software effort and schedule estimation models using Soft Computing Techniques

Accurate estimation of the software effort and schedule affects the budget computation. Bidding for contracts depends mainly on the estimated cost. Inaccurate estimates will lead to failure of making a profit, increased probability of project incompletion and delay of the project delivery date. In this paper, we explore the use of Soft Computing Techniques to build a suitable model structure to utilize improved estimations of software effort for NASA software projects. In doing so, we plan to use Particle Swarm Optimization (PSO) to tune the parameters of the famous COnstructive COst MOdel (COCOMO). We plan also to explore the advantages of Fuzzy Logic to build a set of linear models over the domain of possible software Line Of Code (LOC). The performance of the developed model was evaluated using NASA software projects data set [1]. A comparison between COCOMO tuned-PSO, Fuzzy Logic (FL), Halstead, Walston-Felix, Bailey-Basili and Doty models were provided.

MAC: A Multiclass Associative Classification Algorithm

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 different data sets from the UCI data repository and using different 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.

Emotionally motivated reinforcement learning based controller

There have been several attempts to model emotions in autonomous agents and robotics. The use of emotions in conjunction with reinforcement learning in particular has attracted attention since both notions are borrowed analogies from psychology. The work presented here is an approach to robot control based on modeling emotions within reinforcement learning algorithm. The main contribution of this paper is the use of fuzzy cognitive maps (FCM) to facilitate the modeling of emotions and inferencing for action selection. This approach does not use feeling estimation; instead a direct link between sensory data and emotions is used for emotional estimation. An emotion based reinforcement learning algorithm is proposed for action selection in robotic control

Analytical study to detect threshold number of efficient routes in multipath AODV extensions

AODV is one of the most common reactive routing protocols used in Mobile Ad hoc Networks (MANETs). It has many advantages over proactive protocols and its drawbacks could be overcome by applying some modifications on the protocol mechanism. One drawback of AODV is the single route abstraction which requires a source node to establish a new route discovery process when a link failure is encountered in the primary current route. Many approaches have been conducted to solve this problem either partial-route re-establishment or Multipath establishment approaches. In this paper, we present an analytical study aims to reduce routing delay time overhead by detecting the waiting time needed to receive a threshold number of efficient routes that are actually needed by a source node to communicate a destination. A new Multipath establishment approach named Threshold Routes AODV (TRAODV) has been implemented and evaluated against Multiple-Route AODV (MRAODV) in terms of routing delay time overhead and Route Availability which has been defined in this paper as a new performance indicator for Multipath AODV extensions. A framework of Multipath AODV implementation has been applied on a self-developed simulator based on Unified Modeling Language (UML) and Java open source.

Optimizing the communication distance of an ad hoc wireless sensor networks by genetic algorithms

In this paper we provide our preliminary idea of using Genetic Algorithms (GAs) to solve the ad hoc Wireless Sensor Networks (WSNs) distance optimization problem. Our objective is to minimize the communication distance over a distributed sensor network. The proposed sensor network will be autonomously divided into set of k-clusters (k is unknown) to reduce the energy consumption for the overall network. On doing this, we use GAs to specify; the location of cluster-heads, the number of clusters and the cluster-mumbers which, if chosen, will minimize the communication distance over the distributed sensor network.

Access network selection using combined fuzzy control and MCDM in heterogeneous networks

In the next generation of heterogeneous wireless networks, selecting the most optimal and promising access network is an important consideration for overall networks stability, resource utilization, user satisfaction, and quality of service (QoS) provisioning. This paper proposes a new and generic algorithm for access network selection in the heterogeneous wireless environments. The proposed algorithm uses a combined fuzzy logic control and multi-criteria decision making (MCDM) system to achieve scalable, flexible, simple, general, and adaptable solution. The first layer of the algorithm uses small fuzzy logic based subsystems, where each subsystem represents one input criteria. The fuzzy logic is used to overcome the complexity and fuzziness associated with the heterogeneous wireless environments and their services and to make the proposed algorithm simpler and more adaptable. In the second layer, the algorithm uses a MCDM technique that takes the first layer output as its input. The MCDM ensures that all of the different characteristics and view points are taken into account when making the radio access network selection.

Using Self Organizing Map to cluster Arabic crime documents

This paper presents a system that combines two text mining techniques; information extraction and clustering. A rule-based approach is used to perform the information extraction task, based on the dependency relation between some intransitive verbs and prepositions. This relationship helps in extracting types of crime from documents within the crime domain. With regard to the clustering task, the Self Organizing Map (SOM) is used to cluster Arabic crime documents based on crime types. This work is then validated through experiments, the results of which show that the techniques developed here are promising.

Extracting subtle facial expression for emotional analysis

Humans convey emotions with subtle facial expressions. The interpretation of these expressions not only depends on that expression, but on how it developed. Exposing this development enables a better analysis of the ambiguity experienced when analyzing the underlying emotion. Subtle facial expressions can be objectively measured in terms of action units as defined in the facial action coding system. This paper presents a facial expression language based on FACS which provides a continues stream of expressions for measuring the growth of expression. The development of the emotion surprise which requires two action units is used as a test case