IJARAI Volume 3 Issue 10

Copyright Statement: This is an open access publication licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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Paper 1: A Hybrid Reduction Approach for Enhancing Cancer Classification of Microarray Data

Abstract: This paper presents a novel hybrid machine learning (ML)reduction approach to enhance cancer classification accuracy of microarray data based on two ML gene ranking techniques (T-test and Class Separability (CS)). The proposed approach is integrated with two ML classifiers; K-nearest neighbor (KNN) and support vector machine (SVM); for mining microarray gene expression profiles. Four public cancer microarray databases are used for evaluating the proposed approach and successfully accomplish the mining process. These are Lymphoma, Leukemia SRBCT, and Lung Cancer. The strategy to select genes only from the training samples and totally excluding the testing samples from the classifier building process is utilized for more accurate and validated results. Also, the computational experiments are illustrated in details and comprehensively presented with literature related results. The results showed that the proposed reduction approach reached promising results of the number of genes supplemented to the classifiers as well as the classification accuracy.

Author 1: Abeer M. Mahmoud
Author 2: Basma A.Maher

Keywords: Mining Microarray data; Cancer classification; SVM

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Paper 2: Discrimination of EEG-Based Motor Imagery Tasks by Means of a Simple Phase Information Method

Abstract: We propose an off-line analysis method in order to discriminate between motor imagery tasks manipulated in a brain computer interface system. A measure of large-scale synchronization based on phase locking value is established. The results indicate that it can take advantage of the phase synchrony between scalp-recorded EEG activity in the supplementary motor area and in sezorimotor area, computing the differences between the active and the relaxation states. Phase locking value features are more discriminative in ß rhythm than in µ rhythm. The proposed method is simple, computationally efficient and proves good results on EEG Motor Movement/Imagery Dataset available from PhysioNet research resource for physiologic signals.

Author 1: Ana Loboda
Author 2: Alexandra Margineanu
Author 3: Gabriela Rotariu
Author 4: Anca Mihaela Lazar

Keywords: brain computer interface; motor imagery task; electroencephalogram; phase locking value

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Paper 3: Parameter optimization for intelligent phishing detection using Adaptive Neuro-Fuzzy

Abstract: 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.

Author 1: P. A. Barraclough
Author 2: G. Sexton
Author 3: M.A. Hossain
Author 4: N. Aslam

Keywords: FIS; Intelligent phishing detection; fuzzy inference system; neuro-fuzzy

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Paper 4: FlexRFID: A Security and Service Control Policy-Based Middleware for Context-Aware Pervasive Computing

Abstract: Ubiquitous computing targets the provision of seamless services and applications by providing an environment that involves a variety of devices having different capabilities. The design of applications in these environments needs to consider the heterogeneous devices, applications preferences, and rapidly changing contexts. RFID and WSN technologies are widely used in today’s ubiquitous computing. In Wireless Sensor Networks, sensor nodes sense the physical environment and send the sensed data to the sink by multi-hops. WSN are used in many applications such as military and environment monitoring. In Radio Frequency Identification, a unique ID is assigned to a RFID tag which is associated with a real world object. RFID applications cover many areas such as Supply Chain Management (SCM), healthcare, library management, automatic toll collection, etc. The integration of both technologies will bring many advantages in the future of ubiquitous computing, through the provision of real-world tracking and context information about the objects. This will increase considerably the automation of an information system. In order to process the large volume of data captured by sensors and RFID readers in real time, a middleware solution is needed. This middleware should be designed in a way to allow the aggregation, filtering and grouping of the data captured by the hardware devices before sending them to the backend applications. In this paper we demonstrate how our middleware solution called FlexRFID handles large amount of RFID and sensor scan data, and executes applications’ business rules in real time through its policy-based Business Rules layer. The FlexRFID middleware provides easy addition and removal of hardware devices that capture data, as well as uses the business rules of the applications to control all its services. We demonstrate how the middleware controls some defined healthcare scenarios, and deals with the access control security concern to sensitive healthcare data through the use of policies. We propose hereafter the design of FlexRFID middleware along with its evaluation results.

Author 1: Mehdia Ajana El Khaddar
Author 2: Mhammed Chraibi
Author 3: Hamid Harroud
Author 4: Mohammed Boulmalf
Author 5: Mohammed Elkoutbi
Author 6: Abdelilah Maach

Keywords: RFID; Middleware; WSN; Ubiquitous; Pervasive Computing; FlexRFID; Policy-Based; Security; Healthcare; access control

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Paper 5: Modelling and Simulation of a Biometric Identity-Based Cryptography

Abstract: Government information is a vital asset that must be kept in a trusted environment and efficiently managed by authorised parties. Even though e-Government provides a number of advantages, it also introduces a range of new security risks. Sharing confidential and top-secret information in a secure manner among government sectors tends to be the main element that government agencies look for. Thus, developing an effective methodology is essential and it is a key factor for e-Government success. The proposed e-Government scheme in this paper is a combination of identity-based encryption and biometric technology. This new scheme can effectively improve the security in authentication systems, which provides a reliable identity with a high degree of assurance. This paper also demonstrates the feasibility of using finite-state machines as a formal method to analyse the proposed protocols. Finally we showed how Petri Nets could be used to simulate the communication patterns between the server and client as well as to validate the protocol functionality.

Author 1: Dania Aljeaid
Author 2: Xiaoqi Ma
Author 3: Caroline Langensiepen

Keywords: e-Government; identity-based cryptosystem; biometrics; mutual authentication; finite-state machine; Petri net.

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Paper 6: A real time OCSVM Intrusion Detection module with low overhead for SCADA systems

Abstract: In this paper we present a intrusion detection module capable of detecting malicious network traffic in a SCADA (Supervisory Control and Data Acquisition) system. Malicious data in a SCADA system disrupt its correct functioning and tamper with its normal operation. OCSVM (One-Class Support Vector Machine) is an intrusion detection mechanism that does not need any labeled data for training or any information about the kind of anomaly is expecting for the detection process. This feature makes it ideal for processing SCADA environment data and automate SCADA performance monitoring. The OCSVM module developed is trained by network traces off line and detect anomalies in the system real time. In order to decrease the overhead induced by communicated alarms we propose a new detection mechanism that is based on the combination of OCSVM with a recursive k-means clustering procedure. The proposed intrusion detection module K??OCSVMis capable to distinguish severe alarms from possible attacks regardless of the values of parameters and , making it ideal for real-time intrusion detection mechanisms for SCADA systems. The most severe alarms are then communicated with the use of IDMEF files to an IDSIDS (Intrusion Detection System) system that is developed under CockpitCI project. Alarm messages carry information about the source of the incident, the time of the intrusion and a classification of the alarm.

Author 1: Leandros A. Maglaras
Author 2: Jianmin Jiang

Keywords: SCADA systems; OCSVM; intrusion detection

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Paper 7: Developing a Mathematical Model to Detect Diabetes Using Multigene Genetic Programming

Abstract: Diabetes Mellitus is one of the deadly diseases growing at a rapid rate in the developing countries. Diabetes Mellitus is being one of the major contributors to the mortality rate. It is the sixth reason for death worldwide. Early detection of the disease is highly recommended. This paper attempts to enhance the detection of diabetic based on set of attributes collected from the patients to develop a mathematical model using Multigene Symbolic Regression Genetic Programming technique. Genetic Programming (GP) showed significant advantages on evolving nonlinear model which can be used for prediction. The developed GP model is evaluated using Pima Indian data set and showed higher capability and accuracy in detection and diagnosis of Diabetes.

Author 1: Ahlam A Sharief
Author 2: Alaa Sheta

Keywords: Diabetes; Classification; Genetic Programming; Pima Indian data

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