Gaik-Yee Chan

Discovering fuzzy association rule patterns and increasing sensitivity analysis of XML-related attacks

Most active research in Host and Network-based Intrusion Detection (ID) and Intrusion Prevention (IP) systems are only able to detect and prevent attacks of the computer systems and attacks at the Network Layer. They are not adequate to countermeasure XML-related attacks. Furthermore, although research have been conducted to countermeasure Web application attacks, they are still not adequate in countering SOAP or XML-based attacks. In this paper, a predictive fuzzy association rule model aimed at segregating known attack patterns (such as SQL injection, buffer overflow and SOAP oversized payload) and anomalies is developed. First, inputs are validated using business policies. The validated input is then fed into our fuzzy association rule model (FARM). Consequently, 20 fuzzy association rule patterns matching input attributes with 3 decision outcomes are discovered with at least 99% confidence. These fuzzy association rule patterns will enable the identification of frequently occurring features, useful to the security administrator in prioritizing which feature to focus on in the future, hence addressing the features selection problem. Data simulated using a Web service e-commerce application are collected and tested on our model. Our models detection or prediction rate is close to 100% and false alarm rate is less than 1%. Compared to other classifiers, our models classification accuracy using random forests achieves the best results with RMSE close to 0.02 and time to build the model within 0.02s for each data set with sample size of more than 600 instances. Thus, our novel fuzzy association rule model significantly provides a viable added layer of security protection for Web service and Business Intelligence-based applications.

Policy-enhanced ANFIS model to counter SOAP-related attacks

Business Intelligence or e-commerce applications are increasingly built on the Web Service platform. Thus, SOAP-related attacks have a higher chance of occurring at the Application Layer. Although active research has been on-going in Host and Network-based intrusion detection and intrusion prevention areas, they are not adequate to countermeasure the attacks occurring at the Application Layer. This is detrimental, especially for e-commerce where sensitive and huge amount of business-related information are being exposed over the Internet. Consequently, in this paper, a policy-enhanced fuzzy model with adaptive neuro-fuzzy inference system features is introduced. Transactions generated by simulation reveal that SOAP-related attacks at the Application Layer can be detected and prevented by validating input values, input field lengths, and SOAP size using our model to classify the possibilities of granting or denying access to the backend application or database. Restricting the inputs using business policies further strengthens the model to be able to achieve detection accuracy of 99% and false positive rate of only 1%. Thus, our model has significantly contributed to an added layer of security protection for Web Service-based e-commerce applications.

An adaptive monitoring framework for ensuring accountability and quality of services in cloud computing

Cloud computing platform has gained popularity among service providers and consumers to perform business operations due to the ease of communication and transaction convenience in terms of accessibility and availability. However, due to the vulnerability of this dynamic open environment, it is crucial to have a binding agreement between all the service parties for ensuring trust while fulfilling the expected Quality of Services (QoS). There is a need to improve on the current Service Level Agreements (SLAs) practice which does not focus on the QoS and accountability assurance. In this paper, we propose an adaptive monitoring framework to dynamically monitor QoS metrics and performance measures to verify compliances to the respective SLAs. The framework is validated with scenarios on response time and availability which shown to provide adaptive remedy action to rectify violation situation. Besides, any service party which establishes non-compliance to SLAs shall be penalized in monetary terms.

Intelligent Context-Based Pattern Matching Approaches to Enhance Decision Making

In this Internet and Cloud Computing era, every second, there is huge volume of data, whether structured or unstructured, is being stored or retrieved by various applications for use in different ways to support decision making. These business applications certainly require effective and accurate means to store and retrieve information on contextual basis to support decision making. Merely using pattern matching methods without considering the context may not help to retrieve the most suitable and accurate information for decision making. This paper therefore introduces three web applications that apply intelligent pattern matching approaches to retrieve accurate information to enhance decision making on contextual basis. In the first study, stemming and Boyer-Moore methods are incorporated with company policies to auto search, and recommend the right candidate to attend the most appropriate training course. The second study, through several iterations of pattern matching using a lookup table, locates the best three real estate properties that match potential buyer’s preferences. In the third study, a color matching scheme is used to find users’ preferred images or photos stored in a Cloud storage. Testing and performance evaluation of these methods using the web applications show results that could effectively enhance decision making.

A Machine Learning Model for Detection and Prediction of Cloud Quality of Service Violation

Cloud services connect user with cloud computing platform where services range from Infrastructure as a Service, Software as a Service and Platform as a Service. It is important for Cloud Service Provider to provide reliable cloud services which are fast in performance and to predict possible service violation before any issue emerges so then remedial action can be taken. In this paper, we therefore experiment with five different machine learning algorithms namely Support Vector Machine, Random Forest, Naive Bayes, Neural Network, and k-Nearest Neighbors for the detection and prediction of cloud quality of service violations in terms of response time and throughput. Experimental results show that the model created using SVM incorporated with 16 derived cloud quality of service violation rules has consistent accuracy of greater than 99%. With this machine learning model coupled with 16 decision rules, the Cloud Service Provider shall be able to know before hand, whether violation of services based on response time and throughput is occurring. When transactions tend to go beyond the threshold limits, system administrator shall be alerted to take necessary preventive measures to bring the system back to normal conditions. This shall reduce the chance for violation to occur, hence mitigating lose or costly penalty.

An Ontology-Based Hybrid Recommender System for Internet Protocol Television

Internet Protocol Television (IPTV) has gained popularity in providing TV channels and program choices to broad range of user. The service providers are attempting ways to attract more users’ subscription and as from user point of view, they would like to have channel or program recommendations based on their preferences as well as public suggestions. This motivates us to propose an ontology-based hybrid recommender system. This system applies content-based and collaborative filtering in IPTV domain to increase users’ satisfaction. The preliminary experimental results show that our proposed system works more effectively by eliminating the cold-start problem, over specialization, data sparsity and new item problems and efficiently by using the ontological user profile for computation of recommendations.

Sequential and Global Learning Styles as Pathways to Improve Learning in Programming

Programming knowledge is increasingly important to facilitate code reuse. Nevertheless, comprehending another programming language is not simple because of its complexity and clarification needs. Prior work focused on different learning styles to aid programming, but it was important to identify which ones were more effective. This research highlights findings in assessing the different documentation styles, including sequential and global documentation styles. Organizing an observation of 125 intermediate undergraduates participated in cloud hosting computation and file content programming exercises, this empirical investigation revealed that sequential documentation exhibits a positive impact in obtaining programming knowledge, significantly pertaining faster completion time, higher multiple choice comprehension, and fewer difficulties. This concludes that sequential documentation solutions can lead intermediate undergraduates with sequential learning styles to faster growth in gaining programming knowledge.