Rosmawati Nordin

DEPENDABILITY ATTRIBUTES FOR INCREASED SECURITY IN COMPONENT-BASED SOFTWARE DEVELOPMENT

Existing software applications become increasingly distributed as their continuity and lifetimes are lengthened; consequently, the users’ dependence on these applications is increased. The security of these applications has become a primary concern in their design, construction and evolution. Thus, these applications give rise to major concerns on t he capability of the current development approach t o develop secure systems. Component-Based Software Development (CBSD) is a software engineering approach. CBSD has been successfully applied in many domains. However, the CBSD capability to develop secure software applications is lacking to date. This study is an extension of the previous study on the challenges of the security features in CBSD models. Therefore, this study proposes a solution to the lack of security in CBSD models by highlighting the attributes that must be embedded into the CBSD process. A thorough analysis of exist ing studies is conducted to investigate the related software security attributes. The outcome analysis is beneficial for industries, such as software development companies, as well as for academic inst itutions. The analysis also serves as a baseline reference for companies that develop component-based software.

AWARENESS OF EMBEDDING SECURITY FEATURES INTO COMPONENT-BASED SOFTWARE DEVELOPMENT MODEL: A SURVEY

Current applications and systems contain the software components as the basic elements and Component Based Software Development (CBSD) has been successful in building applications and systems. However, the security of CBSD for the software component is still lacking. This study highlights the results of a survey pertaining to the embedding of security features in the CBSD process. The main objective of this survey is to investigate the awareness of embedding security features in the CBSD process in the Malaysian context. For this purpose, experts from industry as well as from the academic community were interviewed. Moreover, an online survey was formulated and e-mailed to the experts and potential candidates. The results show that the embedding of security features in the software lifecycle is crucial because the incorporation of security activities in CBSD will minimize vulnerabilities in the software system, thus reducing system cost.

Embedding Dependability Attributes into Component-Based Software Development Using the Best Practice Method: A Guideline

Current organizational vulnerabilities mainly originate from Web applications. The security holes in Web applications have resulted in credit card theft, damaged financial resources and reputation of institutions, and compromised computers. Existing Web application systems encounter a high record of vulnerabilities that target dependability attributes. Mitigating software vulnerabilities and increasing software protection against bugs or vulnerabilities are critical to increase consumer confidence in software component products. Improved software engineering practices must also be adopted to mitigate the vulnerabilities in modern systems. Component-based software development (CBSD) is a software engineering approach. CBSD has been successfully applied in many domains. However, the CBSD capability to develop secure software applications is lacking to date. Therefore, this article proposes a guideline to overcome the lack of security trust in CBSD process. The proposed guideline embeds dependability attributes into CBSD by using the best practice method. The proposed guideline is significant for eliciting, analyzing, specifying, and composing the dependability attributes of CBSD.

Feature points repeatability on facial deformation

Feature point detection has been an important subject in image processing researches. It holds extensive potential applications in computer recognition, medical image analysis, artificial intelligence, and other fields. This paper explores and compares the performances of Harris corner detection and Scale Invariant Feature Transform (SIFT) methods in feature point detection works on facial image pre-processed using two different techniques; normal pre-process and background subtraction. Each method is performed to test their relation with repeatability. Three experimental stages have been executed; starting with feature point detection, identifying repeatability and finally measured the repeatability. The feature point is experimented on facial image surfaces with natural and smile expression. Based from the experimental result, we found background subtraction pre-processing technique give a good impact to the performances of both Harris Corner and SIFT detector in terms of searching the repeatability points.

Embedding dependability attributes into component-based software development

An increasing competition among companies specialising in software production and services has emerged over the years. Today, companies compete even on trivial matters, aiming to produce dependable, reliable and affordable software applications. To achieve this goal, the software application must either be developed more efficiently or large portions must be reused. The component approach leads to the production of cheaper, faster and more reliable software. Consequently, many industries have begun to focus on software development using the reuse approach. Many industries have turned to reusing software components during development because this makes applications cheaper, faster and more reliable. However, it also makes them hard to secure. Hasan Kahtan, Nordin Abu Bakar, Rosmawati Nordin and Mansoor Abdullateef Abdulgabber of Universiti Teknologi MARA discuss an implementation process that overcomes the lack of security during component-based software development and show how its implemented via an industrial software application case study.

Decision fusion for frontal face verification

It has been established that the combination of a set of classifiers designed for a given pattern recognition problem may achieve higher recognition/classification rates than any of the classifiers taken individually. One of the contributing factor for the improvement is the rule applied to get a unified decision and the diversity of the classifiers. Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) are two popular approaches in face recognition and verification. The authors will demonstrate a verification performance in which the fusion of both methods produces an improved rate compared to individual performance. Tests are carried out on FERET (Facial Recognition Technology) database using a modified protocol. A major drawback in applying LDA is that it requires a large set of individual face images sample to extract the intra-class variation. Performance is presented as the rate of verification when false acceptance rate is zero, in other words, no impostors allowed. Results using fusion of three verification experts show improvement compared with the best individual expert.