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I S S N 2 2 7 7 -3061 V o l u m e 1 6 N u m b e r 7 I n t e r n a t i o n a l j o u r n a l o f C o m p u t e r s a n d T e c h n o l o g y 7047 AbstractSoftware provide services that may come with some vulnerabilities or risks. Attackers perform actions that break security of system through threats and cause a failure. To avoid security vulnerability, there are many security-specific concepts that should be determined as requirements during software development life cycle in order to deliver a strong and secure software. This paper first, survey a number of existing processes, life cycle and methodologies needed for developing secure software based on the related published works. It starts by presenting the most relevant Secure Software Development Lifecycles, a comparison between the main security features for each process is proposed. The results of the comparison will give the software developer with a guideline which will help on selecting the best secure process. Second, the paper list a set of the most widely used specification languages with the advantages and disadvantages for each.

Clustering is the process of grouping a set of patterns into different disjoint clusters where each cluster contains the alike patterns. Many algorithms had been proposed before for clustering. K-medoid is a variant of k-mean that use an actual point in the cluster to represent it instead of the mean in the k-mean algorithm to get the outliers and reduce noise in the cluster. In order to enhance performance of k-medoid algorithm and get more accurate clusters, a hybrid algorithm is proposed which use CRO algorithm along with k-medoid. In this method, CRO is used to expand searching for the optimal medoid and enhance clustering by getting more precise results.The performance of the new algorithm is evaluated by comparing its results with five clustering algorithms, k-mean, k-medoid, DB/rand/1/bin, CRO based clustering algorithm and hybrid CRO-k-mean by using four real world datasets: Lung cancer, Iris, Breast cancer Wisconsin and Haberman's survival from UCI machine learning data repository. The results were conducted and compared base on different metrics and show that proposed algorithm enhanced clustering technique by giving more accurate results.

Face recognition is one of the most well-known biometric methods. It is a technique used for identifying individual from his face. The recognition process takes the face and compares it with the one stored in the database for recognizing it. Many methods were proposed to achieve that. In this paper, a new technique is proposed by using meta-heuristic algorithm. The algorithm is used to search for the best point in the image to be selected as a pivot, generate a vector of extracted features that are not necessary for the recognition and may reduce accuracy of it and evaluate the weight value for each area in the face image areas. A dataset with 371 images was used for evaluation. The results were conducted and compared with the original face recognition technique. That results show that proposed idea could enhance recognition by increasing accuracy up to 20% over original face recognition technique.