Cloud Security was provided for the services such as storage, network, applications and software through internet. The Security was given at each layer (Saas, Paas, and Iaas), in each layer, there are some security threats which became the major problem in cloud computing. In Saas, the security issues are mainly present in Web Application services and this issue can be overcome by web application scanners and service level agreement(SLA). In Paas, the major problem is Data Transmission. During transmission of data, some data may be lost or modified. The PaaS environment accomplishes proficiency to some extent through duplication of information. The duplication of information makes high accessibility of information for engineers and clients. However, data is never fully deleted instead the pointers to the data are deleted. In order to overcome this problem the techniques that used are encryp-tion[12], data backup. In Iaas the security threat that occurs in is virtualization and the techniques that are used to overcome the threats are Dynamic Security Provisioning(DSC), operational security procedure, for which Cloud Software is available in the market, for e.g. Eucalyptus, Nimbus 6.

One of the major challenges of Global Software Development (GSD) is associated with overcoming integration problems that remain hidden throughout development and surface at the end of a project. Incompatibilities and other integration complexities can lead to extra costs, delays, lowered quality and even failure of a GSD project. A solid integration strategy can be an effective solution to overcome integration challenges, but this requires a good understanding of what causes failure and how the failures can be mitigated. The aim of this study is to investigate integration problems that occurred in different phases of GSD, and successful integration practices with their relative importance through an extensive literature review and a Delphi survey. As a result, a prioritized list of failure and success factors is obtained. The findings are applicable for planning new projects at an early stage of GSD or improving ongoing projects.

Abstract--Spyware represents a serious threat to confidentiality since it may result in loss of control over private data for computer users. This type of software might collect the data and send it to a third party without informed user consent. Traditionally two approaches have been presented for the purpose of spyware detection: Signature-based Detection and Heuristic-based Detection. These approaches perform well against known Spyware but have not been proven to be successful at detecting new spyware. This paper presents a Spyware detection approach by using Data Mining (DM) technologies. Our approach is inspired by DM-based malicious code detectors, which are known to work well for detecting viruses and similar software. However, this type of detector has not been investigated in terms of how well it is able to detect spyware. We extract binary features, called n-grams, from both spyware and legitimate software and apply five different supervised learning algorithms to train classifiers that are able to classify unknown binaries by analyzing extracted n-grams. The experimental results suggest that our method is successful even when the training data is scarce.

Abstract-Scareware is a recent type of malicious software that may pose financial and privacy-related threats to novice users. Traditional countermeasures, such as anti-virus software, require regular updates and often lack the capability of detecting novel (unseen) instances. This paper presents a scareware detection method that is based on the application of machine learning algorithms to learn patterns in extracted variable length opcode sequences derived from instruction sequences of binary files. The patterns are then used to classify software as legitimate or scareware but they may also reveal interpretable behavior that is unique to either type of software. We have obtained a large number of real world scareware applications and designed a data set with 550 scareware instances and 250 benign instances. The experimental results show that several common data mining algorithms are able to generate accurate models from the data set. The Random Forest algorithm is shown to outperform the other algorithms in the experiment. Essentially, our study shows that, even though the differences between scareware and legitimate software are subtler than between, say, viruses and legitimate software, the same type of machine learning approach can be used in both of these dissimilar cases.

No abstract