Cloud computing is a platform for expanding capabilities and developing potentialities dynamically without employing new infrastructure, personnel, or software systems. In Addition, cloud computing originated from a commercial enterprise concept, and developed into a flourishing IT invention. However, given that considerable information on individuals and companies are identified in the cloud, concerns have been raised regarding the safety of the cloud environment. Despite the hype surrounding cloud computing, customers remain reluctant to deploy their commercial enterprise into the cloud. Nevertheless, lack of protection is the only major concern that hinders increased use of cloud computing. Furthermore, the complexity with which cloud computing manages data secrecy, and information security makes the market hesitant about cloud computing. The architecture of cloud models threatens the security of existing technologies when deployed in a cloud environment. Thus, users of cloud services should know the dangers of uploading data into this new environment. Therefore, \ud in this paper different cryptography aspects that pose a threat to cloud computing are reviewed. This paper is a survey of specific security issues brought by the use of cryptography in a cloud computing system

Malware is one of the major security threats that can break computer operation. However, commercial anti-virus or anti-spyware that used signature-based matching to detects malware cannot solve that kind of threats. Nowadays malware writers try to avoid detection by using several techniques such as polymorphic, metamorphic and also hiding technique. In order to overcome that issue, we proposed a new framework for malware behavior identification and classification that apply dynamic approach. This framework consists of two major processes such as behavior identification and malware classification. These two major processes will integrate together as interrelated process in our proposed framework. Result from this study is a new framework that able to identify and classify malware based on it behaviors.

Malware detection must apply sophisticated technique to minimize malware thread that can break computer operation. Nowadays malware writers try to avoid detection by using several techniques such as polymorphic, hiding and also zero day of attack. However, commercial anti-virus or anti-spyware that used signature-based matching to detects malware cannot solve that kind of attack. In order to overcome this issue, we propose a new framework for malware detection that combines signature-based technique and genetic algorithm technique. This framework consists of three main components such as s-based detection, GA detection and signature generator. These three main components will work together as interrelated process in our propose framework. Result from this study is the new framework that design to solve new launce malware and also to generate signature automatically that can be used on signature-based detection.

Malware is one of the major security threats in computer and network environment. However, Signature-based approach that commonly used does not provide enough opportunity to learn and understand malware threats that can be used in implementing security prevention mechanisms. In order to learn and understand the malwares, behavior-based technique that applied dynamic approach is the possible solution for identification, classification and clustering the malwares. In the paper, we present a new approach for conducting behavior-based analysis of malicious programs. One experiment was conducted on the campus network to generate an analysis of current malware behaviors. The result shows that the most potential malware threats in campus network are worm and Trojan.

In past decades, we have seen that the increasing speed of the network attacks compromising computer system functionality and degrading network performance. The security of these systems has attracted a lot of research in the field of intrusion detection and response system to reduce the effect of these attacks. Response is a major part of intrusion detection system. Intrusion detection system without a timely response is not considered good even they detect threat and generate alarms. Optimum response is based on the selection of proper response option. In this paper, we categorize the attacks and propose some response option to thwart these attacks.