Strategies of Mobile Agents on Malicious Clouds

Cloud computing is a service model enabling resources limited mobile devices to remotely execute tasks on the clouds. The Mobile Agent is a software program on behalf of the software installed in the mobile device to negotiate with other mobile agents in the clouds, which provides a diversity of automated negotiation based applications in Mobile Commences. However, the negotiation plans carried by mobile agents are easily be eavesdropped by the malicious cloud platforms, since the codes of mobile agents are read and executed on the cloud platform. Thus, the sellers can take cheat actions to increase their profits, which is to tailor the negotiation plans to seize buyers’ profits after eavesdropping on buyers’ negotiation plans. In this paper, we consider the buyers can take actions to resist the sellers’ cheatings, that is the buyers can tailor their plans with extremely low demands before migrate to the cloud platform. Above situations are modeled as a mathematical model, called the Eavesdropping and Resistance of Negotiation (ERN) Game. We develop a simulator to simulate an artificial market for analyzing the behaviors on ERN Game. The simulation results show buyers’ resistances deter sellers from cheating and cooperative strategies are adopted by buyers and sellers

DCDIDP: A distributed, collaborative, and data-driven intrusion detection and prevention framework for cloud computing environments

With the growing popularity of cloud computing, the exploitation of possible vulnerabilities grows at the same pace; the distributed nature of the cloud makes it an attractive target for potential intruders. Despite security issues delaying its adoption, cloud computing has already become an unstoppable force; thus, security mechanisms to ensure its secure adoption are an immediate need. Here, we focus on intrusion detection and prevention systems (IDPSs) to defend against the intruders. In this paper, we propose a Distributed, Collaborative, and Data-driven Intrusion Detection and Prevention system (DCDIDP). Its goal is to make use of the resources in the cloud and provide a holistic IDPS for all cloud service providers which collaborate with other peers in a distributed manner at different architectural levels to respond to attacks. We present the DCDIDP framework, whose infrastructure level is composed of three logical layers: network, host, and global as well as platform and software levels. Then, we review its components and discuss some existing approaches to be used for the modules in our proposed framework. Furthermore, we discuss developing a comprehensive trust management framework to support the establishment and evolution of trust among different cloud service providers. © 2011 ICST

Handling Confidential Data on the Untrusted Cloud: An Agent-based Approach

Cloud computing allows shared computer and storage facilities to be used by a multitude of clients. While cloud management is centralized, the information resides in the cloud and information sharing can be implemented via off-the-shelf techniques for multiuser databases. Users, however, are very diffident for not having full control over their sensitive data. Untrusted database-as-a-server techniques are neither readily extendable to the cloud environment nor easily understandable by non-technical users. To solve this problem, we present an approach where agents share reserved data in a secure manner by the use of simple grant-and-revoke permissions on shared data.Comment: 7 pages, 9 figures, Cloud Computing 201

Reputation-based Cooperation in the Clouds

The popularity of the cloud computing paradigm is opening new opportunities for collaborative computing. In this paper we tackle a fundamental problem in open-ended cloud-based distributed comput- ing platforms, i.e., the quest for potential collaborators. We assume that cloud participants are willing to share their computational resources for shared distributed computing problems, but they are not willing to dis- closure the details of their resources. Lacking such information, we advo- cate to rely on reputation scores obtained by evaluating the interactions among participants. More specifically, we propose a methodology to as- sess, at design time, the impact of different (reputation-based) collabo- rator selection strategies on the system performance. The evaluation is performed through statistical analysis on a volunteer cloud simulator