Analyzing strategies of mobile agents on malicious cloud platform with Agent-Based Computational Economic Approach

Abstract

Cloud computing is a service model that enables resource-limited mobile devices to remotely execute tasks from a server. The mobile agent is a software program installed in the mobile device to negotiate a diversity of commerce transactions with other mobile agents in the cloud. However, the negotiation plans carried by mobile agents are easily be eavesdropped on by the malicious cloud platforms, since the codes of mobile agents are read and executed by the cloud platform. Thus, sellers can tailor the negotiation plans to cheat buyers for seizing buyers’ profits in negotiations after eavesdropping on buyers’ negotiation plans. In this paper, we consider the buyers can take actions to resist the sellers’ cheatings, which is that the buyers can tailor their plans with extremely low demand to decrease sellers’ profits before migrate to the hosts. In this paper, we consider actions that buyers can take to resist sellers’ cheatings, that is the buyers can tailor their plans with extremely low demands before migrate to the cloud platform. Above situations between buyers and sellers are modeled as a mathematical model called Eavesdropping and Resistance of Negotiation (ERN) Game. The strategies of the buyers and sellers playing the ERN Game are analyzed by the Agent-Based Computational Economic approach. The simulation results show the cooperative strategies will be emerged between buyers and sellers in the ERN Game.

Introduction

Cloud computing is an emerging service model enabling mobile devices to reduce the energy consumptions and to execute diversity services on the clouds remotely. A mobile agent is a software program executed in the clouds on behalf of the software installed in a mobile device such as PDA, smart phone, or tablet PC to remotely execute jobs or to interact with other mobile agents. Integrating the mobile agents into the clouds, the mobile devices are energy efficient and resources saving, which enables a diversity of applications in the Mobile Commerce in the literatures (Aversa et al., 2010, Oberheide et al., 2008, Shih et al., 2007).

The mobile agents are able to negotiate with other mobile agents on behalf of users (Bartolini et al., 2005, Fasli, 2007). The mobile agents are thus used to build services in Mobile Commerce (An et al., 2010, Tsaur, 2012). Security is one of the most important considerations in mobile agent based applications (Cavalcante et al., 2012). It is generally agreed that it is difficult for mobile agents to prevent eavesdropping attacks from cloud platform (Dadhich et al., 2010, Oberheide et al., 2008). When the mobile agents dispatched to a malicious platform of clouds (or malicious platform, for short), the malicious cloud platform can eavesdrop on the negotiation plan carried by the mobile agents, which is called the “Malicious platform eavesdropping attack”. It is hard to prevent such attacks from two reasons: (1) The malicious platform can read and execute any instructions of the mobile agent (Adane, Adane, & Sathe, 2007). (2) The malicious platform can intercept secret communications of mobile agents (Cavalcante et al., 2012).

Based on “Malicious platform eavesdropping attack”, the sellers’ mobile agents can cheat buyers’ mobile agents in negotiation to seize buyers’ profits according to negotiation plans of buyers’ agents discoursed by malicious platform. As illustration in Fig. 1, a buyers’ mobile agent is executed on the malicious platform on behalf of the software installed in the mobile device for negotiation with other mobile agents. The malicious platform can eavesdrop of the buyers’ negotiation plans and disclose those to the sellers’ mobile agents. Thus, the seller’s mobile agent can tailor its negotiation plan to seize buyer’s profits. Such cheating behaviors will discourage people to use mobile agents based negotiation applications in the clouds.

Knowing sellers may cheat in negotiations, a buyer thus resist sellers’ cheating by adjustment of his negotiation plan with extremely low demand (i.e., very low demand price) before migrate to the cloud platform in order to lower the deal price in negotiation for decreasing the sellers’ profits. Above resistance from buyers may make the sellers take honest actions. Above situations between buyers and sellers are mathematically modeled as a Normal-Form Game model in Game Theory (Branzei, Dimitrov, & Tijs, 2008), called Eavesdropping and Resistance of Negotiation Game (ERN Game). The behaviors and strategies adopt by buyers and sellers playing ERN Game are analyzed by an economic analysis model, Agent Based Computation Economic (ACE) Model.

The Agent Based Computation Economics (ACE) is an analysis model of a dynamic system consisting of economic processes interacting agents (Laib and Radjef, 2011, Tesfatsion, 2006). To study the strategies will be evolved between buyers and sellers, an artificial economy consisting of buyers and sellers playing the ERN Game is constructed. The simulation results show the buyers’ resistances will deter from sellers’ cheating behaviors and the cooperative strategies of buyers and sellers will be emerged.

The rest of this paper is organized as follows. In Section 2, we present the Eavesdropping and Resistance of Negotiation Game (ERN) model. In Section 3, the Agent Based Computation Economic (ACE) Modeling for ERG model is presented. The simulation results of strategies and behaviors in playing ERN Game based on ACE modeling is presented in Section 4. Finally, some concluding presented in Section 5.