Abstract-A trust-based intrusion detection scheme for hybrid cloud computing is proposed. We consider a trust metric based on honesty, cooperation and efficiency for detecting malicious machines. We use Perron-Frobenius theorem to detect intrusion based on trust and observations. By statistically analyzing peer-to-peer trust distributed results, we apply trust-based intrusion detection to assess the trustworthiness and maliciousness. An analytical model and simulation for performance are developed. We analyze the sensitivity of false alarms with respect to the minimum trust threshold below where a node is considered malicious. Results confirm that our proposal is flexible enough to detect malicious behaviours in various context of executing application in hybrid cloud. With this work, we can guide future execution in the cloud resource.

Host-Based Intrusion Detection Systems (HIDS) have been widely used to detect malicious behaviors of nodes in heterogenous networks. Collaborative intrusion detection can be more secure with a framework using reputation aggregation as an incentive. The problem of incentives and efficiency are well known problems that can be addressed in such collaborative environment. In this paper, we propose to use game theory to improve detection and optimize intrusion detection systems used in collaboration. The main contribution of this paper is that the reputation of HIDS is evaluated before modeling the game between the HIDS and attackers. Our proposal has three phases: the first phase builds reputation evaluation between HIDS and estimates the reputation for each one. In the second phase, a proposed algorithm elects a leader using reputation value to make decisions. In the last phase, using game theory the leader decides to activate or not the HIDS for optimization reasons.

Cloud computing is very useful for improving distributed applications performance. However, it is difficult to manage risks related to trust when collaborating with unknown and potentially malicious peers. Besides, trust evaluation is the target of dishonest behaviors trying to disturb the control process. In this paper, reputation-based trust management models for cloud computing are proposed. These Peer-to-Peer (P2P) reputation models are based on the interaction between peers. Using evaluations and feedbacks, a central entity can estimate the trust of a given peer. Three approaches are proposed to estimate the trust: PerronTrust, CredTrust and CredTrust-trust. They are studied, simulated and compared between them and to two existing methods for trust under several attack scenarios. Our analysis clearly shows that the third approach CredTrust-trust combining the concepts of trust and credibility in an appropriate way is the most efficient to avoid malicious behaviors and to guide and advise future executions in the open cloud in term of selecting the dependable and reliable peers in cloud environment.

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