Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation

With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. On the other hand, a secured query service should still provide efficient query processing and significantly reduce the in-house workload to fully realize the benefits of cloud computing. We propose the RASP data perturbation method to provide secure and efficient range query and kNN query services for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. It also preserves multidimensional ranges, which allows existing indexing techniques to be applied to speedup range query processing. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries. We have carefully analyzed the attacks on data and queries under a precisely defined threat model and realistic security assumptions. Extensive experiments have been conducted to show the advantages of this approach on efficiency and security.Comment: 18 pages, to appear in IEEE TKDE, accepted in December 201

A Game Theoretic approach based virtual machine migration for cloud environment security

In cloud computing environment, static configurations can provide for the attackers an environment too easy for exploitation and discovering the network vulnerabilities in order to compromise the network and launching intrusions; while dynamic reconfiguration seeks to develop a virtual machine (VM) migration over the cloud by applying unpredictability of network configuration’s change, and thus improving the system security. In this work a novel approach that performs proactive and reactive measures to ensure a high availability and to minimize the attack surface using VM migration is proposed. This interaction between attack and defense systems was formulated as game model. As result, we have calculated the Nash equilibrium and the utilities for the both attacker and defender, evaluate the parameters which can maximize the defender’s utility when the VM migration was planned and identify the potential attack paths. Therefore, the effectiveness of the game model was validated by some numerical results that determine optimal migration strategies in order to ensure the security of the system

Preserving Data Confidentiality and Query Privacy Using KNN-R Approach

Cloud computing is one of the famous and well known technique that processes the data query efficiently. Since it is maintaining huge amount of resources, its privacy and security is an issue. Cloud service providers are not trust worthy, so data is to be secured. Whenever the data is sent to the cloud, it is encrypted because to protect the sensitive data such that query privacy and data confidentiality is assured. Cloud computing reduces the in- house resources .This doesn’t mean processing of the query should be slow. To ensure query privacy and data confidentiality RASP approach is designed. The RASP Perturbation technique combines Order preserving Encryption, Dimensionality Expansion, random noise injection , random projection to provide strong safety to the perturbed data and query. RASP make use of the kNN algorithm to process the query efficiently. kNN approach use the minimum square range to process the query. It transfers data to the multidimensional space where it uses indexing approach to process the minimum square range points. DOI: 10.17762/ijritcc2321-8169.150313

A Method for Revealing and Addressing Security Vulnerabilities in Cyber-physical Systems by Modeling Malicious Agent Interactions with Formal Verification

Several cyber-attacks on the cyber-physical systems (CPS) that monitor and control critical infrastructure were publically announced over the last few years. Almost without exception, the proposed security solutions focus on preventing unauthorized access to the industrial control systems (ICS) at various levels – the defense in depth approach. While useful, it does not address the problem of making the systems more capable of responding to the malicious actions of an attacker once they have gained access to the system. The first step in making an ICS more resilient to an attacker is identifying the cyber security vulnerabilities the attacker can use during system design. This paper presents a method that reveals cyber security vulnerabilities in ICS through the formal modeling of the system and malicious agents. The inclusion of the malicious agent in the analysis of an existing systems identifies security vulnerabilities that are missed in traditional functional model checking