Biblio

Runtime cloud security auditing plays a vital role in mitigating security concerns in a cloud. However, there currently does not exist a comprehensive solution that can protect a cloud tenant against the threats rendered from the multiple levels (e.g., user, virtual, and physical) of the cloud design. Furthermore, most of the existing solutions suffer from slow response time and require significant manual efforts. Therefore, a simple integration of the existing solutions for different levels is not a practical solution. In this paper, we propose a multilevel proactive security auditing system, which overcomes all the above-mentioned limitations. To this end, our main idea is to automatically build a predictive model based on the dependency relationships between cloud events, proactively verify the security policies related to different levels of a cloud by leveraging this model, and finally enforce those policies on the cloud based on the verification results. Our experiments using both synthetic and real data show the practicality and effectiveness of this solution (e.g., responding in a few milliseconds to verify each level of the cloud).

Converged Multi-Level Secure systems allow users to interact with and freely move between applications and data of varying sensitivity on a single user interface. They promise unprecedented usability and security, especially in security-critical environments like Defence. Yet these promises rely on hard assumptions about secure user behaviour. We present initial work to test the validity of these assumptions in the absence of deception by an adversary. We conducted a user study with 21 participants on the Cross Domain Desktop Compositor. Chief amongst our findings is that the vast majority of participants (19 of 21) behave securely, even when doing so requires more effort than to behave insecurely. Our findings suggest that there is large scope for further research on converged Multi-Level Secure systems, and highlight the value of user studies to complement formal security analyses of critical systems.

Contemporary cloud environments are built on low-assurance components, so they cannot provide a high level of assurance about the isolation and protection of information. A ``multi-level'' secure cloud environment thus typically consists of multiple, isolated clouds, each of which handles data of only one security level. Not only are such environments duplicative and costly, data ``sharing'' must be implemented by massive, wasteful copying of data from low-level domains to high-level domains. The requirements for certifiable, scalable, multi-level cloud security are threefold: 1) To have trusted, high-assurance components available for use in creating a multi-level secure cloud environment; 2) To design a cloud architecture that efficiently uses the high-assurance components in a scalable way, and 3) To compose the secure components within the scalable architecture while still verifiably maintaining the system security properties. This paper introduces a trusted, high-assurance file server and architecture that satisfies all three requirements. The file server is built on mature technology that was previously certified and deployed across domains from TS/SCI to Unclassified and that supports high-performance, low-to-high and high-to-low file sharing with verifiable security.

5G, the fifth generation of mobile communication networks, is considered as one of the main IoT enablers. Connecting billions of things, 5G/IoT will be dealing with trillions of GBytes of data. Securing such large amounts of data is a very challenging task. Collected data varies from simple temperature measurements to more critical transaction data. Thus, applying uniform security measures is a waste of resources (processing, memory, and network bandwidth). Alternatively, a multi-level security model needs to be applied according to the varying requirements. In this paper, we present a multi-level security scheme (BLP) applied originally in the information security domain. We review its application in the network domain, and propose a modified version of BLP for the 5G/IoT case. The proposed model is proven to be secure and compliant with the model rules.

The increasing demand for secure interactions between network domains brings in new challenges to access control technologies. In this paper we design an access control framework which provides a multilevel mapping method between hierarchical access control structures for achieving multilevel security protection in cross-domain networks. Hierarchical access control structures ensure rigorous multilevel security in intra domains. And the mapping method based on subject attributes is proposed to determine the subject's security level in its target domain. Experimental results we obtained from simulations are also reported in this paper to verify the effectiveness of the proposed access control model.

This paper introduces a multi-factors security key generation mechanism for self-organising Internet of Things (IoT) network and nodes. The mechanism enables users to generate unique set of security keys to enhance IoT security while meeting various business needs. The multi-factor security keys presents an additional security layer to existing security standards and practices currently being adopted by the IoT community. The proposed security key generation mechanism enables user to define and choose any physical and logical parameters he/she prefers, in generating a set of security keys to be encrypted and distributed to registered IoT nodes. IoT applications and services will only be activated after verifying that all security keys are present. Multiple levels of authorisation for different user groups can be easily created through the mix and match of the generated multi-factors security keys. A use case, covering indoor and outdoor field tests was conducted. The results of the tests showed that the mechanism is easily adaptable to meet diverse multivendor IoT devices and is scalable for various applications.