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Cloud computing has become widely adopted by businesses for hosting applications with improved performance at a fraction of the operational costs and complexity. The rise of cloud applications has been coupled with an increase in security threat vectors and vulnerabilities. In this paper, we propose a new security assessment and enforcement tool for the cloud named CloudSafe, which provides an automated security assessment and enforce best security control for the cloud by collating various security tools. To demonstrate the applicability and usability of CloudSafe, we implemented CloudSafe and conducted security assessment in Amazon AWS. Also, we analyzed four different security countermeasure options in depth; Vulnerability Patching, Virtual Patching, Network Hardening and Moving Target Defence. Virtual Patching, Network Hardening and Moving Target Defence were determined to be feasible with regards to deployment implementation for the project. Proof of concepts were developed demonstrating the effectiveness of each feasible countermeasure option. These results indicate that the proposed tool CloudSafe is effective and efficient in helping security administrators to select optimal countermeasures to secure their cloud by conducting an in-depth security assessment.

Resource constrained Internet-of-Things (IoT) devices are highly likely to be compromised by attackers, because strong security protections may not be suitable to be deployed. This requires an alternative approach to protect vulnerable components in IoT networks. In this article, we propose an integrated defense technique to achieve intrusion prevention by leveraging cyberdeception (i.e., a decoy system) and moving target defense (i.e., network topology shuffling). We evaluate the effectiveness and efficiency of our proposed technique analytically based on a graphical security model in a software-defined networking (SDN)-based IoT network. We develop four strategies (i.e., fixed/random and adaptive/hybrid) to address “when” to perform network topology shuffling and three strategies (i.e., genetic algorithm/decoy attack path-based optimization/random) to address “how” to perform network topology shuffling on a decoy-populated IoT network, and we analyze which strategy can best achieve a system goal, such as prolonging the system lifetime, maximizing deception effectiveness, maximizing service availability, or minimizing defense cost. We demonstrated that a softwaredefined IoT network running our intrusion prevention technique at the optimal parameter setting prolongs system lifetime, increases attack complexity of compromising critical nodes, and maintains superior service availability compared with a counterpart IoT network without running our intrusion prevention technique. Further, when given a single goal or a multi-objective goal (e.g., maximizing the system lifetime and service availability while minimizing the defense cost) as input, the best combination of “when” and “how” strategies is identified for executing our proposed technique under which the specified goal can be best achieved. [ABSTRACT FROM AUTHOR]

Software Defined Networking (SDN) extends capabilities of existing networks by providing various functionalities, such as flexible networking controls. However, there are many security threat vectors in SDN, including existing and emerging ones arising from new functionalities, that may hinder the use of SDN. To tackle this problem, many countermeasures have been developed to mitigate various threats faced in SDN. However, their effectiveness must be analyzed and compared to fully understand how security posture of SDN changes when the countermeasure is adopted. Also, it becomes difficult to optimize the security of SDN without using a systematic approach to evaluate the security posture of SDN. In this paper, we propose a novel framework to systematically model and analyze the security posture of SDN. We develop a novel graphical security model formalism named Threat Vector Hierarchical Attack Representation Model (TV-HARM), which provides a systematic approach to evaluate threats, attacks and countermeasures for SDN. The TV-HARM captures different threats and their combinations, enabling security risk assessment of SDN. In addition, we define three new security metrics to represent security of SDN. Our experimental results showed that the proposed security assessment framework can capture and evaluate various security threats to SDN, demonstrating the applicability and feasibility of the proposed framework.