The Internet of Things (IoT) infrastructure is a network of interconnected devices and systems, with applications ranging from remote healthcare to large-scale industrial applications. Securing such a resource-constrained infrastructure requires effective and efficient security measures, starting with a comprehensive security risk assessment. In traditional IT infrastructure, Bayesian Attack Graphs (BAGs) are commonly used for security risk assessment. The BAGs illustrate and quantify logical dependencies among vulnerabilities using real probabilities to depict potential attack paths, with their likelihood and impact. However, applying traditional BAGs to IoT infrastructure is challenging. First, purely additive nature of real probabilities makes it difficult to quantify exploits launched in tandem from the same or varying sources that may collectively exploit a given vulnerability or cancel out each other’s degenerative consequences on the network. Second, cycles may arise in BAGs, especially when attackers backtrack to a previously compromised state, a problem challenging to quantify using traditional BAGs. To address these challenges, this research proposes a risk assessment framework using complex probabilities for large-scale IoT applications. The framework effectively quantifies unpredictable attacker behavior, particularly when cycles occur due to attacker backtracking. A use case in the Connected and Autonomous Vehicles (CAVs) network demonstrates the framework’s effectiveness and validation.

Summary Generic notions of security on cloud platforms make clients apprehensive about fully migrating their applications on these platforms. The challenge lies in the capability of personalizing the security assessments of different cloud service providers from the perspective of the security requirements of the client applications to be hosted on them. This challenge was addressed by the previously proposed offline risk assessment framework for cloud service providers. This article presents a comprehensive analysis of a cloud migration framework that has been designed by adapting the novel security assessment principles of the offline risk assessment framework. The migration strategy has been modeled as a multiobjective optimization problem to further study the performance of numerous evolutionary algorithms in designing various cloud migration scenarios. The overall effectiveness of the proposed framework has been examined using a use‐case application scenario and semisynthetic cloud service providers.

A sensor cloud consists of various heterogeneous wireless sensor networks (WSNs). These WSNs may have different owners and run a wide variety of user applications on demand in a wireless communication medium. Hence, they are susceptible to various security attacks. Thus, a need exists to formulate effective and efficient security measures that safeguard these applications impacted from attack in the sensor cloud. However, analyzing the impact of different attacks and their cause-consequence relationship is a prerequisite before security measures can be either developed or deployed. In this paper, we propose a risk assessment framework for WSNs in a sensor cloud that utilizes attack graphs. We use Bayesian networks to not only assess but also to analyze attacks on WSNs. The risk assessment framework will first review the impact of attacks on a WSN and estimate reasonable time frames that predict the degradation of WSN security parameters like confidentiality, integrity and availability. Using our proposed risk assessment framework allows the security administrator to better understand the threats present and take necessary actions against them. The framework is validated by comparing the assessment results with that of the results obtained from different simulated attack scenarios.

Wireless Sensor Networks (WSNs) are popular for their usage in various application like environmental monitoring because of their small size and ease of deployment. However, there is a considerable cost of owning and maintaining involved which might impede small to medium scale industries from availing their services. Even for large-scale industries, WSNs deployed and maintained with so much financial investment, when remains under utilized affects their profitability. This challenge with WSNs needs to be addressed in such a manner that small and medium scale industries could use a hassle free on-demand provisioned and de-provisioned service by paying usage fee only. On the other hand, this should result in a better utilization of the installed capacity of WSNs, pruning down their under-utilization to the barest minimum. In order to address this challenge such that everybody benefits by using the services offered by WSNs and as well as building new services, we have proposed Sensor Cloud infrastructure. The proposed Sensor Cloud infrastructure is a cloud of heterogeneous WSNs, where owners of WSNs collaborate to offer sensing-as-a-service. In this paper, we discuss the implementation of our proposed Sensor Cloud infrastructure by building middleware, client centric-layer which creates virtual sensors on top of physical WSNs, and present the results of its usage and operation.

A Sensor cloud framework is composed of various heterogeneous wireless sensor networks (WSNs) integrated with the cloud platform. Integration with the cloud platform, in addition to the inherent resource and power constrained nature of the sensor nodes makes these WSNs belonging to a sensor cloud susceptible to security attacks. As such there is a need to formulate effective and efficient security measures for such an environment. But in doing so, requires an understanding of the likelihood and impact of different attacks feasible on the WSNs. In this paper, we propose a risk assessment framework for the WSNs belonging to a sensor cloud. The proposed risk assessment framework addresses the feasible set of attacks on a WSN identifying the relationships between them and thus estimating their likelihood and impact. This kind of assessment will give the security administrator a better perspective of their network and help formulating the required security measures.

The acceptance of cloud as a platform to migrate applications has seen a boom in the past few decades. Hosting applications on the cloud cuts down its maintenance and infrastructure costs. Nonetheless security of these applications on the cloud is one of the primary concerns which prevents complete adoption of cloud. Although cloud provides security, they do not address it in terms of application security and thus organizations cannot fully comprehend them. In this paper, we propose an off-line risk assessment framework to evaluate a cloud service provider's security from the point of view of an application to be migrated there. Once the most secure cloud service provider is determined for an application, the framework will perform a cost-benefit tradeoff analysis to estimate an optimal cloud migration strategy.