Hardware Trojans (HTs) pose a serious threat to the security of Integrated Circuits (ICs). Detecting HTs in an IC is an important but difficult problem due to the wide spectrum of HTs and their stealthy nature. While researchers have been working on enhancing traditional IC tests and developing new methods to try to detect Trojans, there is still a possibility a Trojan will avoid detection during test time and be activated once the chip is in use. A runtime Trojan detection system could monitor an IC during its operational life time and provide a last-line of defense. However, most runtime approaches are infeasible due to the overhead introduced by additional hardware, or computational complexity, or both. In this paper, we propose a hardware-based runtime detection model that overcomes the aforementioned constraints. It applies chaos theory, which has been shown to be effective in several other domains, to characterize dynamic data in a reconstructed phase space, which helps us describe, analyze, and interpret power consumption data (whether chaotic or not). The proposed chaos based approach does not make any assumption on the statistical distribution of power consumption, this makes our model applicable for runtime use given the fact that power consumption is very dynamic as well as heavily application and data dependent. Hardware overhead, which is the main challenge for runtime approaches, is reduced by taking advantage of available thermal sensors present in most modern ICs. For real world implementation, thermal sensor noise cancelation is considered in our proposed model. Our simulation results for detecting Trojans on publicly available Trojan benchmarks demonstrate that the proposed model outperforms the current runtime Trojan detection approaches in terms of detection rate, computational complexity, and implementation feasibility. Approved for Public Release; Distribution Unlimited: 88ABW-2016-4308; Dated 31 AUG 2016.

With the growth of cloud computing, many businesses, both small and large, are opting to use cloud services compelled by a great cost savings potential. This is especially true of public cloud computing which allows for quick, dynamic scalability without many overhead or long-term commitments. However, one of the largest dissuasions from using cloud services comes from the inherent and unknown danger of a shared platform such as the hyper visor. An attacker can attack a virtual machine (VM) and then go on to compromise the hyper visor. If successful, then all virtual machines on that hyper visor can become compromised. This is the problem of negative externalities, where the security of one player affects the security of another. This work shows that there are multiple Nash equilibria for the public cloud security game. It also demonstrates that we can allow the players' Nash equilibrium profile to not be dependent on the probability that the hyper visor is compromised, reducing the factor externality plays in calculating the equilibrium. Finally, by using our allocation method, the negative externality imposed onto other players can be brought to a minimum compared to other common VM allocation methods.

As cloud computing thrives, many small organizations are joining a public cloud to take advantage of its multiple benefits. Cloud computing is cost efficient, i.e., cloud user can reduce spending on technology infrastructure and have easy access to their information without up-front or long-term commitment of resources. Moreover, a cloud user can dynamically grow and shrink the resources provisioned to an application on demand. Despite those benefits, cyber security concern is the main reason many large organizations with sensitive information such as the Department of Defense have been reluctant to join a public cloud. This is because different public cloud users share a common platform such as the hypervisor. A common platform intensifies the well-known problem of cyber security interdependency. In fact, an attacker can compromise a virtual machine (VM) to launch an attack on the hypervisor which if compromised can instantly yield the compromising of all the VMs running on top of that hypervisor. Therefore, a user that does not invest in cyber security imposes a negative externality on others. This research uses the mathematical framework of game theory to analyze the cause and effect of interdependency in a public cloud platform. This work shows that there are multiple possible Nash equilibria of the public cloud security game. However, the players use a specific Nash equilibrium profile depending on the probability that the hypervisor is compromised given a successful attack on a user and the total expense required to invest in security. Finally, there is no Nash equilibrium in which all the users in a public cloud will fully invest in security.