New Container Architectures for Mobile, Drone, and Cloud Computing

Containers are increasingly used across many different types of computing to isolate and control apps while efficiently sharing computing resources. By using lightweight operating system virtualization, they can provide apps with a virtual computing abstraction while imposing minimal hardware requirements and a small footprint. My thesis is that new container architectures can provide additional functionality, better resource utilization, and stronger security for mobile, drone, and cloud computing. To demonstrate this, we introduce three new container architectures that enable new mobile app migration functionality, a new notion of virtual drones and efficient utilization of drone hardware, and stronger security for cloud computing by protecting containers against untrusted operating systems. First, we introduce Flux to support multi-surface apps, apps that seamlessly run across multiple user devices, through app migration. Flux introduces two key mechanisms to overcome device heterogeneity and residual dependencies associated with app migration to enable app migration. Selective Record/Adaptive Replay to record just those device-agnostic app calls that lead to the generation of app-specific device-dependent state in services and replay them on the target. Checkpoint/Restore in Android (CRIA) to transition an app into a state in which device-specific information the app contains can be safely discarded before checkpointing and restoring the app within a containerized environment on the new device. Second, we introduce AnDrone, a drone-as-a-service solution that makes drones accessible in the cloud. AnDrone provides a drone virtualization architecture to leverage the fact that computational costs are cheap compared to the operational and energy costs of putting a drone in the air. This enables multiple virtual drones to run simultaneously on the same physical drone at very little additional cost. To enable multiple virtual drones to run in an isolated and secure manner, each virtual drone runs its own containerized operating system instance. AnDrone introduces a new device container architecture, providing virtual drones with secure access to a full range of drone hardware devices, including sensors such as cameras and geofenced flight control. Finally, we introduce BlackBox, a new container architecture that provides fine-grain protection of application data confidentiality and integrity without the need to trust the operating system. BlackBox introduces a container security monitor, a small trusted computing base that creates separate and independent physical address spaces for each container, such that there is no direct information flow from container to operating system or other container physical address spaces. Containerized apps do not need to be modified, can still make full use of operating system services via system calls, yet their CPU and memory state are isolated and protected from other containers and the operating system

Dynamic particle swarm optimization of biomolecular simulation parameters with flexible objective functions

Molecular simulations are a powerful tool to complement and interpret ambiguous experimental data on biomolecules to obtain structural models. Such data-assisted simulations often rely on parameters, the choice of which is highly non-trivial and crucial to performance. The key challenge is weighting experimental information with respect to the underlying physical model. We introduce FLAPS, a self-adapting variant of dynamic particle swarm optimization, to overcome this parameter selection problem. FLAPS is suited for the optimization of composite objective functions that depend on both the optimization parameters and additional, a priori unknown weighting parameters, which substantially influence the search-space topology. These weighting parameters are learned at runtime, yielding a dynamically evolving and iteratively refined search-space topology. As a practical example, we show how FLAPS can be used to find functional parameters for small-angle X-ray scattering-guided protein simulations

Wireless intrusion detection system using fingerprinting

Wireless network is the network which is easy to deploy and very easy to access that network and that network is user friendly. The main reason behind of getting popular is because it provide benefits, like as easy to installation, flexibility, mobility, scalability and reduced cost-of-ownership. But drawback in these wireless networks is that it doesn't provide security as much as required, due to that user faces attacks of various types which are damageable to user information. One of the serious attack is Identity based attacks which steals the identity of some other user in that network and performed some other attack. The available present security tools to detect such these identity(spoofed MAC) based attacks are quite limited. In this proposed work a new technique is developed for detecting masquerade(identity) attacks or spoofed MAC attack exploited in 802.11 wireless network. Current methods of device fingerprinting includes only probe request packets fingerprinting, which results in large amount of false positive. In our proposed work fingerprint is created on basis of three frames which are required in three section of connectivity phase and that frames are probe request frame, authentication frame and association frame. Time differences between consecutive frames are take into consideration and on the basis of that fingerprint is created of different device. In this proposed technique cross-correlation method is used to estimate the signals similarity in terms of time lagging to each other. Those signals are captured by different devices. Stored signature of actual device and captured signal of transmitting device is compared using this technique and after that result analysis, identification of device is done