Cloud providers attempt to maintain the highest levels of isolation between Virtual Machines (VMs) and inter-user processes to keep co-located VMs and processes separate. This logical isolation creates an internal virtual network to separate VMs co-residing within a shared physical network. However, as co-residing VMs share their underlying VMM (Virtual Machine Monitor), virtual network, and hardware are susceptible to cross VM attacks. It is possible for a malicious VM to potentially access or control other VMs through network connections, shared memory, other shared resources, or by gaining the privilege level of its non-root machine. This research presents a two novel zero-day cross-VM network channel attacks. In the first attack, a malicious VM can redirect the network traffic of target VMs to a specific destination by impersonating the Virtual Network Interface Controller (VNIC). The malicious VM can extract the decrypted information from target VMs by using open source decryption tools such as Aircrack. The second contribution of this research is a privilege escalation attack in a cross VM cloud environment with Xen hypervisor. An adversary having limited privileges rights may execute Return-Oriented Programming (ROP), establish a connection with the root domain by exploiting the network channel, and acquiring the tool stack (root domain) which it is not authorized to access directly. Countermeasures against this attacks are also presented

Cross-Cloud Federation (CCF) enables resource exchange among multiple, heterogeneous Cloud Service Providers (CSPs) to support the composition of services (workflow) hosted by different providers. CCF participation can either be fixed, or the types of services that can be used are limited to reduce the potential risk of service failure or secure access. Although many service selection approaches have been proposed in literature for cloud computing, their applicability to CCF i.e., cloud-to-cloud interaction, has not been adequately investigated. A key component of this cloud-to-cloud paradigm involves assessing the combined capability of contributing participants within a federation and their connectivity. A novel Aggregated Capability Assessment (AgCA) approach based on using the Consensus Assessment Initiative Questionnaire from Cloud Security Alliance is proposed for CCF. The proposed mechanism is implemented as a component of a centralized broker to enhance the quality of the selection process for participants within a federation. Our experimental results show thatAgCAis a useful tool for partner selection in a dynamic, heterogeneous and multilevel cloud federation.

In this article, we investigate the wireless power transfer and energy-efficiency (EE) optimization problem for nano-empowered vehicular networks operating over the terahertz band. The nano-sensors in air can harvest energy from a power station and then can transmit the trace information to the micro-device under reconnaissance vehicular scenarios. Hence, by considering the properties of the terahertz band, we develop a long-term EE optimization problem. Furthermore, with the help of the equivalent transformation method, we converted the EE optimization problem into a series of energy-efficient resource allocation problems over the time slots. Each reformulated optimization problem becomes a mixed integer nonlinear programming (MINLP) over a time slot. Hence, to obtain the sub-optimal solution of the reformulated optimization problem, we developed a Quantum-behaved Particle swarm-based EE Optimization (QPEEO) algorithm. Furthermore, by exploiting the special structure of the reformulated problem, we propose an Improved Discrete Particle swarm-based EE Optimization (IDPEEO) algorithm. The proposed IDPEEO algorithm handles the problem's constraints effectively, and greatly reduces the search space and the convergence time. Our simulation results validate the theoretical analysis of the proposed scheme.

A Tactile Internet-based nanonetwork is an emerging field that promises a new range of e-health applications, in which human operators can efficiently operate and control devices at the nanoscale for remote-patient treatment. A haptic feedback is inevitable for establishing a link between the operator and unknown in-body environment. However, haptic communications over the terahertz band may incur significant path loss due to molecular absorption. In this paper, we propose an optimization framework for haptic communications over nanonetworks, in which in-body nanodevices transmit haptic information to an operator via the terahertz band. By considering the properties of the terahertz band, we employ Brownian motion to describe the mobility of the nanodevices and develop a time-variant terahertz channel model. Furthermore, based on the developed channel model, we construct a stochastic optimization problem for improving haptic communications under the constraints of system stability, energy consumption, and latency. To solve the formulated nonconvex stochastic problem, an improved time-varying particle swarm optimization algorithm is presented, which can deal with the constraints of the problem efficiently by reducing the convergence time significantly. The simulation results validate the theoretical analysis of the proposed system.

Data privacy and security is a leading concern for providers and customers of cloud computing, where Virtual Machines (VMs) can co-reside within the same underlying physical machine. Side channel attacks within multi-tenant virtualized cloud environments are an established problem, where attackers are able to monitor and exfiltrate data from co-resident VMs. Virtualization services have attempted to mitigate such attacks by preventing VM-to-VM interference on shared hardware by providing logical resource isolation between co-located VMs via an internal virtual network. However, such approaches are also insecure, with attackers capable of performing network channel attacks which bypass mitigation strategies using vectors such as ARP Spoofing, TCP/IP steganography, and DNS poisoning. In this paper we identify a new vulnerability within the internal cloud virtual network, showing that through a combination of TAP impersonation and mirroring, a malicious VM can successfully redirect and monitor network traffic of VMs co-located within the same physical machine. We demonstrate the feasibility of this attack in a prominent cloud platform - OpenStack - under various security requirements and system conditions, and propose countermeasures for mitigation.

Recently, nodes cooperation has emerged as a popular means for improving the quality of multimedia delivery over fifth‐generation cellular networks. However, in the conventional relaying scheme such as amplify‐and‐forward (AaF), there is a higher probability of duplicate packets at the receiver node which affect the decoding probability and consequently deteriorate the quality of multimedia transmission. In this study, the authors propose a cooperative multimedia transmission protocol based on a novel merge‐and‐forward relaying and the best relay selection (RS) schemes. Their best RS scheme is based on two important parameters: (i) two‐hop link distances and (ii) minimum block error rate value. Moreover, to combat the packet loss for enhanced and reliable video delivery, they adopt application layer forward error correction scheme which is based on the most improved and advanced version of fountain codes (i.e. RaptorQ codes). They evaluate the performance of the proposed scheme under different time‐sharing scenarios between the direct and best indirect transmission links in terms of decoding failure probability, decoding overhead, peak signal‐to‐noise ratio, and mean opinion score. Simulation results show that the proposed scheme outperforms the conventional AaF relaying scheme.

In order to obtain comprehensive research analysis of mobility management schemes and routing protocols for Wireless Communication, it is prerequisite that the mobility model on which the simulation is based, shows a realistic mobility pattern. A key problem with much of the literature on random models is that they produce unrealistic node movement trajectory and have limitations viz., border effect, un-sociological behavior and memory less mobility phenomenon. This paper presents a novel community mobility model, which provide a foundation for generating realistic mobility traces and mimics real-world scenarios. The analytical results validate its performance, extensive simulation experiments are conducted through steady state space analysis.

In the dynamic environment of robots, the information of devices mounted on the robot may have alternative representations. Particularly, if the devices of the robot use different programming languages, operating platform and data management standards may restricts the ability of devices to interact with each other flawlessly. In order to handle such situations, Service-Oriented Architecture (SOA) can be used which can handle the complexity (different programming languages, operating platform and data standards) with the help of ontologies. Although the researchers working in the area of robots have achieved a big milestone in producing small, easily pluggable and energy-efficient sensors but still the research on the requirements like extensibility, correctness, modularity, relationship and activity monitoring is need to be researched in-order to achieve dynamicity and flexibility while managing heterogeneity complexity which restricts the ability of devices to interact with each other flawlessly. Therefore, for correct interaction among robot sensors and sharing of data between them, we have developed an robot ontology (OntAPIbot) for defining the formal specification of concepts in-order to reduce the heterogeneity and to facilitate the reasoning tools for correct knowledge interpretation and reuse.