Nano-networks communication architecture: Modeling and functions

Nano-network is a communication network at the Nano-scale between Nano-devices. Nano-devices face certain challenges in functionalities, because of limitations in their processing capabilities and power management. Hence, these devices are expected to perform simple tasks, which require different and novel approaches. In order to exploit different functionalities of Nano-machines, we need to manage and control a set of Nano-devices in a full Nano-network using an appropriate architecture. This step will enable unrivaled applications in the biomedical, environmental and industrial fields. By the arrival of Internet of Things (IoT) the use of the Internet has transformed, where various types of objects, sensors and devices can interact making our future networks connect nearly everything from traditional network devices to people. In this paper, we provide an unified architectural model of Nano-network communication with a layered approach combining Software Defined Network (SDN), Network Function Virtualization (NFV) and IoT technologies and present how this combination can help in Nano-networks’ context. Consequently, we propose a set of functions and use cases that can be implemented by Nano-devices and discuss the significant challenges in implementing these functions with Nano-technology paradigm and the open research issues that need to be addressed.Peer ReviewedPostprint (published version

Autonomic care platform for optimizing query performance

Background: As the amount of information in electronic health care systems increases, data operations get more complicated and time-consuming. Intensive Care platforms require a timely processing of data retrievals to guarantee the continuous display of recent data of patients. Physicians and nurses rely on this data for their decision making. Manual optimization of query executions has become difficult to handle due to the increased amount of queries across multiple sources. Hence, a more automated management is necessary to increase the performance of database queries. The autonomic computing paradigm promises an approach in which the system adapts itself and acts as self-managing entity, thereby limiting human interventions and taking actions. Despite the usage of autonomic control loops in network and software systems, this approach has not been applied so far for health information systems. Methods: We extend the COSARA architecture, an infection surveillance and antibiotic management service platform for the Intensive Care Unit (ICU), with self-managed components to increase the performance of data retrievals. We used real-life ICU COSARA queries to analyse slow performance and measure the impact of optimizations. Each day more than 2 million COSARA queries are executed. Three control loops, which monitor the executions and take action, have been proposed: reactive, deliberative and reflective control loops. We focus on improvements of the execution time of microbiology queries directly related to the visual displays of patients' data on the bedside screens. Results: The results show that autonomic control loops are beneficial for the optimizations in the data executions in the ICU. The application of reactive control loop results in a reduction of 8.61% of the average execution time of microbiology results. The combined application of the reactive and deliberative control loop results in an average query time reduction of 10.92% and the combination of reactive, deliberative and reflective control loops provides a reduction of 13.04%. Conclusions: We found that by controlled reduction of queries' executions the performance for the end-user can be improved. The implementation of autonomic control loops in an existing health platform, COSARA, has a positive effect on the timely data visualization for the physician and nurse

AUTOMATED NETWORK SECURITY WITH EXCEPTIONS USING SDN

Campus networks have recently experienced a proliferation of devices ranging from personal use devices (e.g. smartphones, laptops, tablets), to special-purpose network equipment (e.g. firewalls, network address translation boxes, network caches, load balancers, virtual private network servers, and authentication servers), as well as special-purpose systems (badge readers, IP phones, cameras, location trackers, etc.). To establish directives and regulations regarding the ways in which these heterogeneous systems are allowed to interact with each other and the network infrastructure, organizations typically appoint policy writing committees (PWCs) to create acceptable use policy (AUP) documents describing the rules and behavioral guidelines that all campus network interactions must abide by. While users are the audience for AUP documents produced by an organization\u27s PWC, network administrators are the responsible party enforcing the contents of such policies using low-level CLI instructions and configuration files that are typically difficult to understand and are almost impossible to show that they do, in fact, enforce the AUPs. In other words, mapping the contents of imprecise unstructured sentences into technical configurations is a challenging task that relies on the interpretation and expertise of the network operator carrying out the policy enforcement. Moreover, there are multiple places where policy enforcement can take place. For example, policies governing servers (e.g., web, mail, and file servers) are often encoded into the server\u27s configuration files. However, from a security perspective, conflating policy enforcement with server configuration is a dangerous practice because minor server misconfigurations could open up avenues for security exploits. On the other hand, policies that are enforced in the network tend to rarely change over time and are often based on one-size-fits-all policies that can severely limit the fast-paced dynamics of emerging research workflows found in campus networks. This dissertation addresses the above problems by leveraging recent advances in Software-Defined Networking (SDN) to support systems that enable novel in-network approaches developed to support an organization\u27s network security policies. Namely, we introduce PoLanCO, a human-readable yet technically-precise policy language that serves as a middle-ground between the imprecise statements found in AUPs and the technical low-level mechanisms used to implement them. Real-world examples show that PoLanCO is capable of implementing a wide range of policies found in campus networks. In addition, we also present the concept of Network Security Caps, an enforcement layer that separates server/device functionality from policy enforcement. A Network Security Cap intercepts packets coming from, and going to, servers and ensures policy compliance before allowing network devices to process packets using the traditional forwarding mechanisms. Lastly, we propose the on-demand security exceptions model to cope with the dynamics of emerging research workflows that are not suited for a one-size-fits-all security approach. In the proposed model, network users and providers establish trust relationships that can be used to temporarily bypass the policy compliance checks applied to general-purpose traffic -- typically by network appliances that perform Deep Packet Inspection, thereby creating network bottlenecks. We describe the components of a prototype exception system as well as experiments showing that through short-lived exceptions researchers can realize significant improvements for their special-purpose traffic

Towards Bayesian-Based Trust Management for Insider Attacks in Healthcare Software-Defined Networks

© 2004-2012 IEEE. The medical industry is increasingly digitalized and Internet-connected (e.g., Internet of Medical Things), and when deployed in an Internet of Medical Things environment, software-defined networks (SDNs) allow the decoupling of network control from the data plane. There is no debate among security experts that the security of Internet-enabled medical devices is crucial, and an ongoing threat vector is insider attacks. In this paper, we focus on the identification of insider attacks in healthcare SDNs. Specifically, we survey stakeholders from 12 healthcare organizations (i.e., two hospitals and two clinics in Hong Kong, two hospitals and two clinics in Singapore, and two hospitals and two clinics in China). Based on the survey findings, we develop a trust-based approach based on Bayesian inference to figure out malicious devices in a healthcare environment. Experimental results in either a simulated and a real-world network environment demonstrate the feasibility and effectiveness of our proposed approach regarding the detection of malicious healthcare devices, i.e., our approach could decrease the trust values of malicious devices faster than similar approaches