Internet of Things (IoT) aims to bring every object (e.g. smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive amounts of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real-time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need a evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim, to model IoT and Fog environments and measure the impact of resource management techniques in terms of latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit in terms of RAM consumption and execution time is verified under different circumstances.
The OASIS standard Devices Profile for Web Services (DPWS) aims at enabling the deployment of web services on constrained devices. To accelerate the development of DPWS enabled applications, proposed DPWSim @cite_6 , a simulation toolkit that allows developers to design, develop, and test service-based IoT applications using the DPWS technology without the presence of physical sensors and gateways.
@cite_6
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The OASIS standard Devices Profile for Web Services (DPWS) enables the use of Web services on smart and resource-constrained devices, which are the cornerstones of the Internet of Things (IoT). DPWS sees a perspective of being able to build service-oriented and event-driven IoT applications on top of these devices with secure Web service capabilities and a seamless integration into existing World Wide Web infrastructure. We introduce DPWSim, a simulation toolkit to support the development of such applications. DPWSim allows developers to prototype, develop, and test IoT applications using the DPWS technology without the presence of physical devices. It also can be used for the collaboration between manufacturers, developers, and designers during the new product development process. IoT devices are increasingly being implicated in cyber-attacks, raising community concern about the risks they pose to critical infrastructure, corporations, and citizens. In order to reduce this risk, the IETF is pushing IoT vendors to develop formal specifications of the intended purpose of their IoT devices, in the form of a Manufacturer Usage Description (MUD), so that their network behavior in any operating environment can be locked down and verified rigorously. This paper aims to assist IoT manufacturers in developing and verifying MUD profiles, while also helping adopters of these devices to ensure they are compatible with their organizational policies. Our first contribution is to develop a tool that takes the traffic trace of an arbitrary IoT device as input and automatically generates the MUD profile for it. We contribute our tool as open source, apply it to 28 consumer IoT devices, and highlight insights and challenges encountered in the process. Our second contribution is to apply a formal semantic framework that not only validates a given MUD profile for consistency, but also checks its compatibility with a given organizational policy. Finally, we apply our framework to representative organizations and selected devices, to demonstrate how MUD can reduce the effort needed for IoT acceptance testing. The IETF's push towards standardizing the Manufacturer Usage Description (MUD) grammar and mechanism for specifying IoT device behavior is gaining increasing interest from industry. The ability to control inappropriate communication between devices in the form of access control lists (ACLs) is expected to limit the attack surface on IoT devices; however, little is known about how MUD policies will get enforced in operational networks, and how they will interact with current and future intrusion detection systems (IDS). We believe this paper is the first attempt to translate MUD policies into flow rules that can be enforced using SDN, and in relating exception behavior to attacks that can be detected via off-the-shelf IDS. Our first contribution develops and implements a system that translates MUD policies to flow rules that are proactively configured into network switches, as well as reactively inserted based on run-time bindings of DNS. We use traces of 28 consumer IoT devices taken over several months to evaluate the performance of our system in terms of switch flow-table size and fraction of exception traffic that needs software inspection. Our second contribution identifies the limitations of flow-rules derived from MUD in protecting IoT devices from internal and external network attacks, and we show how our system is able to detect such volumetric attacks (including port scanning, TCP UDP ICMP flooding, ARP spoofing, and TCP SSDP SNMP reflection) by sending only a very small fraction of exception packets to off-the-shelf IDS. In this paper, we propose a hierarchical monitoring intrusion detection system (HAMIDS) for industrial control systems (ICS). The HAMIDS framework detects the anomalies in both level 0 and level 1 of an industrial control plant. In addition, the framework aggregates the cyber-physical process data in one point for further analysis as part of the intrusion detection process. The novelty of this framework is its ability to detect anomalies that have a distributed impact on the cyber-physical process. The performance of the proposed framework evaluated as part of SWaT security showdown (S3) in which six international teams were invited to test the framework in a real industrial control system. The proposed framework outperformed other proposed academic IDS in term of detection of ICS threats during the S3 event, which was held from July 25-29, 2016 at Singapore University of Technology and Design.