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Positioning systems can be found everywhere, from the navigation system of a car to the smart watch that tracks the running performances of its wearer. With the generalization of positioning systems, new use cases have begun to emerge that require or could benefit from increased accuracy. While the technology has been detailed in the literature for several years, the deployment of positioning techniques at the centimeter level has proved challenging. In this paper we propose OpenPosLib, an open source library that aims to fill the gap between all the components needed to achieve a centimetric accuracy and the user-facing application. Our objective is to remove most of the complexity needed to obtain centimetric accuracy from the developer so as to enable end-users to reap the benefits of more applications that leverage centimetric accuracy. Our results show that when coupled with inexpensive hardware, OpenPosLib enables users to get centimetric precision on a budget.

With the arrival of the Internet of Things (IoT), more devices appear online with default credentials or lacking proper security protocols. Consequently, we have seen a rise of powerful DDoS attacks originating from IoT devices in the last years. In most cases the devices were infected by bot malware through the telnet protocol. This has lead to several honeypot studies on telnet-based attacks. However, IoT installations also involve other protocols, for example for Machine-to-Machine communication. Those protocols often provide by default only little security. In this paper, we present a measurement study on attacks against or based on those protocols. To this end, we use data obtained from a /15 network telescope and three honey-pots with 15 IPv4 addresses. We find that telnet-based malware is still widely used and that infected devices are employed not only for DDoS attacks but also for crypto-currency mining. We also see, although at a much lesser frequency, that attackers are looking for IoT-specific services using MQTT, CoAP, UPnP, and HNAP, and that they target vulnerabilities of routers and cameras with HTTP.

The Internet of Things (IoT) is not one single entity, but a collection of different devices, communication technologies, protocols and services. IoT systems can span a large number of individually managed networks that are interconnected through the Internet and host the different components of an IoT application, such as sensor devices, storage servers and data processing services. Protecting such a complex multiparty system from abuse becomes a very challenging task. New difficulties arise everyday when policies are updated or new collaborations and federations appear between entities. Moreover, hacked IoT devices can also become the source of powerful attacks, as the Mirai malware has demonstrated, and therefore a danger for the other involved parties. In this paper, we propose an approach to improve the management and protection of collaborating IoT systems using distributed intrusion detection and permission-based access control. Our approach is based on interconnected middleboxes that monitor the communication between the various IoT networks and are able to stop incoming as well as outgoing attacks. We evaluate our approach through experiments with different types of attacks.

With the arrival of the Internet of Things (IoT), more devices appear online with default credentials or lacking proper security protocols. Consequently, we have seen a rise of powerful DDoS attacks originating from IoT devices in the last years. In most cases the devices were infected by bot malware through the telnet protocol. This has lead to several honeypot studies on telnet-based attacks. However, IoT installations also involve other protocols, for example for Machine-to-Machine communication. Those protocols often provide by default only little security. In this paper, we present a measurement study on attacks against or based on those protocols. To this end, we use data obtained from a /15 network telescope and three honey-pots with 15 IPv4 addresses. We find that telnet-based malware is still widely used and that infected devices are employed not only for DDoS attacks but also for crypto-currency mining. We also see, although at a much lesser frequency, that attackers are looking for IoT-specific services using MQTT, CoAP, UPnP, and HNAP, and that they target vulnerabilities of routers and cameras with HTTP.

The Internet of Things leads to the inter-connectivity of a wide range of devices. This heterogeneity of hardware and software poses significant challenges to security. Constrained IoT devices often do not have enough resources to carry the overhead of an intrusion protection system or complex security protocols. A typical initial step in network security is a network scan in order to find vulnerable nodes. In the context of IoT, the initiator of the scan can be particularly interested in finding constrained devices, assuming that they are easier targets. In IoT networks hosting devices of various types, performing a scan with a high discovery rate can be a challenging task, since low-power networks such as IEEE 802.15.4 are easily overloaded. In this paper, we propose an approach to increase the efficiency of network scans by combining them with active network measurements. The measurements allow the scanner to differentiate IoT nodes by the used network technology. We show that the knowledge gained from this differentiation can be used to control the scan strategy in order to reduce probe losses.

The Internet of Things (IoT) leads to the interconnectivity of a wide range of device types running an equally wide range of operating systems and applications. This heterogeneity of hardware and software poses significant challenges to security. Constrained IoT devices often do not have enough resources to carry the overhead of an intrusion protection system or complex security protocols. Furthermore, they are often not properly managed and updated. Network scans are a valuable tool to discover vulnerable devices. In the context of IoT, the initiator of the scan can be particularly interested in finding constrained devices, assuming that they are easier targets for attacks. However, in IoT networks hosting devices of various types, performing a scan with a high discovery rate can be a challenging task, since a scan working well for, eg, a WiFi network might easily overload a low-power network such as IEEE 802.15.4. In this paper, we propose an approach to increase the efficiency of network scans in heterogeneous environments by combining them with active round-trip time measurements. The measurements allow the scanner to differentiate IoT nodes by the used network technology. Using the knowledge gained from this differentiation, our approach adapts the scan strategy to reduce probe losses, and hence the speed and efficiency of the scan. We validate our approach through simulations of a mixed IoT infrastructure consisting of WiFi and multihop IEEE 802.15.4 subnetworks.