The wide variety of application domains makes the Internet of Things (IoT) quite unique among other types of computer networks: IoT networks can be made of devices of different types, i.e., characterized by different hardware, functionalities, computing capabilities, and also network topology and communication protocols may drastically change from one IoT application to another. Such a heterogeneity requires ad-hoc security solutions, as security techniques that are effective in one IoT context may not be so in another context. Furthermore, IoT networks are ever-evolving by their very nature as smart devices can be easily added or removed. These factors call for the design of security tools capable of adapting themselves to the specific IoT instance, but also to the continuous network changes. In this paper we propose a context-aware, Security-as-a-Service based approach for intrusion detection whereby an IDS (i) autonomously collects information about the monitored system, (ii) chooses the best detection strategy accordingly, and (iii) modifies the detection strategy as the network evolves over time. This comprehensive approach to intrusion detection is an attempt to face the heterogeneity which characterizes the IoT in all its aspects, making it possible the design of a security tool able to be self-adaptive and context-aware, that is, effective in different and evolving IoT scenarios with little or no human intervention.

Applications for TinyOS, a popular operating system for embedded systems and wireless sensor networks, are written in nesC, a C dialect prone to the same type and memory safety vulnerabilities as C. While availability and integrity are critical requirements, the distributed and concurrent nature of such applications, combined with the intrinsic unsafety of the language, makes those security goals hard to achieve. Traditional memory safety techniques cannot be applied, due to the strict platform constraints and hardware differences of embedded systems.

In this paper, we introduce Kalis, a self-adapting, knowledge-driven expert Intrusion Detection System able to detect attacks in real time across a wide range of IoT systems. Kalis does not require changes to existing IoT software, can monitor a wide variety of protocols, has no performance impact on applications on IoT devices, and enables collaborative security scenarios. Kalis is the first comprehensive approach to intrusion detection for IoT that does not target individual protocols or applications, and adapts the detection strategy to the specific network features. Extensive evaluation shows that Kalis is effective and efficient in detecting attacks to IoT systems.

The Internet of Things (IoT) is built of many small smart objects continuously connected to the Internet. This makes these devices an easy target for attacks exploiting vulnerabilities at the network, application, and mobile level. With that it comes as no surprise that distributed denial of service attacks leveraging these vulnerable devices have become a new standard for effective botnets. In this paper, we propose Heimdall, a whitelist-based intrusion detection technique tailored to IoT devices. Heimdall operates on routers acting as gateways for IoT as a homogeneous defense for all devices behind the router. Our experimental results show that our defense mechanism is effective and has minimal overhead.

In many Internet of Thing (IoT) application domains security is a critical requirement, because malicious parties can undermine the effectiveness of IoT-based systems by compromising single components and/or communication channels. Thus, a security infrastructure is needed to ensure the proper functioning of such systems even under attack. However, it is also critical that security be at a reasonable resource and energy cost. In this article, we focus on the problem of efficiently and effectively securing IoT networks by carefully allocating security resources in the network area. In particular, given a set of security resources R and a set of attacks to be faced A , our method chooses the subset of R that best addresses the attacks in A , and the set of locations where to place them, that ensure the security coverage of all IoT devices at minimum cost and energy consumption. We model our problem according to game theory and provide a Pareto-optimal solution in which the cost of the security infrastructure, its energy consumption, and the probability of a successful attack are minimized. Our experimental evaluation shows that our technique improves the system robustness in terms of packet delivery rate for different network topologies. Furthermore, we also provide a method for handling the computation of the resource allocation plan for large-scale networks scenarios, where the optimization problem may require an unreasonable amount of time to be solved. We show how our proposed method drastically reduces the computing time, while providing a reasonable approximation of the optimal solution.

Resource constraints, unattended operating environments, and communication phenomena make Wireless Sensor Networks (WSNs) susceptible to operational failures and security attacks. However, applications often impose stringent requirements on data reliability and service availability, due to the deployment of sensor networks in various critical infrastructures. Given the failure- and attack-prone nature of sensor networks, enabling sensor networks to continuously provide their services as well as to effectively recover from attacks is a crucial requirement. We present Kinesis, a security incident response system designed to keep WSNs functional despite anomalies or attacks and to recover from attacks without significant interruption. Kinesis is quick and effective in responding to incidents, distributed in nature, dynamic in selecting response actions based on the context, and lightweight in terms of response policy specification, communication, and energy overhead. A per-node single timer-based distributed strategy to select the most effective response executor in a neighborhood makes the system simple and scalable, while achieving load balancing and redundant action optimization. We implement Kinesis in TinyOS and measure its performance for various application and network layer incidents. Extensive TOSSIM simulations and testbed experiments show that Kinesis successfully counteracts anomalies/attacks and behaves consistently under various attack scenarios and rates.

In many Internet of Thing (IoT) application domains security is a critical requirement, because malicious parties can undermine the effectiveness of IoT-based systems by compromising single components and/or communication channels. Thus, a security infrastructure is needed to ensure the proper functioning of such systems even under attack. In this paper, we focus on the problem of efficiently and effectively securing IoT networks by carefully allocating security tools.

In this paper, we present an architecture and implementation of a secure, automated, proximity-based access control that we refer to as Context-Aware System to Secure Enterprise Content (CASSEC). Using the pervasive WiFi and Bluetooth wireless devices as components in our underlying positioning infrastructure, CASSEC addresses two proximity-based scenarios often encountered in enterprise environments: Separation of Duty and Absence of Other Users. The first scenario is achieved by using Bluetooth MAC addresses of nearby occupants as authentication tokens. The second scenario exploits the interference of WiFi received signal strength when an occupant crosses the line of sight (LOS). Regardless of the scenario, information about the occupancy of a particular location is periodically extracted to support continuous authentication. To the best of our knowledge, our approach is the first to incorporate WiFi signal interference caused by occupants as part of proximity-based access control system. Our results demonstrate that it is feasible to achieve great accuracy in localization of occupants in a monitored room.

In wireless sensor networks, packet losses are often an indicator of possible undergoing attacks. Therefore, security solutions aiming at providing comprehensive protection must include functions for packet-loss detection. Determining the actual cause of these losses is crucial to a quick and automated reaction to the attack, be it a simple reporting of the attack or a more sophisticated action such as packet rerouting and retransmission. Packet losses in wireless sensor networks can be caused by either attacks affecting the nodes or attacks focused on the wireless links. The efficacy of the response to such attacks is highly dependent on an accurate identification of the actual attack cause. Therefore, approaches to correctly identifying the cause of packet losses are needed. The work presented in this article addresses this problem by designing and implementing a fine-grained analysis (FGA) tool that investigates packet-loss events and reports their most likely cause. Our FGA tool profiles the wireless links between the nodes, as well as their neighborhood, by leveraging resident parameters, such as RSSI and LQI, available within every received packet. The design of the system is fully distributed and event-driven, and its low overhead makes it suitable for resource-constrained entities such as wireless motes. We have validated our approach through real-world experiments, showing that our FGA tool is effective in differentiating between the various attacks that may affect nodes and links.

Most of the existing trust-based security schemes for MANETs compute and update the trustworthiness of the other nodes with a fixed frequency. Although this approach works well in some scenarios, some nodes may not be able to afford the periodical trust update due to the limited resources in energy and computation power. To avoid energy depletion and extend the network lifetime, trust-based security schemes need approaches to update the trust taking into account the network conditions at each node. At the same time, a trade-off in terms of packet loss rate, false positives, detection rate, and energy of nodes is needed for network performance. In this paper, we first investigate the impact of trust update frequency on energy consumption and packet loss rate. We then identify network parameters, such as packet transmission rate, packet loss rate, remaining node energy, and rate of link changes, and leverage these parameters to design an Adaptive Trust Update Frequency scheme that takes into account runtime network conditions. The evaluation of our prototype shows significant improvements in the tradeoff between energy saving and packet loss rate over traditional fixed-frequency approaches.

As privacy today is a major concern for mobile systems, network anonymizers are widely available on smartphones systems, such as Android. However, in many cases applications are still able to identify the user and the device by means different from the IP address. In this demo we show two solutions that address this problem by providing application-level anonymity. The first solution shadows sensitive data that can reveal the user identity. The second solutions dynamically revokes Android application permissions associated with sensitive information at run-time. In addition, both solutions offer protection from applications that identify their users through traces left in the application's data storage or by exchanging identifying data messages. We developed IdentiDroid, a customized Android operating system, to deploy these solutions, and built IdentiDroid Profile Manager, a profile-based configuration tool for setting different configurations for each installed Android application.

Packet losses in a wireless sensor network represent an indicator of possible attacks to the network. Detecting and reacting to such losses is thus an important component of any comprehensive security solution. However, in order to quickly and automatically react to such a loss, it is important to determine the actual cause of the loss. In a wireless sensor network, packet losses can result from attacks affecting the nodes or the wireless links connecting the nodes. Failure to identify the actual attack can undermine the efficacy of the attack responses. We thus need approaches to correctly identify the cause of packet losses. In this paper, we address this problem by proposing and building a fine-grained analysis (FGA) tool that investigates the causes of packet losses and reports the most likely cause of these losses. Our tool uses parameters, e.g. RSSI and LQI, present within every received packet to profile the links between nodes and their corresponding neighborhood. Through real-world experiments, we have validated our approach and shown that our tool is able to differentiate between the various attacks that may affect the nodes and the links.

This paper presents Kinesis, a security incident response and prevention system for wireless sensor networks, designed to keep the network functional despite anomalies or attacks and to recover from attacks without significant interruption. Due to the deployment of sensor networks in various critical infrastructures, the applications often impose stringent requirements on data reliability and service availability. Given the failure- and attack-prone nature of sensor networks, it is a pressing concern to enable the sensor networks provide continuous and unobtrusive services.

In a wireless sensor networks, packet losses can result from attacks affecting the nodes or the wireless links connecting the nodes. Failure to identify the actual attack can undermine the efficacy of the attack responses. We thus need approaches to correctly identify the cause of packet losses. In this poster paper, we address this problem by proposing and building a fine-grained analysis (FGA) tool that investigates the causes of packet losses and reports the most likely cause of these losses. Our tool uses parameters, e.g. RSSI and LQI, transmitted with every received packet to profile the links between nodes and their corresponding neighborhood. Through real-world experiments, we have validated our approach and shown that our tool is able to differentiate between the various attacks that may affect the nodes and the links.