Resilient networking in wireless sensor networks

This report deals with security in wireless sensor networks (WSNs), especially in network layer. Multiple secure routing protocols have been proposed in the literature. However, they often use the cryptography to secure routing functionalities. The cryptography alone is not enough to defend against multiple attacks due to the node compromise. Therefore, we need more algorithmic solutions. In this report, we focus on the behavior of routing protocols to determine which properties make them more resilient to attacks. Our aim is to find some answers to the following questions. Are there any existing protocols, not designed initially for security, but which already contain some inherently resilient properties against attacks under which some portion of the network nodes is compromised? If yes, which specific behaviors are making these protocols more resilient? We propose in this report an overview of security strategies for WSNs in general, including existing attacks and defensive measures. In this report we focus at the network layer in particular, and an analysis of the behavior of four particular routing protocols is provided to determine their inherent resiliency to insider attacks. The protocols considered are: Dynamic Source Routing (DSR), Gradient-Based Routing (GBR), Greedy Forwarding (GF) and Random Walk Routing (RWR)

A Study of IEEE 802.15.4 Security Framework for Wireless Body Area Network

A Wireless Body Area Network (WBAN) is a collection of low-power and lightweight wireless sensor nodes that are used to monitor the human body functions and the surrounding environment. It supports a number of innovative and interesting applications, including ubiquitous healthcare and Consumer Electronics (CE) applications. Since WBAN nodes are used to collect sensitive (life-critical) information and may operate in hostile environments, they require strict security mechanisms to prevent malicious interaction with the system. In this paper, we first highlight major security requirements and Denial of Service (DoS) attacks in WBAN at Physical, Medium Access Control (MAC), Network, and Transport layers. Then we discuss the IEEE 802.15.4 security framework and identify the security vulnerabilities and major attacks in the context of WBAN. Different types of attacks on the Contention Access Period (CAP) and Contention Free Period (CFP) parts of the superframe are analyzed and discussed. It is observed that a smart attacker can successfully corrupt an increasing number of GTS slots in the CFP period and can considerably affect the Quality of Service (QoS) in WBAN (since most of the data is carried in CFP period). As we increase the number of smart attackers the corrupted GTS slots are eventually increased, which prevents the legitimate nodes to utilize the bandwidth efficiently. This means that the direct adaptation of IEEE 802.15.4 security framework for WBAN is not totally secure for certain WBAN applications. New solutions are required to integrate high level security in WBAN.Comment: 14 pages, 7 figures, 2 table

Security in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) pose a new challenge to network designers in the area of developing better and secure routing protocols. Many sensor networks have mission-critical tasks, so it is clear that security needs to be taken into account at design time. However, sensor networks are not traditional computing devices, and as a result, existing security models and methods are ill suited. The security issues posed by sensor networks represent a rich field of research problems. Improving network hardware and software may address many of the issues, but others will require new supporting technologies. With the recent surge in the use of sensor networks, for example, in ubiquitous computing and body sensor networks (BSNs) the need for security mechanisms has a more important role. Recently proposed solutions address but a small subset of current sensor network attacks. Also because of the special battery requirements for such networks, normal cryptographic network solutions are irrelevant. New mechanisms need to be developed to address this type of network

Bio-inspired enhancement of reputation systems for intelligent environments

Providing security to the emerging field of ambient intelligence will be difficult if we rely only on existing techniques, given their dynamic and heterogeneous nature. Moreover, security demands of these systems are expected to grow, as many applications will require accurate context modeling. In this work we propose an enhancement to the reputation systems traditionally deployed for securing these systems. Different anomaly detectors are combined using the immunological paradigm to optimize reputation system performance in response to evolving security requirements. As an example, the experiments show how a combination of detectors based on unsupervised techniques (self-organizing maps and genetic algorithms) can help to significantly reduce the global response time of the reputation system. The proposed solution offers many benefits: scalability, fast response to adversarial activities, ability to detect unknown attacks, high adaptability, and high ability in detecting and confining attacks. For these reasons, we believe that our solution is capable of coping with the dynamism of ambient intelligence systems and the growing requirements of security demands

Accurate Sybil attack detection based on fine-grained physical channel information

With the development of the Internet-of-Things (IoT), wireless network security has more and more attention paid to it. The Sybil attack is one of the famous wireless attacks that can forge wireless devices to steal information from clients. These forged devices may constantly attack target access points to crush the wireless network. In this paper, we propose a novel Sybil attack detection based on Channel State Information (CSI). This detection algorithm can tell whether the static devices are Sybil attackers by combining a self-adaptive multiple signal classification algorithm with the Received Signal Strength Indicator (RSSI). Moreover, we develop a novel tracing scheme to cluster the channel characteristics of mobile devices and detect dynamic attackers that change their channel characteristics in an error area. Finally, we experiment on mobile and commercial WiFi devices. Our algorithm can effectively distinguish the Sybil devices. The experimental results show that our Sybil attack detection system achieves high accuracy for both static and dynamic scenarios. Therefore, combining the phase and similarity of channel features, the multi-dimensional analysis of CSI can effectively detect Sybil nodes and improve the security of wireless network

Secure Transmission in Wireless Sensor Networks Data Using Linear Kolmogorov Watermarking Technique

In Wireless sensor networks (WSNs), All communications between different nodes are sent out in a broadcast fashion. These networks are used in a variety of applications including military, environmental, and smart spaces. Sensors are susceptible to various types of attack, such as data modification, data insertion and deletion, or even physical capture and sensor replacement. Hence security becomes important issue in WSNs. However given the fact that sensors are resources constrained, hence the traditional intensive security algorithms are not well suited for WSNs. This makes traditional security techniques, based on data encryption, not very suitable for WSNs. This paper proposes Linear Kolmogorov watermarking technique for secure data communication in WSNs. We provide a security analysis to show the robustness of the proposed techniques against various types of attacks. This technique is robust against data deletion, packet replication and Sybil attack

Accurate Sybil attack detection based on fine-grained physical channel information

With the development of the Internet-of-Things (IoT), wireless network security has more and more attention paid to it. The Sybil attack is one of the famous wireless attacks that can forge wireless devices to steal information from clients. These forged devices may constantly attack target access points to crush the wireless network. In this paper, we propose a novel Sybil attack detection based on Channel State Information (CSI). This detection algorithm can tell whether the static devices are Sybil attackers by combining a self-adaptive multiple signal classification algorithm with the Received Signal Strength Indicator (RSSI). Moreover, we develop a novel tracing scheme to cluster the channel characteristics of mobile devices and detect dynamic attackers that change their channel characteristics in an error area. Finally, we experiment on mobile and commercial WiFi devices. Our algorithm can effectively distinguish the Sybil devices. The experimental results show that our Sybil attack detection system achieves high accuracy for both static and dynamic scenarios. Therefore, combining the phase and similarity of channel features, the multi-dimensional analysis of CSI can effectively detect Sybil nodes and improve the security of wireless network

Guaranteeing Spoof-Resilient Multi-Robot Networks

Multi-robot networks use wireless communication to provide wide-ranging services such as aerial surveillance and unmanned delivery. However, effective coordination between multiple robots requires trust, making them particularly vulnerable to cyber-attacks. Specifically, such networks can be gravely disrupted by the Sybil attack, where even a single malicious robot can spoof a large number of fake clients. This paper proposes a new solution to defend against the Sybil attack, without requiring expensive cryptographic key-distribution. Our core contribution is a novel algorithm implemented on commercial Wi-Fi radios that can "sense" spoofers using the physics of wireless signals. We derive theoretical guarantees on how this algorithm bounds the impact of the Sybil Attack on a broad class of robotic coverage problems. We experimentally validate our claims using a team of AscTec quadrotor servers and iRobot Create ground clients, and demonstrate spoofer detection rates over 96%