The knowledge of sensors' locations is crucial information for many applications in Wireless Sensor Networks (WSNs). When sensor nodes are deployed in hostile environments, the localization schemes are vulnerable to various attacks, e.g., wormhole attack, pollution attack, range enlargement/reduction attack, and etc. Therefore, sensors' locations are not trustworthy and need to be verified before they can be used by location-based applications. Previous verification schemes either require group-based deployment knowledge of the sensor field, or depend on expensive or dedicated hardware, thus they cannot be used for low-cost sensor networks. In this paper, we propose a lightweight location verification system that performs both “on-spot” and “in-region” location verifications. The on-spot verification intends to verify whether the locations claimed by sensors are far from their true spots beyond a certain distance. We propose two algorithms that detect abnormal locations by exploring the inconsistencies between sensors' claimed locations and their neighborhood observations. The in-region verification verifies whether a sensor is inside an application-specific verification region. Compared to on-spot verification, the in-region verification is tolerable to large errors as long as the locations of sensors don't cause the application to malfunction. We study how to derive the verification region for different applications and design a probabilistic algorithm to compute in-region confidence for each sensor. Experiment results show that our on-spot and in-region algorithms can verify sensors' locations with high detection rate and low false positive rate. They are robust in the presence of malicious attacks that are launched during the verification process. Moreover, compared with previous verification schemes, our algorithms are effective and lightweight because they do not rely on the knowledge of deployment of sensors, and they don't require expensive or dedicated hardware, so our algorithms can be used in any low-cost sensor networks.

The promise of RFID technology has been evidently foreseeable due to the low cost and high convenience value of RFID tags. However, the low-cost RFID tags poses new challenges to security and privacy. Some solutions utilize expensive cryptographic primitives such as hash or encryption functions, and some lightweight approaches have been reported to be broken. In this paper, we propose a lightweight solution to Mutual Authentication for RFID systems in which only the authenticated readers and tags can successfully communicate with each other. Then, we adapt our Mutual Authentication scheme to secure the Ownership Transfer of RFID tags. Our mutual authentication and ownership transfer protocols are realized utilizing minimalistic cryptography such as Physically Unclonable Functions (PUF) and Linear Feedback Shift Registers (LFSR). PUFs and LFSRs are very efficient in hardware and particularly suitable for the low-cost RFID tags. Compared to existing solutions built on top of hash functions that require 8000 to 10000 gates, our schemes demand only 784 gates for 64-bit variables and can be easily accommodated by the cheapest RFID tags with only 2000 gates available for security functions.

RFID technology can potentially be used in many applications. A typical RFID system involves a reader and a number of tags, which may range from the battery-powered tags that have Wi-Fi capabilities, to the low-cost tags that are constrained in computation capacities and hardware resources. Keeping RFID systems secure is crucial since RFID systems are vulnerable to a number of malicious attacks. As for low-cost RFID systems, security problems become much more challenging, because traditional security mechanisms are infeasible to be used on low-cost tags due to their resource constraints. Tag search is an important functionality that a RFID system should provide. In this paper, we study how to secure tag search with a focus on low-cost RFID systems. Existing solutions are mostly based on hash functions and consume 8000 to 10000 gates, however, the low-cost tags can afford at most 2000 gates for secure features. In this paper, we propose several lightweight secure search protocols based on linear feedback shift registers (LFSR) and physically unclonable functions (PUF). Our protocols prevent adversaries from learning tag identity and impersonating RFID reader or tags. Experimental results show that our solutions have hundreds of nanoseconds processing time and require no more than 1400 hardware gates on tags.

Network coding is promising to maximize throughput in various networking systems. Compared to normal network coding operated over large finite fields, XOR network coding has gained an increasing number of applications for its simplicity, especially in wireless networks. However, both types of network coding systems are vulnerable to pollution attacks in which the compromised forwarders inject polluted messages into the systems. Existing solutions to pollution attacks can protect only the normal network coding, but none of them is able to secure XOR network coding. In this paper, we propose an efficient scheme for securing XOR network coding against pollution attacks. Our scheme exploits probabilistic key pre-distribution and message authentication codes (MACs). In our scheme, the source appends multiple MACs to each message, where each MAC can authenticate only a part of the message and the parts authenticated by different MACs are overlapped. Thus, multiple forwarders can collaboratively verify different parts of messages using the MACs with their own shared keys. By carefully controlling the overlapping between the parts authenticated by different MACs, our scheme can filter polluted messages in a few hops with a high probability. To the best of our knowledge, this is the first solution to pollution attacks for XOR network coding. Experimental results show that it is 200 to 1000 times faster than existing ones, hence, it is particularly suitable for resource-constrained wireless networks.

Network coding provides the possibility to maximize network throughput and receives various applications in traditional computer networks, wireless sensor networks and peer-to-peer systems. However, the applications built on top of network coding are vulnerable to pollution attacks, in which the compromised forwarders can inject polluted or forged messages into networks. Existing schemes addressing pollution attacks either require an extra secure channel or incur high computation overhead. In this paper, we propose an efficient signature-based scheme to detect and filter pollution attacks for the applications adopting linear network coding techniques. Our scheme exploits a novel homomorphic signature function to enable the source to delegate its signing authority to forwarders, that is, the forwarders can generate the signatures for their output messages without contacting the source. This nice property allows the forwarders to verify the received messages, but prohibit them from creating the valid signatures for polluted or forged ones. Our scheme does not need any extra secure channels, and can provide source authentication and batch verification. Experimental results show that it can improve computation efficiency up to ten times compared to some existing one. In addition, we present an alternate lightweight scheme based on a much simpler linear signature function. This alternate scheme provides a tradeoff between computation efficiency and security.

Location awareness is critical for supporting location-based access control (LBAC). The challenge is how to determine locations accurately and efficiently in indoor environments. Existing solutions based on WLAN signal strength either cannot provide high accuracy, or are too complicated in general i

Because the knowledge of sensors' locations is very important to many location-based applications proposed for wireless sensor networks, many secure localization and location verification schemes have been proposed to provide robust location estimations for sensors. We propose two lightweight location verification algorithms, namely, greedy filtering by matrix (GFM) and trustability indicator (TI). Unlike other schemes, our algorithms do not require any specialized hardware or deployment knowledge. In GFM algorithm, the verification center(VC) calculates several matrices based on sensors' estimated locations and their neighborhood observations, and uses these matrixes to identify and revoke inconsistent locations. In TI algorithm, VC calculates trustability indicators for the sensors and reject those whose indicators are below a threshold. Simulation results demonstrated the effectiveness of both algorithms in detecting abnormal location and their robustness under attacks.