# A PUF- and Biometric-Based Lightweight Hardware Solution to Increase Security at Sensor Nodes
## 1 Introduction
Using the new response and the stored Helper Data, a noisy version of the encoded secret key is obtained. Then, the decoder of the ECC is able to recover the secret key. This paper proposes a low-cost solution to carry out these stages at sensor nodes. Many applications of sensor networks not only require trusted sensors but also that genuine individuals prove with their physical presence that they are associated with the sensed data. For example, a soldier wearing the sensor node can prove that the geolocation data transmitted are associated with him/her and not with an enemy that could have stolen it [12]. These sensor nodes can be considered as nodes with dual-factor authentication. Individual recognition at sensor nodes allows high-level security from the origin of the data, which is interesting to create a digital chain of custody [13]. Neither a genuine sensor employed by a non-legitimate individual nor a fake sensor employed by a legitimate individual are accepted. If the dual-authentication is carried out at sensor node, the security perimeter is constrained to the node. Hence, the number of possible attacks is necessarily smaller. As PUFs authenticate the physical identity of the sensor, biometrics authenticates the physical presence of the user through physical, physiological or behavioral traits [14]. Examples of biometric traits are fingerprints or faces (as physical traits); heart or brain signals (as physiological traits); and gait or handwritten signatures (as behavioral traits). Among the biometric features available, fingerprints are employed in this work because they are general (every individual possesses fingers except in rare cases), very distinctive (even for twins), and permanent along the life of an individual [15]. Besides,small and low-cost fingerprint sensors can capture high-quality fingerprint images. Taking into account that sensor nodes have limited computational, memory, and power resources, this paper proposes a lightweight fingerprint recognition solution which considers a novel texture-based fingerprint feature named QFingerMap16, herein referred to as QFM. It is obtained after applying very simple arithmetic and logic operations at all the processing steps so that it can be implemented in low-cost sensor nodes. The proposal is able to provide good accuracy in recognition by using several fingers and samples per finger. In addition, the solution allows user interaction and collaboration by informing about the quality and evolution of the feature extraction process. If the network can verify both the identity of the sensor that transmits the data and the individual that uses it, repudiation attacks are more difficult because a node and a user cannot deny having sent a message already sent. Other entities can prove that sensor with the presence of that user have sent the measurement. Impersonation attacks (in which a fake sensor node assumes the identity of one of the legitimate sensors in the network or an impostor user assumes the identity of one of the legitimate users) are more difficult. Like the secret key used in the cryptographic algorithms, the biometric templates of the individuals are sensitive information that should not be stored in the sensor [16; 17; 18]. Biometric salting techniques add random information (salt) to increase the entropy of the biometric template and generate a transformed template. A well-known technique is BioHashing, which generates a transformed template from the biometric template and random data contained in a token, smart card, etc. [18]. Instead of using random data stored in the sensor node and complex transformations of the biometric template, the solution proposed in this paper is to use again the PUF responses of the sensor, which is more secure because they are not stored anywhere. Besides, the QFM-based features are employed without any transformation, which allows a lightweight hardware solution.