Design of an architecture for multiple security levels in wireless sensor networks

Intelligent Information Processing VI

Guo

2012

Wireless sensor network is one of the fundamental components of the Internet of Things. With the growing use of wireless sensor networks in commercial and military, data security is a critical problem in these applications. Considerable security works have been studied. However, the majority of these works based on the scenarios that the sensitivities of data in the networks are in the same. In this paper, we present a cluster-based multilevel security model that enforces information flow from low security level to high security level. The design of the model is motivated by the observation that sensor nodes in numerous applications have different security clearances. In these scenarios, it is not enough for just protecting the data at a single level. The multilevel security mechanism is needed to prevent the information flow from high level nodes to low level nodes. We give the formal description of the model and present a scheme to achieve it. In our model, sensor nodes are grouped into different clusters. In each cluster, the security clearance of sensor nodes must not be higher than the security clearance of the cluster head. We use cryptography techniques to enforce the information flow policy of this model. The higher level nodes can derive the keys of lower level nodes and use the derived key to get the information from lower-level nodes. abstract environment.

Section: Related Workmentioning

confidence: 99%

A Cluster-Based Multilevel Security Model for Wireless Sensor Networks

Chao

Intelligent Information Processing VI

Guo

2012

“…Therefore, the relay can be partly malicious in the sense that it still functions in compliance with the relaying protocol, whilst it leaks secret information. Another application is the multiple level access control in wireless sensor networks, where sensors have different authorizations and sensitivities depending on their roles and collected data [25][26][27][28]. In such setups, the relaying node might be only allowed to help forwarding messages from one terminal to another, as not all terminals have direct access to other members in the sensor group.…”

Section: Introductionmentioning

confidence: 99%

Securing Relay Networks with Artificial Noise: An Error Performance-Based Approach

Alexandropoulos

et al. 2017

Entropy

Abstract:We apply the concept of artificial and controlled interference in a two-hop relay network with an untrusted relay, aiming at enhancing the wireless communication secrecy between the source and the destination node. In order to shield the square quadrature amplitude-modulated (QAM) signals transmitted from the source node to the relay, the destination node designs and transmits artificial noise (AN) symbols to jam the relay reception. The objective of our considered AN design is to degrade the error probability performance at the untrusted relay, for different types of channel state information (CSI) at the destination. By considering perfect knowledge of the instantaneous CSI of the source-to-relay and relay-to-destination links, we first present an analytical expression for the symbol error rate (SER) performance at the relay. Based on the assumption of an average power constraint at the destination node, we then derive the optimal phase and power distribution of the AN that maximizes the SER at the relay. Furthermore, we obtain the optimal AN design for the case where only statistical CSI is available at the destination node. For both cases, our study reveals that the Gaussian distribution is generally not optimal to generate AN symbols. The presented AN design takes into account practical parameters for the communication links, such as QAM signaling and maximum likelihood decoding.

“…Another essential requirement of an ECC implementation for WSNs is to support curves of different order (i.e. different cryptographic "strength") since the various tasks a sensor node performs during its lifetime have very different security needs [30,22]. For example, a multi-tier security framework for WSNs can permit lower security levels (i.e.…”

Section: Introductionmentioning

confidence: 99%

MoTE-ECC: Energy-Scalable Elliptic Curve Cryptography for Wireless Sensor Networks

Lecture Notes in Computer Science

Wenger

Großschädl

2014

Abstract. Wireless Sensor Networks (WSNs) are susceptible to a wide range of malicious attacks, which has stimulated a body of research on "light-weight" security protocols and cryptographic primitives that are suitable for resource-restricted sensor nodes. In this paper we introduce MoTE-ECC, a highly optimized yet scalable ECC library for Memsic's MICAz motes and other sensor nodes equipped with an 8-bit AVR processor. MoTE-ECC supports scalar multiplication on Montgomery and twisted Edwards curves over Optimal Prime Fields (OPFs) of variable size, e.g. 160, 192, 224, and 256 bits, which allows for various trade-offs between security and execution time (resp. energy consumption). OPFs are a special family of "low-weight" prime fields that, in contrast to the NIST-specified fields, facilitate a parameterized implementation of the modular arithmetic so that one and the same software function can be used for operands of different length. To demonstrate the performance of MoTE-ECC, we take (ephemeral) ECDH key exchange between two nodes as example, which requires each node to execute two scalar multiplications. The first scalar multiplication is performed on a fixed base point (to generate a key pair), whereas the second scalar multiplication gets an arbitrary point as input. Our implementation uses a fixed-base comb method on a twisted Edwards curve for the former and a simple ladder approach on a birationally-equivalent Montgomery curve for the latter. Both scalar multiplications require about 9 · 10 6 clock cycles in total and occupy only 380 bytes in RAM when the underlying OPF has a length of 160 bits. We also describe our efforts to harden MoTE-ECC against side-channel attacks (e.g. simple power analysis) and introduce a highly regular implementation of the comb method.