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

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

Low-complexity, low-area computer architectures for cryptographic application in resource constrained environments

RCE (Resource Constrained Environment) is known for its stringent hardware design requirements. With the rise of Internet of Things (IoT), low-complexity and low-area designs are becoming prominent in the face of complex security threats. Two low-complexity, low-area cryptographic processors based on the ultimate reduced instruction set computer (URISC) are created to provide security features for wireless visual sensor networks (WVSN) by using field-programmable gate array (FPGA) based visual processors typically used in RCEs. The first processor is the Two Instruction Set Computer (TISC) running the Skipjack cipher. To improve security, a Compact Instruction Set Architecture (CISA) processor running the full AES with modified S-Box was created. The modified S-Box achieved a gate count reduction of 23% with no functional compromise compared to Boyar’s. Using the Spartan-3L XC3S1500L-4-FG320 FPGA, the implementation of the TISC occupies 71 slices and 1 block RAM. The TISC achieved a throughput of 46.38 kbps at a stable 24MHz clock. The CISA which occupies 157 slices and 1 block RAM, achieved a throughput of 119.3 kbps at a stable 24MHz clock. The CISA processor is demonstrated in two main applications, the first in a multilevel, multi cipher architecture (MMA) with two modes of operation, (1) by selecting cipher programs (primitives) and sharing crypto-blocks, (2) by using simple authentication, key renewal schemes, and showing perceptual improvements over direct AES on images. The second application demonstrates the use of the CISA processor as part of a selective encryption architecture (SEA) in combination with the millions instructions per second set partitioning in hierarchical trees (MIPS SPIHT) visual processor. The SEA is implemented on a Celoxica RC203 Vertex XC2V3000 FPGA occupying 6251 slices and a visual sensor is used to capture real world images. Four images frames were captured from a camera sensor, compressed, selectively encrypted, and sent over to a PC environment for decryption. The final design emulates a working visual sensor, from on node processing and encryption to back-end data processing on a server computer

Low-complexity, low-area computer architectures for cryptographic application in resource constrained environments

RCE (Resource Constrained Environment) is known for its stringent hardware design requirements. With the rise of Internet of Things (IoT), low-complexity and low-area designs are becoming prominent in the face of complex security threats. Two low-complexity, low-area cryptographic processors based on the ultimate reduced instruction set computer (URISC) are created to provide security features for wireless visual sensor networks (WVSN) by using field-programmable gate array (FPGA) based visual processors typically used in RCEs. The first processor is the Two Instruction Set Computer (TISC) running the Skipjack cipher. To improve security, a Compact Instruction Set Architecture (CISA) processor running the full AES with modified S-Box was created. The modified S-Box achieved a gate count reduction of 23% with no functional compromise compared to Boyar’s. Using the Spartan-3L XC3S1500L-4-FG320 FPGA, the implementation of the TISC occupies 71 slices and 1 block RAM. The TISC achieved a throughput of 46.38 kbps at a stable 24MHz clock. The CISA which occupies 157 slices and 1 block RAM, achieved a throughput of 119.3 kbps at a stable 24MHz clock. The CISA processor is demonstrated in two main applications, the first in a multilevel, multi cipher architecture (MMA) with two modes of operation, (1) by selecting cipher programs (primitives) and sharing crypto-blocks, (2) by using simple authentication, key renewal schemes, and showing perceptual improvements over direct AES on images. The second application demonstrates the use of the CISA processor as part of a selective encryption architecture (SEA) in combination with the millions instructions per second set partitioning in hierarchical trees (MIPS SPIHT) visual processor. The SEA is implemented on a Celoxica RC203 Vertex XC2V3000 FPGA occupying 6251 slices and a visual sensor is used to capture real world images. Four images frames were captured from a camera sensor, compressed, selectively encrypted, and sent over to a PC environment for decryption. The final design emulates a working visual sensor, from on node processing and encryption to back-end data processing on a server computer