Executive Summary Confidentiality, integrity, and authentication services are critical to preventing an adversary from compromising the security of a distributed sensor network. Key management is likewise critical to establishing the keys necessary to provide this protection. However, providing key management is difficult due to the ad hoc nature, intermittent connectivity, and resource limitations of the sensor network environment. As part of the SensIT program, NAI Labs is addressing this problem by identifying and developing cryptographic protocols and mechanisms that efficiently provide key management security support services. This document describes our sensor network constraints and key management approaches research for FY 2000. As a first step, NAI Labs has researched battlefield sensor and sensor network technology and the unique communications environment in which it will be deployed. We have identified the requirements specific to our problem of providing key management for confidentiality and group-level authentication. We have also identified constraints, particularly energy consumption, that render this problem difficult. NAI Labs has developed novel key management protocols specifically designed for the distributed sensor network environment, including Identity-Based Symmetric Keying and Rich Uncle. We have analyzed both existing and NAI Labs-developed keying protocols for their suitability at satisfying identified requirements while overcoming battlefield energy constraints. Our research has focused heavily on key management energy consumption, evaluating protocols based on total system, average sensor node, and individual sensor node energy consumption. We examined a number of secret-key-based protocols, determining some to be suitable for sensor networks but all of the protocols have flexibility limitations. Secret-key-based protocols are generally energy-efficient, using encryption and hashing algorithms that consume relatively little energy. Security of secret-key-based protocols is generally determined by the granularity of established keys, which vary widely for the protocols described herein. During our examination of these protocols we noted that some of these protocols are not sufficiently flexible for use in battlefield sensor network, since they cannot efficiently handle unanticipated additions of sensor nodes to the network. Our Identity-Based Symmetric Keying protocol and the less efficient Symmetric Key Certificate Based Protocol are well suited for certain sensor networks, establishing granular keys while consuming relatively little energy. However, all of the secure secret-key-based protocols use special nodes that operate as Key Distribution Centers (or Translators). The sensor nodes communicate with these centers exchanging information as part of the key establishment process. Since these special nodes are expected to make up less than 1% of the sensor …

/pdf/constraints-and-approaches-for-distributed-sensor-network-10gcbdlzo3.pdf

Constraints and approaches for distributed sensor network security

Tamper-resistant cryptographic processors are becoming the standard way to enforce data-usage policies. Their origins lie with military cipher machines and PIN processing in banking payment networks, expanding in the 1990s into embedded applications: token vending machines for prepayment electricity and mobile phone credit. Major applications such as GSM mobile phone identification and pay TV set-top boxes have pushed low-cost cryptoprocessors toward ubiquity. In the last five years, dedicated crypto chips have been embedded in devices such as game console accessories and printer ink cartridges, to control product and accessory after markets. The "Trusted Computing" initiative will soon embed cryptoprocessors in PCs so they can identify each other remotely. This paper surveys the range of applications of tamper-resistant hardware and the array of attack and defense mechanisms which have evolved in the tamper-resistance arms race.

/pdf/cryptographic-processors-a-survey-7b1dssqik5.pdf

Cryptographic Processors-A Survey

Random numbers are needed in many areas: cryptography, Monte Carlo computation and simulation, industrial testing and labeling, hazard games, gambling, etc. Our assumption has been that random numbers cannot be computed; because digital computers operate deterministically, they cannot produce random numbers. Instead, random numbers are best obtained using physical (true) random number generators (TRNG), which operate by measuring a well-controlled and specially prepared physical process. Randomness of a TRNG can be precisely, scientifically characterized and measured. Especially valuable are the information-theoretic provable random number generators (RNGs), which, at the state of the art, seem to be possible only by exploiting randomness inherent to certain quantum systems. On the other hand, current industry standards dictate the use of RNGs based on free-running oscillators (FRO) whose randomness is derived from electronic noise present in logic circuits and which cannot be strictly proven as uniformly random, but offer easier technological realization. The FRO approach is currently used in 3rd- and 4th-generation FPGA and ASIC hardware, unsuitable for realization of quantum RNGs. In this chapter we compare weak and strong aspects of the two approaches. Finally, we discuss several examples where use of a true RNG is critical and show how it can significantly improve security of cryptographic systems, and discuss industrial and research challenges that prevent widespread use of TRNGs.

True Random Number Generators

Since their inception, field-programmable gate arrays (FPGAs) have grown in capacity and complexity so that now FPGAs include millions of gates of logic, megabytes of memory, high-speed transceivers, analog interfaces, and whole multicore processors. Applications running in the FPGA include communications infrastructure, digital cinema, sensitive database access, critical industrial control, and high-performance signal processing. As the value of the applications and the data they handle have grown, so has the need to protect those applications and data. Motivated by specific threats, this paper describes FPGA security primitives from multiple FPGA vendors and gives examples of those primitives in use in applications.

FPGA Security: Motivations, Features, and Applications

Voltage noise not only detracts from reliability and performance, but has been used to attack system security. Most systems are completely unaware of fluctuations occurring on nanosecond time scales. This paper quantifies the threat to FPGA-based systems and presents a solution approach. Novel measurements of transients on 28nm FPGAs show that extreme activity in the fabric can cause enormous undershoot and overshoot, more than 10× larger than what is allowed by the specification. An existing voltage sensor is evaluated and shown to be insufficient. Lastly, a sensor design using reconfigurable logic is presented; its time-to-digital converter enables sample rates 500× faster than the 28nm Xilinx ADC. This enables quick characterization of transients that would normally go undetected, thereby providing potentially useful data for system optimization and helping to defend against supply voltage attacks.

Sensing nanosecond-scale voltage attacks and natural transients in FPGAs

/pdf/security-requirements-for-cryptographic-modules-nist-ahdtlreesn.pdf

Security Requirements for Cryptographic Modules | NIST

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Security Requirements for Cryptographic Modules | NIST