Random numbers are widely used in cryptographic systems. Often they are essential in the very first steps of protocols (e.g., generation of session keys and challenges), or repeatedly used throughout the entire communication (e.g., random nonces). Therefore, failure of random number generators (RNGs) may obliterate security measures. Scale and severity of these failures can be depicted by a study conducted by Bernstein et al. The study shows how improper operation of RNGs, that have passed FIPS 140-2 Level 2 certification, lead to factorization of 184 distinct 1024-bit RSA keys used in Taiwan's national "Citizen Digital Certificate".
Citizen Digital Certificate is your internet ID for bilateral identification while you are exchanging information on the internet.
In other words, a team of several researchers could easily track, surveil, or impersonate 184 Taiwanese citizens, simply because random numbers were not generated properly.
This is only one of many examples in the public-key setting. As alarming as it is, it may not seem to be of great importance to ones who believe their governments will hire capable security experts, or simply ones who trust their browsers while on the internet. Moreover, generation of these random numbers is often out hands of users, in well guarded server rooms, with considerable computational power to check "the quality" (statistical properties) of obtained numbers. And should all fail, there is an organization behind these numbers to be held liable.
On the other hand emerging Internet of Things (IOT) technologies are bringing numerous devices (e.g., wireless sensors, drones) in our environment. While hand-held "smart" devices have became the most ordinary, maybe even mundane, the "smart" hype expands to cars and houses. In this shroud of "smart" that surrounds us, to start to perceive secure communication more personally. Once you can use your smartphone to control the heating furnace in your home, consequences of unauthorized access may cause more fire than some leaked photos.
Additionally, unlike in the traditional IT setting where ample computational power is available, IOT devices are often constrained in terms of resources, have to be very cheap and operate reliably. Lastly, attackers can easily obtain physical access to deployed IOT devices. Hence, attackers may perform numerous side-channel attacks, and may manipulate devices' environment (e.g., temperature) or tamper with the devices' operation (e.g., shoot lasers).
Various mitigation techniques are studied to protect against these attacks. For example, sturdy casings, protective meshes, and seals for tamper detection, provide mechanical layer of security. They are expensive, and easily avoided by skilled adversaries. On the other end of the spectrum various circuit techniques, often called "secure logic styles", such as Sense-Amplifier Based Logic (SABL), are used. They are very difficult to implement in practice, due to variations in silicon manufacturing processes. Based on secret sharing, Threshold Implementations (TI) provide provable security against widely spread Differential Power Analysis (DPA). The only downfall of TI, as well as all masking schemes, is that masks must be random and uniformly distributed.
Therefore, security of a device depends on cryptographic primitives (algorithms and protocols), which rely on random numbers, as well as the secure implementation. Since many countermeasures rely on use of random masks, secure implementation also often depends on the RNG. These dependencies are depicted in Figure 1.
|Figure 1: Security dependencies.|
Practical problems stem from the circular dependency between RNG and secure implementations. Namely, should the attacker focus RNG as a target of attacks, causing it to malfunction or being able to predict its output, all physical security would be circumvent.
Consequently, outputs of RNGs must be reliably unpredictable. To ensure this, RNG ouputs must be evaluated using prescribed statistical tests. Furthermore, in order to fit the IOT framework, these tests have to be performed in a lightweight, low-latency, and highly reliable manner. Therefore, storing giga bytes of data and computing over them is not a valid strategy.
Figure 2 depicts two solutions proposed by Rožić et al based on NIST SP 800-90B model of the entropy source. Both are variations of the same idea to use canary numbers. This concept is already used in software security, and much longer in the mining industry for early warning threat detection. Namely, caged canaries were used to warn miners of poison gasses in coal mines, since they were much more susceptible to it. Hence small doses of gas, fatal to the canaries, could be detected, and miners would have been warned. Similarly, Rožić et al. propose an additional output of the RNG that has significantly worse characteristics. Whether canary numbers stem from a different noise source, or are obtained by weaker processing of the same noise as the random numbers, quality of canary numbers will decline abruptly once the device is tampered with. In the experiments authors have tampered with the chip temperature, cooling it down quickly to change the physical processes that produce entropy.
|Figure 2: RNG architectures with canary numbers.|
Figure 3 depicts experimental results obtained on an elementary ring-oscillator based RNG. It clearly shows that the quality of canary numbers decays much more significantly, hence an attack can be detected while a RNG is still producing reliably unpredictable random numbers.
|Figure 3: Test results of the elementary ring-oscillator based RNG (from the original paper).|
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