Attacking PUF-Based Pattern Matching Key Generators via Helper Data Manipulation

Abstract. Physically Unclonable Functions (PUFs) provide a unique signature for integrated circuits (ICs), similar to a fingerprint for humans. They are primarily used to generate secret keys, hereby exploiting the unique manufacturing variations of an IC. Unfortunately, PUF output bits are not perfectly reproducible and non-uniformly distributed. To obtain a high-quality key, one needs to implement additional post-processing logic on the same IC. Fuzzy extractors are the well-established standard solution. Pattern Matching Key Generators (PMKGs) have been proposed as an alternative. In this work, we demonstrate the latter construction to be vulnerable against manipulation of its public helper data. Full key recovery is possible, although depending on system design choices. We demonstrate our attacks using a 4-XOR arbiter PUF, manufactured in 65nm CMOS technology. We also propose a simple but effective countermeasure

On the prevalence of hierarchies in social networks

In this paper, we introduce two novel evolutionary processes for hierarchical networks referred to as dominance- and prestige-based evolution models, i.e., DBEM and PBEM, respectively. Our models are deterministic in nature which allows for closed-form derivation of equilibrium points for such type of networks, for the special case of complete networks. After deriving these equilibrium points, we are somewhat surprised in recovering the exponential and power-law strength distribution as the shared property of the resulting hierarchal networks. Additionally, we compute the network properties, Geodesic distance distribution and centrality closeness, for each model in closed form. Interestingly, these results demonstrate very different roles of hubs for each model, shedding the light on the evolutionary advantages of hierarchies in social networks: in short, hierarchies can lead to efficient sharing of resources and robustness to random failures. For the general case of any hierarchical network, we compare the estimations of tie intensities and node strengths using the proposed models to open-source real-world data. The prediction results are statistically compared using the Kolmogorov–Smirnov test with the original data

Stability of cooperation in societies of emotional and moody agents

It is well documented that cooperation may not be achieved in societies where self-interested agents are engaging in Prisoner’s Dil

Lenient multi-agent deep reinforcement learning

Much of the success of single agent deep reinforcement learning (DRL) in recent years can be attributed to the use of experience replay memories (ERM), which allow Deep Q-Networks (DQNs) to be trained efficiently through sampling stored state transitions. However, care is required when using ERMs for multi-agent deep reinforcement learning (MA-DRL), as stored transitions can become outdated because agents update their policies in parallel [11]. In this work we apply leniency [23] to MA-DRL. Lenient agents map state-action pairs to decaying temperature values that control the amount of leniency applied towards negative policy updates that are sampled from the ERM. This introduces optimism in the value-function update, and has been shown to facilitate cooperation in tabular fully-cooperative multi-agent reinforcement learning problems. We evaluate our Lenient-DQN (LDQN) empirically against the related Hysteretic-DQN (HDQN) algorithm [22] as well as a modified version we call scheduled-HDQN, that uses average reward learning near terminal states. Evaluations take place in extended variations of the Coordinated Multi-Agent Object Transportation Problem (CMOTP) [8] which include fully-cooperative sub-tasks and stochastic rewards. We find that LDQN agents are more likely to converge to the optimal policy in a stochastic reward CMOTP compared to standard and scheduled-HDQN agents

Bounds and dynamics for empirical game theoretic analysis

This paper provides several theoretical results for empirical game theory. Specifically, we introduce bounds for empirical game theoretical analysis of complex multi-agent interactions. In doing so we provide insights in the empirical meta game showing that a Nash equilibrium of the estimated meta-game is an approximate Nash equilibrium of the true underlying meta-game. We investigate and show how many data samples are required to obtain a close enough approximation of the underlying game. Additionally, we extend the evolutionary dynamics analysis of meta-games using heuristic payoff tables (HPTs) to asymmetric games. The state-of-the-art has only considered evolutionary dynamics of symmetric HPTs in which agents have access to the same strategy sets and the payoff structure is symmetric, implying that agents are interchangeable. Finally, we carry out an empirical illustration of the generalised method in several domains, illustrating the theory and evolutionary dynamics of several versions of the AlphaGo algorithm (symmetric), the dynamics of the Colonel Blotto game played by human players on Facebook (symmetric), the dynamics of several teams of players in the capture the flag game (symmetric), and an example of a meta-game in Leduc Poker (asymmetric), generated by the policy-space response oracle multi-agent learning algorithm