The United States Air Force Research Laboratory (AFRL) has been exploring the implementation of neurophysiological and psychological constructs to develop a hyper-parallel computing platform. This approach is termed neuromorphic computing. As part of that effort, the primary visual cortex (V1) has been modeled in high performance computing facility. The current columnar V1 model is being expanded to include binocular disparity and motion perception. Additionally, V2 thick and pale stripes are being added to produce a V1/V2 stereomotion and form perception system. Both the V1 and V2 models are based upon structures approximating neocortical minicolumns and functional columns. The neuromorphic strategies employed include columnar organization, integrate-and-fire neurons, temporal coding, point attraction recurrent networks, Reichardt detectors and “confabulation” networks. The interest is driven by the value of applications which can make use of highly parallel architectures we expect to see surpassing one thousand cores per die in the next few years. A central question we seek to answer is what the architecture of hyper-parallel machines should be. We also seek to understand computational methods akin to how a brain deals with sensation, perception, memory, attention decision-making.

Joint analysis of security and routing protocols in wireless networks reveals vulnerabilities of secure network traffic that remain undetected when security and routing protocols are analyzed independently. We formulate a class of continuous metrics to evaluate the vulnerability of network traffic as a function of security and routing protocols used in wireless networks. We develop two complementary vulnerability definitions using set theoretic and circuit theoretic interpretations of the security of network traffic, allowing a network analyst or an adversary to determine weaknesses in the secure network. We formalize node capture attacks using the vulnerability metric as a nonlinear integer programming minimization problem and propose the GNAVE algorithm, a Greedy Node capture Approximation using Vulnerability Evaluation. We discuss the availability of security parameters to the adversary and show that unknown parameters can be estimated using probabilistic analysis. We demonstrate vulnerability evaluation using the proposed metrics and node capture attacks using the GNAVE algorithm through detailed examples and simulation.

We investigate the impact of node capture attacks on the confidentiality and integrity of network traffic. We map the compromise of network traffic to the flow of current through an electric circuit and propose a metric for quantifying the vulnerability of the traffic using the circuit mapping. We compute the vulnerability metric as a function of the routing and the cryptographic protocols used to secure the network traffic. We formulate the minimum cost node capture attack problem as a nonlinear integer programming problem. Due to the NP-hardness of the minimization problem, we provide a greedy heuristic that approximates the minimum cost attack. We provide examples of node capture attacks using our vulnerability metric and show that the adversary can expend significantly less resources to compromise target traffic by exploiting information leakage from the routing and cryptographic protocols.