To validate the effectiveness of a Physical Unclonable Function (PUF), it needs to be characterized over a large population of chips. Though simulation methods can provide approximate results, an on-chip experiment produces more accurate result. In this paper, we characterize a PUF based on ring oscillator (RO) using a significantly large population of 125 FPGAs. We analyze the experimental data using a ring oscillator loop delay model, and quantify the quality factors of a PUF such as uniqueness and reliability. The RO-PUF shows an average inter-die Hamming distance of 47.31%, and an average intra-die Hamming distance of 0.86% at normal operating condition. Additionally, we intend to make this large RO frequency dataset available publicly for the research community.

This study examined contextual control of long-term habituation and whether such effects are dependent on the habituating response system. Habituation of the acoustic startle response transferred from the home cage to the testing context, whereas habituation of lick suppression was context specific (Experiments 1 and 2). Contextual control of habituation was demonstrated between 2 experimental contexts for lick suppression to a tone (Experiment 3) and bar-press suppression to a light (Experiment 4). Experiment 5 extinguished habituation of lick suppression and the orienting response to a tone with 27 exposures to the habituation context. Context specificity of both responses also was found. Previous failures to demonstrate contextual control of habituation may be due to the choice of response system and to less sensitive procedures to detect response recovery. The habituation mechanism for startle is independent from the process or processes that underlie habituation in other response systems, but the nature of these mechanisms is not yet known.

The present study was conducted to examine several different methods and cutpoints for determining smoking status in pregnant and recently postpartum women. Self-reported smoking status, urine cotinine levels determined by gas chromatography (GC) and by enzyme immunoassay testing (EMIT), and breath carbon monoxide (CO) levels were assessed at 28 weeks antepartum and 12 and 24 weeks postpartum in 131 women enrolled in studies on smoking cessation and relapse prevention. Classifications based on urine-cotinine GC testing served as the standard in most analyses. Overall agreement between self-reported smoking status and classification based on urine-cotinine GC testing was excellent (> or =95%) at several cutpoints (50, 25, and 12.5 ng/ml) but highest at 25 ng/ml. Classifications based on EMIT urine cotinine levels were in nearly perfect (> or =98%) agreement with those made by GC when the cutpoint for the former was set at approximately 80 ng/ml (79-87 ng/ml). Classifications based on breath CO were in relatively poor agreement (< or =87%) with GC classifications at all cutpoints examined but best at 4 ppm. Overall, these results provide detailed information on several commonly used methods for classifying smoking in pregnant and recently postpartum women that should be practically useful to researchers and clinicians involved in efforts to eliminate smoking in this population.

The Software Defined Networking (SDN) approach has numerous advantages, including the ability to program the network through simple abstractions, provide a centralized view of network state, and respond to changing network conditions. One of the main challenges in designing SDN enabled switches is efficient packet classification in the data plane. As the complexity of SDN applications increases, the data plane becomes more susceptible to Denial of Service (DoS) attacks, which can result in increased delays and packet loss. Accordingly, there is a strong need for network architectures that operate efficiently in the presence of malicious traffic. In particular, there is a need to protect authorized flows from DoS attacks.In this work we utilize a probabilistic data structure to pre-classify traffic with the aim of decoupling likely legitimate traffic from malicious traffic by leveraging the locality of packet flows. We validate our approach by examining a fundamental SDN application: software defined network firewall. For this application, our architecture dramatically reduces the impact of unknown/malicious flows on established/legitimate flows. We explore the effect of stochastic pre-classification in prioritizing data plane classification. We show how pre-classification can be used to increase the effective Quality of Service (QoS) for established flows and reduce the impact of adversarial traffic.

Packet classification methods rely upon packet content/header matching against rules. Thus, throughput of matching operations is critical in many networking applications. Further, with the advent of Software Defined Networking (SDN), efficient implementation of software approaches to matching are critical for the overall system performance. This article presents 1 GenMatcher, a generic, software-only, arbitrary matching framework for fast, efficient searches. The key idea of our approach is to represent arbitrary rules with efficient prefix-based tries. To support arbitrary wildcards, we rearrange bits within the rules such that wildcards accumulate to one side of the bitstring. Since many non-contiguous wildcards often remain, we use multiple prefix-based tries. The main challenge in this context is to generate efficient trie groupings and expansions to support all arbitrary rules. Finding an optimal mix of grouping and expansion is an NP-complete problem. Our contribution includes a novel, clustering-based grouping algorithm to group rules based upon their bit-level similarities. Our algorithm generates near-optimal trie groupings with low configuration times and provides significantly higher match throughput compared to prior techniques. Experiments with synthetic traffic show that our method can achieve a 58.9X speedup compared to the baseline on a single core processor under a given memory constraint.