Social preferences in a common-pool resource dilemma

Thesis (M.S.) University of Alaska Fairbanks, 2014.The simplifying assumption of rational self-interest is a common one in the social sciences, however it may not always be entirely realistic. People can also adopt a variety of alternative social preferences that place weight on both private and group outcomes. Using experimental methods from economics and psychology, this paper empirically estimates these different "social value orientations" (SVOs), ranging continuously from relatively proself social preferences (competition and individualism) to relatively prosocial (altruism and cooperation). This measure is then applied to a common-pool resource (CPR) experiment to test if social preferences can be used to predict strategic harvesting decisions or participation in a peer-enforced regulatory institution. I find that perfect self-interest is one of many consistent forms of social preference, and that prosocial (proself) preferences successfully predict lower (higher) rates of resource extraction. Social preferences can also be used to predict regulatory participation, but the long-run relationship is less clear

Driving Towards Success in the Air Force Cyber Mission: Leveraging Our Heritage to Shape Our Future

Ongoing debates address what constitutes cyber warfare and whether or not we really are at war in cyberspace. This article does not enter into those issues; rather, it suggests how the Air Force and Air University should move forward to lead and support our nation’s cyber security needs. Thus, it focuses on analogous lessons learned from history, our position today and what it needs to be, and plans for getting there with respect to our cyberspace capabilities

Malware Target Recognition

A method, apparatus and program product are provided to recognize malware in a computing environment having at least one computer. A sample is received. An automatic determination is made by the at least one computer to determine if the sample is malware using static analysis methods. If the static analysis methods determine the sample is malware, dynamic analysis methods are used by the at least one computer to automatically determine if the sample is malware. If the dynamic analysis methods determine the sample is malware, the sample is presented to a malware analyst to adjudicate the automatic determinations of the static and dynamic analysis. If the adjudication determines the sample is malware, a response action is initiated to recover from or mitigate a threat of the sample

A Secure Group Communication Architecture for Autonomous Unmanned Aerial Vehicle

This paper investigates the application of a secure group communication architecture to a swarm of autonomous unmanned aerial vehicles (UAVs). A multicast secure group communication architecture for the low earth orbit (LEO) satellite environment is evaluated to determine if it can be effectively adapted to a swarm of UAVs and provide secure, scalable, and efficient communications. The performance of the proposed security architecture is evaluated with two other commonly used architectures using a discrete event computer simulation developed using MATLAB. Performance is evaluated in terms of the scalability and efficiency of the group key distribution and management scheme when the swarm size, swarm mobility, multicast group join and departure rates are varied. The metrics include the total keys distributed over the simulation period, the average number of times an individual UAV must rekey, the average bandwidth used to rekey the swarm, and the average percentage of battery consumed by a UAV to rekey over the simulation period. The proposed security architecture can successfully be applied to a swarm of autonomous UAVs using current technology. The proposed architecture is more efficient and scalable than the other tested and commonly used architectures. Over all the tested configurations, the proposed architecture distributes 55.2–94.8% fewer keys, rekeys 59.0–94.9% less often per UAV, uses 55.2–87.9% less bandwidth to rekey, and reduces the battery consumption by 16.9–85.4%

A Secure Group Communication Architecture for Autonomous Unmanned Aerial Vehicles

This paper investigates the application of a secure group communication architecture to a swarm of autonomous unmanned aerial vehicles (UAVs). A multicast secure group communication architecture for the low earth orbit (LEO) satellite environment is evaluated to determine if it can be effectively adapted to a swarm of UAVs and provide secure, scalable, and efficient communications. The performance of the proposed security architecture is evaluated with two other commonly used architectures using a discrete event computer simulation developed using MATLAB. Performance is evaluated in terms of the scalability and efficiency of the group key distribution and management scheme when the swarm size, swarm mobility, multicast group join and departure rates are varied. The metrics include the total keys distributed over the simulation period, the average number of times an individual UAV must rekey, the average bandwidth used to rekey the swarm, and the average percentage of battery consumed by a UAV to rekey over the simulation period. The proposed security architecture can successfully be applied to a swarm of autonomous UAVs using current technology. The proposed architecture is more efficient and scalable than the other tested and commonly used architectures. Over all the tested configurations, the proposed architecture distributes 55.2–94.8% fewer keys, rekeys 59.0–94.9% less often per UAV, uses 55.2–87.9% less bandwidth to rekey, and reduces the battery consumption by 16.9–85.4%

Gemini-South + FLAMINGOS Demonstration Science: Near-Infrared Spectroscopy of the z=5.77 Quasar SDSS J083643.85+005453.3

We report an infrared 1-1.8 micron (J+H-bands), low-resolution (R=450) spectrogram of the highest-redshift radio-loud quasar currently known, SDSS J083643.85+005453.3, obtained during the spectroscopic commissioning run of the FLAMINGOS multi-object, near-infrared spectrograph at the 8m Gemini-South Observatory. These data show broad emission from both CIV 1549 and CIII] 1909, with strengths comparable to lower-redshift quasar composite spectra. The implication is that there is substantial enrichment of the quasar environment, even at times less than a billion years after the Big Bang. The redshift derived from these features is z = 5.774 +/- 0.003, more accurate and slightly lower than the z = 5.82 reported in the discovery paper based on the partially-absorbed Lyman-alpha emission line. The infrared continuum is significantly redder than lower-redshift quasar composites. Fitting the spectrum from 1.0 to 1.7 microns with a power law f(nu) ~ nu^(-alpha), the derived power law index is alpha = 1.55 compared to the average continuum spectral index = 0.44 derived from the first SDSS composite quasar. Assuming an SMC-like extinction curve, we infer a color excess of E(B-V) = 0.09 +/- 0.01 at the quasar redshift. Only approximately 6% of quasars in the optically-selected Sloan Digital Sky Survey show comparable levels of dust reddening.Comment: 10 pages, 1 figure; to appear in the Astrophysical Journal Letter

Malware Type Recognition and Cyber Situational Awareness

Current technologies for computer network and host defense do not provide suitable information to support strategic and tactical decision making processes. Although pattern-based malware detection is an active research area, the additional context of the type of malware can improve cyber situational awareness. This additional context is an indicator of threat capability thus allowing organizations to assess information losses and focus response actions appropriately. Malware Type Recognition (MaTR) is a research initiative extending detection technologies to provide the additional context of malware types using only static heuristics. Test results with MaTR demonstrate over a 99% accurate detection rate and 59% test accuracy in malware typing

Malware Target Recognition via Static Heuristics

Organizations increasingly rely on the confidentiality, integrity and availability of their information and communications technologies to conduct effective business operations while maintaining their competitive edge. Exploitation of these networks via the introduction of undetected malware ultimately degrades their competitive edge, while taking advantage of limited network visibility and the high cost of analyzing massive numbers of programs. This article introduces the novel Malware Target Recognition (MaTR) system which combines the decision tree machine learning algorithm with static heuristic features for malware detection. By focusing on contextually important static heuristic features, this research demonstrates superior detection results. Experimental results on large sample datasets demonstrate near ideal malware detection performance (99.9+% accuracy) with low false positive (8.73e-4) and false negative rates (8.03e-4) at the same point on the performance curve. Test results against a set of publicly unknown malware, including potential advanced competitor tools, show MaTR’s superior detection rate (99%) versus the union of detections from three commercial antivirus products (60%). The resulting model is a fine granularity sensor with potential to dramatically augment cyberspace situation awareness