Smart Computer Security Audit: Reinforcement Learning with a Deep Neural Network Approximator

A significant challenge in modern computer security is the growing skill gap as intruder capabilities increase, making it necessary to begin automating elements of penetration testing so analysts can contend with the growing number of cyber threats. In this paper, we attempt to assist human analysts by automating a single host penetration attack. To do so, a smart agent performs different attack sequences to find vulnerabilities in a target system. As it does so, it accumulates knowledge, learns new attack sequences and improves its own internal penetration testing logic. As a result, this agent (AgentPen for simplicity) is able to successfully penetrate hosts it has never interacted with before. A computer security administrator using this tool would receive a comprehensive, automated sequence of actions leading to a security breach, highlighting potential vulnerabilities, and reducing the amount of menial tasks a typical penetration tester would need to execute. To achieve autonomy, we apply an unsupervised machine learning algorithm, Q-learning, with an approximator that incorporates a deep neural network architecture. The security audit itself is modelled as a Markov Decision Process in order to test a number of decisionmaking strategies and compare their convergence to optimality. A series of experimental results is presented to show how this approach can be effectively used to automate penetration testing using a scalable, i.e. not exhaustive, and adaptive approach

The probability density function of a hardware performance parameter

Probability density function of hardware performance parameter and incentive contractin

Evolution of pairing from weak to strong coupling on a honeycomb lattice

We study the evolution of the pairing from weak to strong coupling on a honeycomb lattice by Quantum Monte Carlo. We show numerical evidence of the BCS-BEC crossover as the coupling strength increases on a honeycomb lattice with small fermi surface by measuring a wide range of observables: double occupancy, spin susceptibility, local pair correlation, and kinetic energy. Although at low energy, the model sustains Dirac fermions, we do not find significant qualitative difference in the BCS-BEC crossover as compared to those with an extended Fermi surface, except at weak coupling, BCS regime.Comment: 5 page

Actin filament assembly by bacterial factors VopL/F: Which end is up?

Competing models have been proposed for actin filament nucleation by the bacterial proteins VopL/F. In this issue, Burke et al. (2017. J. Cell Biol. https://doi.org/10.1083/jcb.201608104) use direct observation to demonstrate that VopL/F bind the barbed and pointed ends of actin filaments but only nucleate new filaments from the pointed end

Finite Cluster Typical Medium Theory for Disordered Electronic Systems

We use the recently developed typical medium dynamical cluster (TMDCA) approach~[Ekuma \etal,~\textit{Phys. Rev. B \textbf{89}, 081107 (2014)}] to perform a detailed study of the Anderson localization transition in three dimensions for the Box, Gaussian, Lorentzian, and Binary disorder distributions, and benchmark them with exact numerical results. Utilizing the nonlocal hybridization function and the momentum resolved typical spectra to characterize the localization transition in three dimensions, we demonstrate the importance of both spatial correlations and a typical environment for the proper characterization of the localization transition in all the disorder distributions studied. As a function of increasing cluster size, the TMDCA systematically recovers the re-entrance behavior of the mobility edge for disorder distributions with finite variance, obtaining the correct critical disorder strengths, and shows that the order parameter critical exponent for the Anderson localization transition is universal. The TMDCA is computationally efficient, requiring only a small cluster to obtain qualitative and quantitative data in good agreement with numerical exact results at a fraction of the computational cost. Our results demonstrate that the TMDCA provides a consistent and systematic description of the Anderson localization transition.Comment: 20 Pages, 19 Figures, 3 Table

Metal-Insulator-Transition in a Weakly interacting Disordered Electron System

The interplay of interactions and disorder is studied using the Anderson-Hubbard model within the typical medium dynamical cluster approximation. Treating the interacting, non-local cluster self-energy ($\Sigma_c[{\cal \tilde{G}}](i,j\neq i)$) up to second order in the perturbation expansion of interactions, $U^2$, with a systematic incorporation of non-local spatial correlations and diagonal disorder, we explore the initial effects of electron interactions ($U$) in three dimensions. We find that the critical disorder strength ($W_c^U$), required to localize all states, increases with increasing $U$; implying that the metallic phase is stabilized by interactions. Using our results, we predict a soft pseudogap at the intermediate $W$ close to $W_c^U$ and demonstrate that the mobility edge ($\omega_\epsilon$) is preserved as long as the chemical potential, $\mu$, is at or beyond the mobility edge energy.Comment: 10 Pages, 8 Figures with Supplementary materials include

In The Best Interest Of The (Adult) Child: Ideas About Kinship Care Of Older Adults

This article uses a qualitative, ethnographic approach to examine the experiences older adults and their kin, as the older adult engages in relocation. Studies looking at caregiving by kin for older adults highlight burdens for the adult child. This study offers a life course perspective on kinship care, analyzing older adults’ decisions’ to move. It was found that many older adults are strongly influenced by the desire to not be cared for by their kin as well as to select housing near their existing social network, which might exclude kin. In conclusion, policy implications are discussed

Optimal feeding and swimming gaits of biflagellated organisms

Locomotion is widely observed in life at micrometric scales and is exhibited by many eukaryotic unicellular organisms. Motility of such organisms can be achieved through periodic deformations of a tail-like projection called the eukaryotic flagellum. Although the mechanism allowing the flagellum to deform is largely understood, questions related to the functional significance of the observed beating patterns remain unresolved. Here, we focus our attention on the stroke patterns of biflagellated phytoplanktons resembling the green alga Chlamydomonas. Such organisms have been widely observed to beat their flagella in two different ways - a breast-stroke and an undulatory stroke-both of which are prototypical of general beating patterns observed in eukaryotes. We develop a general optimization procedure to determine the existence of optimal swimming gaits and investigate their functional significance with respect to locomotion and nutrient uptake. Both the undulatory and the breaststroke represent local optima for efficient swimming. With respect to the generation of feeding currents, we found the breaststroke to be optimal and to enhance nutrient uptake significantly, particularly when the organism is immersed in a gradient of nutrients. Keywords: optimization; stroke kinematics; low Reynolds number; efficiencyNational Science Foundation (U.S.) (Grant CCF-0323672)National Science Foundation (U.S.) (Grant CTS-0624830