DeepAPT: Nation-State APT Attribution Using End-to-End Deep Neural Networks

In recent years numerous advanced malware, aka advanced persistent threats (APT) are allegedly developed by nation-states. The task of attributing an APT to a specific nation-state is extremely challenging for several reasons. Each nation-state has usually more than a single cyber unit that develops such advanced malware, rendering traditional authorship attribution algorithms useless. Furthermore, those APTs use state-of-the-art evasion techniques, making feature extraction challenging. Finally, the dataset of such available APTs is extremely small. In this paper we describe how deep neural networks (DNN) could be successfully employed for nation-state APT attribution. We use sandbox reports (recording the behavior of the APT when run dynamically) as raw input for the neural network, allowing the DNN to learn high level feature abstractions of the APTs itself. Using a test set of 1,000 Chinese and Russian developed APTs, we achieved an accuracy rate of 94.6%

Direct Method Transcription for a Human-Class Translunar Injection Trajectory Optimization

This paper presents a new trajectory optimization software package developed in the framework of a low-to-high fidelity 3 degrees-of-freedom (DOF)/6-DOF vehicle simulation program named Mission Analysis Simulation Tool in Fortran (MASTIF) and its application to a translunar trajectory optimization problem. The functionality of the developed optimization package is implemented as a new "mode" in generalized settings to make it applicable for a general trajectory optimization problem. In doing so, a direct optimization method using collocation is employed for solving the problem. Trajectory optimization problems in MASTIF are transcribed to a constrained nonlinear programming (NLP) problem and solved with SNOPT, a commercially available NLP solver. A detailed description of the optimization software developed is provided as well as the transcription specifics for the translunar injection (TLI) problem. The analysis includes a 3-DOF trajectory TLI optimization and a 3-DOF vehicle TLI simulation using closed-loop guidance

Role of dynamic Jahn-Teller distortions in Na2C60 and Na2CsC60 studied by NMR

Through 13C NMR spin lattice relaxation (T1) measurements in cubic Na2C60, we detect a gap in its electronic excitations, similar to that observed in tetragonal A4C60. This establishes that Jahn-Teller distortions (JTD) and strong electronic correlations must be considered to understand the behaviour of even electron systems, regardless of the structure. Furthermore, in metallic Na2CsC60, a similar contribution to T1 is also detected for 13C and 133Cs NMR, implying the occurence of excitations typical of JT distorted C60^{2-} (or equivalently C60^{4-}). This supports the idea that dynamic JTD can induce attractive electronic interactions in odd electron systems.Comment: 3 figure

Genetic testing of canine degenerative myelopathy in the South African Boxer dog population

Canine degenerative myelopathy (DM) is a progressive disease process that is diagnosed late in life and mainly affects the pelvic limbs. Factors that make an ante-mortem definitive diagnosis of DM include: an insidious onset and clinical manifestation that mimics other disease processes of the pelvic limbs (hip dysplasia, cranial cruciate ligament rupture, etc.) or there may even be concurrent disease processes, old-age onset and lack of reliable diagnostic methods. Until recently, South African dog owners had to submit samples to laboratories overseas for genetic testing in order to confirm an affected dog (homozygous A/A) and to aid in the ante-mortem diagnosis of DM. Only affected dogs have been confirmed to manifest the clinical signs of DM. This study aimed to verify whether genetic testing by a local genetic laboratory was possible in order to detect a missense mutation of the superoxide dismutase gene (SOD1) that is implicated in causing the clinical signs of DM. The study also aimed to detect and map the inheritance of this disease process in a local Boxer dog population where the pedigree of the sampled population was known. Venous blood collected from Boxer dogs using a simple random sampling technique. The samples were genotyped for the SOD1:c.118G>A polymorphism. Carrier and affected Boxer dogs were detected. A pedigree that demonstrated the significance of inheriting a carrier or affected state in the population was mapped. The present study concludes that genotyping of the missense mutation in Boxer dogs is possible in South Africa. There are carrier and affected Boxer dogs in the local population, making DM a plausible diagnosis in aged dogs presenting with pelvic limb pathology

Are You Tampering With My Data?

We propose a novel approach towards adversarial attacks on neural networks (NN), focusing on tampering the data used for training instead of generating attacks on trained models. Our network-agnostic method creates a backdoor during training which can be exploited at test time to force a neural network to exhibit abnormal behaviour. We demonstrate on two widely used datasets (CIFAR-10 and SVHN) that a universal modification of just one pixel per image for all the images of a class in the training set is enough to corrupt the training procedure of several state-of-the-art deep neural networks causing the networks to misclassify any images to which the modification is applied. Our aim is to bring to the attention of the machine learning community, the possibility that even learning-based methods that are personally trained on public datasets can be subject to attacks by a skillful adversary.Comment: 18 page

Comparison of high versus low frequency cerebral physiology for cerebrovascular reactivity assessment in traumatic brain injury: a multi-center pilot study

Current accepted cerebrovascular reactivity indices suffer from the need of high frequency data capture and export for post-acquisition processing. The role for minute-by-minute data in cerebrovascular reactivity monitoring remains uncertain. The goal was to explore the statistical time-series relationships between intra-cranial pressure (ICP), mean arterial pressure (MAP) and pressure reactivity index (PRx) using both 10-s and minute data update frequency in TBI. Prospective data from 31 patients from 3 centers with moderate/severe TBI and high-frequency archived physiology were reviewed. Both 10-s by 10-s and minute-by-minute mean values were derived for ICP and MAP for each patient. Similarly, PRx was derived using 30 consecutive 10-s data points, updated every minute. While long-PRx (L-PRx) was derived via similar methodology using minute-by-minute data, with L-PRx derived using various window lengths (5, 10, 20, 30, 40, and 60 min; denoted L-PRx_5, etc.). Time-series autoregressive integrative moving average (ARIMA) and vector autoregressive integrative moving average (VARIMA) models were created to analyze the relationship of these parameters over time. ARIMA modelling, Granger causality testing and VARIMA impulse response function (IRF) plotting demonstrated that similar information is carried in minute mean ICP and MAP data, compared to 10-s mean slow-wave ICP and MAP data. Shorter window L-PRx variants, such as L-PRx_5, appear to have a similar ARIMA structure, have a linear association with PRx and display moderate-to-strong correlations (r ~ 0.700, p Peer reviewe

Transition from ion-coupled to electron-only reconnection: Basic physics and implications for plasma turbulence

Using kinetic particle-in-cell (PIC) simulations, we simulate reconnection conditions appropriate for the magnetosheath and solar wind, i.e., plasma beta (ratio of gas pressure to magnetic pressure) greater than 1 and low magnetic shear (strong guide field). Changing the simulation domain size, we find that the ion response varies greatly. For reconnecting regions with scales comparable to the ion Larmor radius, the ions do not respond to the reconnection dynamics leading to ''electron-only'' reconnection with very large quasi-steady reconnection rates. The transition to more traditional ''ion-coupled'' reconnection is gradual as the reconnection domain size increases, with the ions becoming frozen-in in the exhaust when the magnetic island width in the normal direction reaches many ion inertial lengths. During this transition, the quasi-steady reconnection rate decreases until the ions are fully coupled, ultimately reaching an asymptotic value. The scaling of the ion outflow velocity with exhaust width during this electron-only to ion-coupled transition is found to be consistent with a theoretical model of a newly reconnected field line. In order to have a fully frozen-in ion exhaust with ion flows comparable to the reconnection Alfv\'en speed, an exhaust width of at least several ion inertial lengths is needed. In turbulent systems with reconnection occurring between magnetic bubbles associated with fluctuations, using geometric arguments we estimate that fully ion-coupled reconnection requires magnetic bubble length scales of at least several tens of ion inertial lengths

Cerebral autoregulation monitoring in acute traumatic brain injury: what’s the evidence?

Cerebral autoregulation is conceptualized as a vascular self-regulatory mechanism within the brain. Controlled by elusive relationships between various biophysical processes, it functions to protect the brain against potential damages caused by sudden changes in cerebral perfusion pressures and flow. Following events such as traumatic brain injuries (TBI), autoregulation may be compromised, potentially leading to an unfavorable outcome. In spite of its complexity, autoregulation has been able to be quantified non-invasively within the neuro-critical care setting with the aid of transcranial Doppler. This information is interpreted particularly through calculated derived indices based on commonly-monitored input signals such as arterial blood pressure and intracranial pressure (i.e. Pressure reactivity index (PRx), mean flow index (Mx), etc.). For example, PRx values that trend towards positive numbers are correlated with unfavorable outcome. These predictors are primarily surrogate markers of cerebral hemodynamic activity, although suggesting robust correlations between these indices and patient outcome. This review of the literature seeks to explain the methodology behind the calculations of various measures of autoregulation in adult patients suffering from traumatic brain injuries, and how they can interact with one another to both create larger effects on patient outcome and general outcome prediction models. Insight into the driving forces behind cerebral autoregulation is imperative for guiding both clinical decision-making and global treatment protocols for neuro-critically ill patients. The evidence that autoregulation-oriented therapy may improve outcome after TBI is still oscillating around Level III.Joseph Donnelly is supported by the Woolf Fisher Trust (New Zealand). The funder had no influence over the contents of this manuscript. Frederick A. Zeiler has obtained financial support from: The Royal College of Surgerons of Canada, Harry S. Morton Traveling Fellowship in Surgery, University of Manitoba - McLaughlin Fellowship in Medicine, University of Manitoba - Dean’s Fellowship, the Manitoba Medical Services Foundation (MMSF) and the University of Manitoba Clinican Investigator Program. Eric P. Thelin has obtained financial support from the Swedish Society of Medicine. The funder had no influence over the contents of this manuscript. Both Peter Smielewski and Marek Czosnyka receive licensing fees from ICM+ software (Cambridge Enterprises, Ltd.)