Automatically combining static malware detection techniques

Malware detection techniques come in many different flavors, and cover different effectiveness and efficiency trade-offs. This paper evaluates a number of machine learning techniques to combine multiple static Android malware detection techniques using automatically constructed decision trees. We identify the best methods to construct the trees. We demonstrate that those trees classify sample apps better and faster than individual techniques alone

Hamiltonian Transformation to Compute Thermo-osmotic Forces.

If a thermal gradient is applied along a fluid-solid interface, the fluid experiences a thermo-osmotic force. In the steady state, this force is balanced by the gradient of the shear stress. Surprisingly, there appears to be no unique microscopic expression that can be used for computing the magnitude of the thermo-osmotic force. Here we report how, by treating the mass M of the fluid particles as a tensor in the Hamiltonian, we can eliminate the balancing shear force in a nonequilibrium simulation and therefore compute the thermo-osmotic force at simple solid-fluid interfaces. We compare the nonequilibrium force measurement with estimates of the thermo-osmotic force based on computing gradients of the stress tensor. We find that the thermo-osmotic force as measured in our simulations cannot be derived from the most common microscopic definitions of the stress tensor

Pressure gradients fail to predict diffusio-osmosis.

We present numerical simulations of diffusio-osmotic flow, i.e. the fluid flow generated by a concentration gradient along a solid-fluid interface. In our study, we compare a number of distinct approaches that have been proposed for computing such flows and compare them with a reference calculation based on direct, non-equilibrium molecular dynamics simulations. As alternatives, we consider schemes that compute diffusio-osmotic flow from the gradient of the chemical potentials of the constituent species and from the gradient of the component of the pressure tensor parallel to the interface. We find that the approach based on treating chemical potential gradients as external forces acting on various species agrees with the direct simulations, thereby supporting the approach of Marbach et al (2017 J. Chem. Phys. 146 194701). In contrast, an approach based on computing the gradients of the microscopic pressure tensor does not reproduce the direct non-equilibrium results.European Union ( European Training Network NANOTRANS Grant 674979)

Microscopic Marangoni Flows Cannot Be Predicted on the Basis of Pressure Gradients.

A concentration gradient along a fluid-fluid interface can cause flow. On a microscopic level, this so-called Marangoni effect can be viewed as being caused by a gradient in the pressures acting on the fluid elements or as the chemical-potential gradients acting on the excess densities of different species at the interface. If the interface thickness can be ignored, all approaches should result in the same flow profile away from the interface. However, on a more microscopic scale, the different expressions result in different flow profiles, only one of which can be correct. Here we compare the results of direct nonequilibrium molecular dynamics simulations with the flows that are generated by pressure and chemical-potential gradients. We find that the approach based on the chemical-potential gradients agrees with the direct simulations, whereas the calculations based on the pressure gradients do not

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).Peer reviewe

A voxel-wise, cascaded classification approach to ischemic stroke lesion segmentation

Automated localisation and segmentation of stroke lesions in patients is of great interest to clinicians and researchers alike. We propose a supervised method based on cascaded extremely randomised trees for lesion segmentation, working on a per voxel basis in native subject space. The proposed pipeline is evaluated in the MICCAI Ischemic Stroke Lesion Segmentation (ISLES) challenge, both with nested cross-validation on the training data as well as on independent, multi-centre test data. We obtained good performance although inter-subject variability is large, and reached 3rd place in the SPES sub-challenge.Robben D., Christiaens D., Rangarajan J.R., Gelderblom J., Joris P., Maes F., Suetens P., ''A voxel-wise, cascaded classification approach to stroke lesion segmentation'', Lecture notes in computer science - Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, vol. 9556, pp. 254-265, 2016 (Ischemic stroke lesion segmentation - ISLES challenge 2015, held in conjunction with MICCAI 2015., October 5, 2015, Munich, Germany) (3rd place in SPES subchallenge).status: publishe

DNS tunneling for network penetration

Most networks are connected to the Internet through firewalls to block attacks from the outside and to limit communication initiated from the inside. Because of the limited, supposedly safe functionality of the Domain Name System protocol, its traffic is by and large neglected by firewalls. The resulting possibility for setting up information channels through DNS tunnels is already known, but all existing implementations require help from insiders to set up the tunnels. This paper presents a new Metasploit module for integrated penetration testing of DNS tunnels and uses that module to evaluate the potential of DNS tunnels as communication channels set up through standard, existing exploits and supporting many different command-and-control malware modules