9,308 research outputs found
Analysis and evaluation of SafeDroid v2.0, a framework for detecting malicious Android applications
Android smartphones have become a vital component of the daily routine of millions of people, running a plethora of applications available in the official and alternative marketplaces. Although there are many security mechanisms to scan and filter malicious applications, malware is still able to reach the devices of many end-users. In this paper, we introduce the SafeDroid v2.0 framework, that is a flexible, robust, and versatile open-source solution for statically analysing Android applications, based on machine learning techniques. The main goal of our work, besides the automated production of fully sufficient prediction and classification models in terms of maximum accuracy scores and minimum negative errors, is to offer an out-of-the-box framework that can be employed by the Android security researchers to efficiently experiment to find effective solutions: the SafeDroid v2.0 framework makes it possible to test many different combinations of machine learning classifiers, with a high degree of freedom and flexibility in the choice of features to consider, such as dataset balance and dataset selection. The framework also provides a server, for generating experiment reports, and an Android application, for the verification of the produced models in real-life scenarios. An extensive campaign of experiments is also presented to show how it is possible to efficiently find competitive solutions: the results of our experiments confirm that SafeDroid v2.0 can reach very good performances, even with highly unbalanced dataset inputs and always with a very limited overhead
Introducing Molly: Distributed Memory Parallelization with LLVM
Programming for distributed memory machines has always been a tedious task,
but necessary because compilers have not been sufficiently able to optimize for
such machines themselves. Molly is an extension to the LLVM compiler toolchain
that is able to distribute and reorganize workload and data if the program is
organized in statically determined loop control-flows. These are represented as
polyhedral integer-point sets that allow program transformations applied on
them. Memory distribution and layout can be declared by the programmer as
needed and the necessary asynchronous MPI communication is generated
automatically. The primary motivation is to run Lattice QCD simulations on IBM
Blue Gene/Q supercomputers, but since the implementation is not yet completed,
this paper shows the capabilities on Conway's Game of Life
Combining hardware and software instrumentation to classify program executions
Several research efforts have studied ways to infer properties of software systems from program spectra gathered from the running systems, usually with software-level instrumentation. While these efforts appear to produce accurate classifications, detailed understanding of their costs and potential cost-benefit tradeoffs is lacking. In this work we present a hybrid instrumentation approach which uses hardware performance counters to gather program spectra at very low cost. This underlying data is further augmented with data captured by minimal amounts of software-level instrumentation. We also
evaluate this hybrid approach by comparing it to other existing approaches. We conclude that these hybrid spectra can reliably distinguish failed executions from successful executions at a fraction of the runtime overhead cost of using software-based execution data
Measuring the Impact of Adversarial Errors on Packet Scheduling Strategies
In this paper we explore the problem of achieving efficient packet
transmission over unreliable links with worst case occurrence of errors. In
such a setup, even an omniscient offline scheduling strategy cannot achieve
stability of the packet queue, nor is it able to use up all the available
bandwidth. Hence, an important first step is to identify an appropriate metric
for measuring the efficiency of scheduling strategies in such a setting. To
this end, we propose a relative throughput metric which corresponds to the long
term competitive ratio of the algorithm with respect to the optimal. We then
explore the impact of the error detection mechanism and feedback delay on our
measure. We compare instantaneous error feedback with deferred error feedback,
that requires a faulty packet to be fully received in order to detect the
error. We propose algorithms for worst-case adversarial and stochastic packet
arrival models, and formally analyze their performance. The relative throughput
achieved by these algorithms is shown to be close to optimal by deriving lower
bounds on the relative throughput of the algorithms and almost matching upper
bounds for any algorithm in the considered settings. Our collection of results
demonstrate the potential of using instantaneous feedback to improve the
performance of communication systems in adverse environments
The Computational Power of Beeps
In this paper, we study the quantity of computational resources (state
machine states and/or probabilistic transition precision) needed to solve
specific problems in a single hop network where nodes communicate using only
beeps. We begin by focusing on randomized leader election. We prove a lower
bound on the states required to solve this problem with a given error bound,
probability precision, and (when relevant) network size lower bound. We then
show the bound tight with a matching upper bound. Noting that our optimal upper
bound is slow, we describe two faster algorithms that trade some state
optimality to gain efficiency. We then turn our attention to more general
classes of problems by proving that once you have enough states to solve leader
election with a given error bound, you have (within constant factors) enough
states to simulate correctly, with this same error bound, a logspace TM with a
constant number of unary input tapes: allowing you to solve a large and
expressive set of problems. These results identify a key simplicity threshold
beyond which useful distributed computation is possible in the beeping model.Comment: Extended abstract to appear in the Proceedings of the International
Symposium on Distributed Computing (DISC 2015
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