1,387 research outputs found
Exponential dichotomies of evolution operators in Banach spaces
This paper considers three dichotomy concepts (exponential dichotomy, uniform
exponential dichotomy and strong exponential dichotomy) in the general context
of non-invertible evolution operators in Banach spaces. Connections between
these concepts are illustrated. Using the notion of Green function, we give
necessary conditions and sufficient ones for strong exponential dichotomy. Some
illustrative examples are presented to prove that the converse of some
implication type theorems are not valid
Data augmentation and transfer learning to classify malware images in a deep learning context
In the past few years, malware classification techniques have shifted from shallow traditional machine learning models to deeper neural network architectures. The main benefit of some of these is the ability to work with raw data, guaranteed by their automatic feature extraction capabilities. This results in less technical expertise needed while building the models, thus less initial pre-processing resources. Nevertheless, such advantage comes with its drawbacks, since deep learning models require huge quantities of data in order to generate a model that generalizes well. The amount of data required to train a deep network without overfitting is often unobtainable for malware analysts. We take inspiration from image-based data augmentation techniques and apply a sequence of semantics-preserving syntactic code transformations (obfuscations) to a small dataset of programs to generate a larger dataset. We then design two learning models, a convolutional neural network and a bi-directional long short-term memory, and we train them on images extracted from compiled binaries of the newly generated dataset. Through transfer learning we then take the features learned from the obfuscated binaries and train the models against two state of the art malware datasets, each containing around 10 000 samples. Our models easily achieve up to 98.5% accuracy on the test set, which is on par or better than the present state of the art approaches, thus validating the approach
Choreographies in Practice
Choreographic Programming is a development methodology for concurrent
software that guarantees correctness by construction. The key to this paradigm
is to disallow mismatched I/O operations in programs, called choreographies,
and then mechanically synthesise distributed implementations in terms of
standard process models via a mechanism known as EndPoint Projection (EPP).
Despite the promise of choreographic programming, there is still a lack of
practical evaluations that illustrate the applicability of choreographies to
concrete computational problems with standard concurrent solutions. In this
work, we explore the potential of choreographies by using Procedural
Choreographies (PC), a model that we recently proposed, to write distributed
algorithms for sorting (Quicksort), solving linear equations (Gaussian
elimination), and computing Fast Fourier Transform. We discuss the lessons
learned from this experiment, giving possible directions for the usage and
future improvements of choreography languages
A Semantics-Based Approach to Malware Detection
Malware detection is a crucial aspect of software security. Current malware detectors work by checking for signatures, which attempt to capture the syntactic characteristics of the machine-level byte sequence of the malware. This reliance on a syntactic approach makes current detectors vulnerable to code obfuscations, increasingly used by malware writers, that alter the syntactic properties of the malware byte sequence without significantly affecting their execution behavior. This paper takes the position that the key to malware identification lies in their semantics. It proposes a semantics-based framework for reasoning about malware detectors and proving properties such as soundness and completeness of these detectors. Our approach uses a trace semantics to characterize the behavior of malware as well as that of the program being checked for infection, and uses abstract interpretation to ``hide'' irrelevant aspects of these behaviors. As a concrete application of our approach, we show that (1) standard signature matching detection schemes are generally sound but not complete, (2) the semantics-aware malware detector proposed byChristodorescu et al. is complete with respect to a number of common obfuscations used by malware writers and (3) the malware detection scheme proposed by Kinder et al. and based on standard model-checking techniques is sound in general and complete on some, but not all, obfuscations handled by the semantics-aware malware detector
The Paths to Choreography Extraction
Choreographies are global descriptions of interactions among concurrent
components, most notably used in the settings of verification (e.g., Multiparty
Session Types) and synthesis of correct-by-construction software (Choreographic
Programming). They require a top-down approach: programmers first write
choreographies, and then use them to verify or synthesize their programs.
However, most existing software does not come with choreographies yet, which
prevents their application.
To attack this problem, we propose a novel methodology (called choreography
extraction) that, given a set of programs or protocol specifications,
automatically constructs a choreography that describes their behavior. The key
to our extraction is identifying a set of paths in a graph that represents the
symbolic execution of the programs of interest. Our method improves on previous
work in several directions: we can now deal with programs that are equipped
with a state and internal computation capabilities; time complexity is
dramatically better; we capture programs that are correct but not necessarily
synchronizable, i.e., they work because they exploit asynchronous
communication
A model for adapting 3D graphics based on scalable coding, real-time simplification and remote rendering
Most current multiplayer 3D games can only be played on dedicated platforms, requiring specifically designed content and communication over a predefined network. To overcome these limitations, the OLGA (On-Line GAming) consortium has devised a framework to develop distributive, multiplayer 3D games. Scalability at the level of content, platforms and networks is exploited to achieve the best trade-offs between complexity and quality
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