137,239 research outputs found
Non-intrusive on-the-fly data race detection using execution replay
This paper presents a practical solution for detecting data races in parallel
programs. The solution consists of a combination of execution replay (RecPlay)
with automatic on-the-fly data race detection. This combination enables us to
perform the data race detection on an unaltered execution (almost no probe
effect). Furthermore, the usage of multilevel bitmaps and snooped matrix clocks
limits the amount of memory used. As the record phase of RecPlay is highly
efficient, there is no need to switch it off, hereby eliminating the
possibility of Heisenbugs because tracing can be left on all the time.Comment: In M. Ducasse (ed), proceedings of the Fourth International Workshop
on Automated Debugging (AAdebug 2000), August 2000, Munich. cs.SE/001003
LightBox: Full-stack Protected Stateful Middlebox at Lightning Speed
Running off-site software middleboxes at third-party service providers has
been a popular practice. However, routing large volumes of raw traffic, which
may carry sensitive information, to a remote site for processing raises severe
security concerns. Prior solutions often abstract away important factors
pertinent to real-world deployment. In particular, they overlook the
significance of metadata protection and stateful processing. Unprotected
traffic metadata like low-level headers, size and count, can be exploited to
learn supposedly encrypted application contents. Meanwhile, tracking the states
of 100,000s of flows concurrently is often indispensable in production-level
middleboxes deployed at real networks.
We present LightBox, the first system that can drive off-site middleboxes at
near-native speed with stateful processing and the most comprehensive
protection to date. Built upon commodity trusted hardware, Intel SGX, LightBox
is the product of our systematic investigation of how to overcome the inherent
limitations of secure enclaves using domain knowledge and customization. First,
we introduce an elegant virtual network interface that allows convenient access
to fully protected packets at line rate without leaving the enclave, as if from
the trusted source network. Second, we provide complete flow state management
for efficient stateful processing, by tailoring a set of data structures and
algorithms optimized for the highly constrained enclave space. Extensive
evaluations demonstrate that LightBox, with all security benefits, can achieve
10Gbps packet I/O, and that with case studies on three stateful middleboxes, it
can operate at near-native speed.Comment: Accepted at ACM CCS 201
How migrating 0.0001% of address space saves 12% of energy in hybrid storage
We present a simple, operating-\ud
system independent method to reduce the num-\ud
ber of seek operations and consequently reduce\ud
the energy consumption of a hybrid storage\ud
device consisting of a hard disk and a flash\ud
memory. Trace-driven simulations show that\ud
migrating a tiny amount of the address space\ud
(0.0001%) from disk to flash already results\ud
in a significant storage energy reduction (12%)\ud
at virtually no extra cost. We show that the\ud
amount of energy saving depends on which part\ud
of the address space is migrated, and we present\ud
two indicators for this, namely sequentiality and\ud
request frequency. Our simulations show that\ud
both are suitable as criterion for energy-saving\ud
file placement methods in hybrid storage. We\ud
address potential wear problems in the flash\ud
subsystem by presenting a simple way to pro-\ud
long its expected lifetime.\u
Dynamic Trace-Based Data Dependency Analysis for Parallelization of C Programs
Writing parallel code is traditionally considered a difficult task, even when it is tackled from the beginning of a project. In this paper, we demonstrate an innovative toolset that faces this challenge directly. It provides the software developers with profile data and directs them to possible top-level, pipeline-style parallelization opportunities for an arbitrary sequential C program. This approach is complementary to the methods based on static code analysis and automatic code rewriting and does not impose restrictions on the structure of the sequential code or the parallelization style, even though it is mostly aimed at coarse-grained task-level parallelization. The proposed toolset has been utilized to define parallel code organizations for a number of real-world representative applications and is based on and is provided as free source
CryptoKnight:generating and modelling compiled cryptographic primitives
Cryptovirological augmentations present an immediate, incomparable threat. Over the last decade, the substantial proliferation of crypto-ransomware has had widespread consequences for consumers and organisations alike. Established preventive measures perform well, however, the problem has not ceased. Reverse engineering potentially malicious software is a cumbersome task due to platform eccentricities and obfuscated transmutation mechanisms, hence requiring smarter, more efficient detection strategies. The following manuscript presents a novel approach for the classification of cryptographic primitives in compiled binary executables using deep learning. The model blueprint, a Dynamic Convolutional Neural Network (DCNN), is fittingly configured to learn from variable-length control flow diagnostics output from a dynamic trace. To rival the size and variability of equivalent datasets, and to adequately train our model without risking adverse exposure, a methodology for the procedural generation of synthetic cryptographic binaries is defined, using core primitives from OpenSSL with multivariate obfuscation, to draw a vastly scalable distribution. The library, CryptoKnight, rendered an algorithmic pool of AES, RC4, Blowfish, MD5 and RSA to synthesise combinable variants which automatically fed into its core model. Converging at 96% accuracy, CryptoKnight was successfully able to classify the sample pool with minimal loss and correctly identified the algorithm in a real-world crypto-ransomware applicatio
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