1,966 research outputs found
Lockdown: Dynamic Control-Flow Integrity
Applications written in low-level languages without type or memory safety are
especially prone to memory corruption. Attackers gain code execution
capabilities through such applications despite all currently deployed defenses
by exploiting memory corruption vulnerabilities. Control-Flow Integrity (CFI)
is a promising defense mechanism that restricts open control-flow transfers to
a static set of well-known locations. We present Lockdown, an approach to
dynamic CFI that protects legacy, binary-only executables and libraries.
Lockdown adaptively learns the control-flow graph of a running process using
information from a trusted dynamic loader. The sandbox component of Lockdown
restricts interactions between different shared objects to imported and
exported functions by enforcing fine-grained CFI checks. Our prototype
implementation shows that dynamic CFI results in low performance overhead.Comment: ETH Technical Repor
Machine Learning Aided Static Malware Analysis: A Survey and Tutorial
Malware analysis and detection techniques have been evolving during the last
decade as a reflection to development of different malware techniques to evade
network-based and host-based security protections. The fast growth in variety
and number of malware species made it very difficult for forensics
investigators to provide an on time response. Therefore, Machine Learning (ML)
aided malware analysis became a necessity to automate different aspects of
static and dynamic malware investigation. We believe that machine learning
aided static analysis can be used as a methodological approach in technical
Cyber Threats Intelligence (CTI) rather than resource-consuming dynamic malware
analysis that has been thoroughly studied before. In this paper, we address
this research gap by conducting an in-depth survey of different machine
learning methods for classification of static characteristics of 32-bit
malicious Portable Executable (PE32) Windows files and develop taxonomy for
better understanding of these techniques. Afterwards, we offer a tutorial on
how different machine learning techniques can be utilized in extraction and
analysis of a variety of static characteristic of PE binaries and evaluate
accuracy and practical generalization of these techniques. Finally, the results
of experimental study of all the method using common data was given to
demonstrate the accuracy and complexity. This paper may serve as a stepping
stone for future researchers in cross-disciplinary field of machine learning
aided malware forensics.Comment: 37 Page
Leveraging HTC for UK eScience with very large Condor pools: demand for transforming untapped power into results
We provide an insight into the demand from the UK eScience community for very large HighThroughput Computing resources and provide an example of such a resource in current productionuse: the 930-node eMinerals Condor pool at UCL. We demonstrate the significant benefits thisresource has provided to UK eScientists via quickly and easily realising results throughout a rangeof problem areas. We demonstrate the value added by the pool to UCL I.S infrastructure andprovide a case for the expansion of very large Condor resources within the UK eScience Gridinfrastructure. We provide examples of the technical and administrative difficulties faced whenscaling up to institutional Condor pools, and propose the introduction of a UK Condor/HTCworking group to co-ordinate the mid to long term UK eScience Condor development, deploymentand support requirements, starting with the inaugural UK Condor Week in October 2004
Robust Watermarking using Hidden Markov Models
Software piracy is the unauthorized copying or distribution of software. It is a growing problem that results in annual losses in the billions of dollars. Prevention is a difficult problem since digital documents are easy to copy and distribute. Watermarking is a possible defense against software piracy. A software watermark consists of information embedded in the software, which allows it to be identified. A watermark can act as a deterrent to unauthorized copying, since it can be used to provide evidence for legal action against those responsible for piracy.In this project, we present a novel software watermarking scheme that is inspired by the success of previous research focused on detecting metamorphic viruses. We use a trained hidden Markov model (HMM) to detect a specific copy of software. We give experimental results that show our scheme is robust. That is, we can identify the original software even after it has been extensively modified, as might occur as part of an attack on the watermarking scheme
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