Recent Posts all posts. On Antivirus Performance Testing. Sat 11 April 2015. Modeling Zero Day Malware Spread. Mon 09 March 2015. Using Machine Learning to Name Malware. Sun 08 March 2015. Indistinguishability obfuscation and malware detection. Fri 03 October 2014. Thu 02 October 2014. Compiling NumPy and SciPy with Intel MKL. Sun 07 April 2013.
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Recent Posts all posts. On Antivirus Performance Testing. Sat 11 April 2015. Modeling Zero Day Malware Spread. Mon 09 March 2015. Using Machine Learning to Name Malware. Sun 08 March 2015. Indistinguishability obfuscation and malware detection. Fri 03 October 2014. Thu 02 October 2014. Compiling NumPy and SciPy with Intel MKL. Sun 07 April 2013.
lqdc.github.io
Compiling NumPy and SciPy with Intel MKL · lqdc blog
http://lqdc.github.io/compiling-numpy-and-scipy-with-intel-mkl.html
Compiling NumPy and SciPy with Intel MKL. If you have an Intel processor, you can take advantage of the Intel MKL, which contains performance optimizations for math routines. Although it works with AMD processors too, Atlas seems to be a better choice there. Intel provides their own guide here. However they skip on some details. Below is how I compiled and tested numpy-1.8, scipy-0.13, and Intel MKL-11.0 on Ubuntu 12.10. 1 See how fast your current numpy is:. I get the following on an i5-3570k machine:.
All Categories · lqdc blog
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Sun 07 April 2013. Compiling NumPy and SciPy with Intel MKL. Sat 11 April 2015. On Antivirus Performance Testing. Mon 09 March 2015. Modeling Zero Day Malware Spread. Sun 08 March 2015. Using Machine Learning to Name Malware. Fri 03 October 2014. Indistinguishability obfuscation and malware detection. Thu 02 October 2014.
On Antivirus Performance Testing · lqdc blog
http://lqdc.github.io/on-antivirus-performance-testing.html
On Antivirus Performance Testing. There are lots of malware testing companies/websites/articles including av-tests. All of them disagree about which product is better every year and also change their opinion about the best ones every now and then. The performance of the top AVs on these sites is always very close to 100% detection. This seems very strange. The problem of course is that testing the antiviruses performance well is very difficult. Here are some of the issues that make it challenging:. If th...
“Indistinguishability obfuscation” and malware detection · lqdc blog
http://lqdc.github.io/indistinguishability-obfuscation-and-malware-detection.html
Indistinguishability obfuscation and malware detection. In cryptography, widely reported. On in the media showed that it is possible to reassemble any given program into a mathematical jigsaw puzzle so complicated that, although it functions identically to the original, divining its purpose without running it is effectively impossible. We’ll look at the security implications of these findings. Do the results imply that any malware program can be obfuscated to be indistinguishable from benign programs?
Modeling Zero Day Malware Spread · lqdc blog
http://lqdc.github.io/modeling-zero-day-malware-spread.html
Modeling Zero Day Malware Spread. All models are wrong, but some are useful. George E. P. Box. Does installing an antivirus that catches 2% more malware really make a difference? Should antiviruses try to concentrate on catching yet unknown malware requiring a small delay or is it acceptable if they primarily rely on simpler and faster signature-based detection? These and many other questions can be answered by modeling the behavior of malware and later mapping it to existing malware infection data.
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Recent Posts all posts. On Antivirus Performance Testing. Sat 11 April 2015. Modeling Zero Day Malware Spread. Mon 09 March 2015. Using Machine Learning to Name Malware. Sun 08 March 2015. Indistinguishability obfuscation and malware detection. Fri 03 October 2014. Thu 02 October 2014. Compiling NumPy and SciPy with Intel MKL. Sun 07 April 2013.
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