Blackhat US 2012 slides and papers

The recent reading/presentation of BH US 2012 can be downloaded here.

Paper accepted at UKSIM 2012, Cambridge, UK

My paper was submitted at UKSIM 2012, Cambridge, UK and was accepted
To quote, use to following BibTeX:

@INPROCEEDINGS{Ahma1203:Malware,
AUTHOR="Muhammad Najmi {Ahmad Zabidi}",
TITLE="Malware Analysis with Multiple Features",
BOOKTITLE="UKSim 14th International Conference on Computer Modelling and Simulation,
UKSim2012 (UKSim2012)",
ADDRESS="Cambridge, United Kingdom",
DAYS=28,
MONTH=mar,
YEAR=2012,
KEYWORDS="malware, static analysis, feature selection",
ABSTRACT="Malware analysis process is being categorized into static analysis and
dynamic analysis. Both static and dynamic analysis have their own strengths
and weaknesses. In this paper, we present a tool written in Python
programming language called as pi-ngaji, which could assist the work of
malware analyst to get the static features of malware. pi-ngaji contains
several modules - Application Programming Interface (API) calls extractor,
binary entropy information, anti virtual machine and anti debugger detector
and XOR encrypted strings decryptor. pi-ngaji was developed in order to
assist our work in getting malware features. pi-ngaji is focusing on
ripping Microsoft Windows executable binaries' malicious features."
}

New published paper - 2011

I presented my paper in 7th IAS 2011 conference in Melaka last December 2011. The paper now already appears in IEEExplore. Get it here

Since I already transfer my copyright to them, you have to get (paid) access to the paper. If your organization subscribe for them, you'll be able to access the paper.

Most universities have access to IEEExplore, should not be a problem.

To cite my paper, use the following BibTex entry

@INPROCEEDINGS{6122799,
author={Zabidi, Muhammad Najmi Ahmad and Maarof, Mohd Aizaini and Zainal, Anazida},
booktitle={Information Assurance and Security (IAS), 2011 7th International Conference on},
title={Ensemble based categorization and adaptive model for malware detection},
year={2011},
month={dec.},
volume={},
number={},
pages={80 -85},
keywords={},
doi={10.1109/ISIAS.2011.6122799},
ISSN={},}