Statistical Detection of Malicious PE-Executables for Fast Offline Analysis - Communications and Multimedia Security
Conference Papers Year : 2010

Statistical Detection of Malicious PE-Executables for Fast Offline Analysis

Abstract

While conventional malware detection approaches increasingly fail, modern heuristic strategies often perform dynamically, which is not possible in many applications due to related effort and the quantity of files. Based on existing work from [1] and [2] we analyse an approach towards statistical malware detection of PE executables. One benefit is its simplicity (evaluating 23 static features with moderate resource constrains), so it might support the application on large file amounts, e.g. for network-operators or a posteriori analyses in archival systems. After identifying promising features and their typical values, a custom hypothesis-based classification model and a statistical classification approach using the WEKA machine learning tool [3] are generated and evaluated. The results of large-scale classifications are compared showing that the custom, hypothesis based approach performs better on the chosen setup than the general purpose statistical algorithms. Concluding, malicious samples often have special characteristics so existing malware-scanners can effectively be supported.
Fichier principal
Vignette du fichier
cms2010_submission_37.pdf (216.46 Ko) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-01056387 , version 1 (18-08-2014)

Licence

Identifiers

Cite

Ronny Merkel, Tobias Hoppe, Christian Kraetzer, Jana Dittmann. Statistical Detection of Malicious PE-Executables for Fast Offline Analysis. 11th IFIP TC 6/TC 11 International Conference on Communications and Multimedia Security (CMS), May 2010, Linz, Austria. pp.93-105, ⟨10.1007/978-3-642-13241-4_10⟩. ⟨hal-01056387⟩
189 View
663 Download

Altmetric

Share

More