Accurate shellcode recognition from network traffic data using artificial neural nets

ONOTU, Patrick, DAY, David and RODRIGUES, Marcos (2015). Accurate shellcode recognition from network traffic data using artificial neural nets. In: Electrical and Computer Engineering (CCECE), 2015 IEEE 28th Canadian Conference on. IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) . IEEE.

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Official URL: http://ieeexplore.ieee.org/document/7129302/
Link to published version:: https://doi.org/10.1109/CCECE.2015.7129302
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    Abstract

    This paper presents an approach to shellcode recognition directly from network traffic data using a multi-layer perceptron with back-propagation learning algorithm. Using raw network data composed of a mixture of shellcode, image files, and DLL-Dynamic Link Library files, our proposed design was able to classify the three types of data with high accuracy and high precision with neither false positives nor false negatives. The proposed method comprises simple and fast pre-processing of raw data of a fixed length for each network data package and yields perfect results with 100\% accuracy for the three data types considered. The research is significant in the context of network security and intrusion detection systems. Work is under way for real time recognition and fine-tuning the differentiation between various shellcodes.

    Item Type: Book Section
    Additional Information: Print ISSN: 0840-7789
    Research Institute, Centre or Group - Does NOT include content added after October 2018: Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
    Departments - Does NOT include content added after October 2018: Faculty of Science, Technology and Arts > Department of Computing
    Identification Number: https://doi.org/10.1109/CCECE.2015.7129302
    Depositing User: David Day
    Date Deposited: 09 May 2017 11:41
    Last Modified: 28 Mar 2019 16:01
    URI: http://shura.shu.ac.uk/id/eprint/15665

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