期刊
COMPLEX ADAPTIVE SYSTEMS, 2015
卷 61, 期 -, 页码 349-354出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procs.2015.09.156
关键词
Neural networks; Classification; Cyber security; Signal processing; Deep learning
Recent cyber security events have demonstrated the need for algorithms that adapt to the rapidly evolving threat landscape of complex network systems. In particular, human analysts often fail to identify data exfiltration when it is encrypted or disguised as innocuous data. Signature-based approaches for identifying data types are easily fooled and analysts can only investigate a small fraction of network events. However, neural networks can learn to identify subtle patterns in a suitably chosen input space. To this end, we have developed a signal processing approach for classifying data files which readily adapts to new data formats. We evaluate the performance for three input spaces consisting of the power spectral density, byte probability distribution and sliding-window entropy of the byte sequence in a file. By combining all three, we trained a deep neural network to discriminate amongst nine common data types found on the Internet with 97.4% accuracy. (C) 2015 The Authors. Published by Elsevier B.V.
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