  Machine-learning based intrusion detection classifiers are able to detect
unknown attacks, but at the same time, they may be susceptible to evasion by
obfuscation techniques. An adversary intruder which possesses a crucial
knowledge about a protection system can easily bypass the detection module. The
main objective of our work is to improve the performance capabilities of
intrusion detection classifiers against such adversaries. To this end, we
firstly propose several obfuscation techniques of remote attacks that are based
on the modification of various properties of network connections; then we
conduct a set of comprehensive experiments to evaluate the effectiveness of
intrusion detection classifiers against obfuscated attacks. We instantiate our
approach by means of a tool, based on NetEm and Metasploit, which implements
our obfuscation operators on any TCP communication. This allows us to generate
modified network traffic for machine learning experiments employing features
for assessing network statistics and behavior of TCP connections. We perform
the evaluation of five classifiers: Gaussian Naive Bayes, Gaussian Naive Bayes
with kernel density estimation, Logistic Regression, Decision Tree, and Support
Vector Machines. Our experiments confirm the assumption that it is possible to
evade the intrusion detection capability of all classifiers trained without
prior knowledge about obfuscated attacks, causing an exacerbation of the TPR
ranging from 7.8% to 66.8%. Further, when widening the training knowledge of
the classifiers by a subset of obfuscated attacks, we achieve a significant
improvement of the TPR by 4.21% - 73.3%, while the FPR is deteriorated only
slightly (0.1% - 1.48%). Finally, we test the capability of an
obfuscations-aware classifier to detect unknown obfuscated attacks, where we
achieve over 90% detection rate on average for most of the obfuscations.
