1,538 research outputs found
Detection of Early-Stage Enterprise Infection by Mining Large-Scale Log Data
Recent years have seen the rise of more sophisticated attacks including
advanced persistent threats (APTs) which pose severe risks to organizations and
governments by targeting confidential proprietary information. Additionally,
new malware strains are appearing at a higher rate than ever before. Since many
of these malware are designed to evade existing security products, traditional
defenses deployed by most enterprises today, e.g., anti-virus, firewalls,
intrusion detection systems, often fail at detecting infections at an early
stage.
We address the problem of detecting early-stage infection in an enterprise
setting by proposing a new framework based on belief propagation inspired from
graph theory. Belief propagation can be used either with "seeds" of compromised
hosts or malicious domains (provided by the enterprise security operation
center -- SOC) or without any seeds. In the latter case we develop a detector
of C&C communication particularly tailored to enterprises which can detect a
stealthy compromise of only a single host communicating with the C&C server.
We demonstrate that our techniques perform well on detecting enterprise
infections. We achieve high accuracy with low false detection and false
negative rates on two months of anonymized DNS logs released by Los Alamos
National Lab (LANL), which include APT infection attacks simulated by LANL
domain experts. We also apply our algorithms to 38TB of real-world web proxy
logs collected at the border of a large enterprise. Through careful manual
investigation in collaboration with the enterprise SOC, we show that our
techniques identified hundreds of malicious domains overlooked by
state-of-the-art security products
CharBot: A Simple and Effective Method for Evading DGA Classifiers
Domain generation algorithms (DGAs) are commonly leveraged by malware to
create lists of domain names which can be used for command and control (C&C)
purposes. Approaches based on machine learning have recently been developed to
automatically detect generated domain names in real-time. In this work, we
present a novel DGA called CharBot which is capable of producing large numbers
of unregistered domain names that are not detected by state-of-the-art
classifiers for real-time detection of DGAs, including the recently published
methods FANCI (a random forest based on human-engineered features) and LSTM.MI
(a deep learning approach). CharBot is very simple, effective and requires no
knowledge of the targeted DGA classifiers. We show that retraining the
classifiers on CharBot samples is not a viable defense strategy. We believe
these findings show that DGA classifiers are inherently vulnerable to
adversarial attacks if they rely only on the domain name string to make a
decision. Designing a robust DGA classifier may, therefore, necessitate the use
of additional information besides the domain name alone. To the best of our
knowledge, CharBot is the simplest and most efficient black-box adversarial
attack against DGA classifiers proposed to date
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