  Advanced attack campaigns span across multiple stages and stay stealthy for
long time periods. There is a growing trend of attackers using off-the-shelf
tools and pre-installed system applications (such as \emph{powershell} and
\emph{wmic}) to evade the detection because the same tools are also used by
system administrators and security analysts for legitimate purposes for their
routine tasks. To start investigations, event logs can be collected from
operational systems; however, these logs are generic enough and it often
becomes impossible to attribute a potential attack to a specific attack group.
Recent approaches in the literature have used anomaly detection techniques,
which aim at distinguishing between malicious and normal behavior of computers
or network systems. Unfortunately, anomaly detection systems based on point
anomalies are too rigid in a sense that they could miss the malicious activity
and classify the attack, not an outlier. Therefore, there is a research
challenge to make better detection of malicious activities. To address this
challenge, in this paper, we leverage Group Anomaly Detection (GAD), which
detects anomalous collections of individual data points.
  Our approach is to build a neural network model utilizing Adversarial
Autoencoder (AAE-$\alpha$) in order to detect the activity of an attacker who
leverages off-the-shelf tools and system applications. In addition, we also
build \textit{Behavior2Vec} and \textit{Command2Vec} sentence embedding deep
learning models specific for feature extraction tasks. We conduct extensive
experiments to evaluate our models on real-world datasets collected for a
period of two months. The empirical results demonstrate that our approach is
effective and robust in discovering targeted attacks, pen-tests, and attack
campaigns leveraging custom tools.
