  As the number of heterogenous IP-connected devices and traffic volume
increase, so does the potential for security breaches. The undetected
exploitation of these breaches can bring severe cybersecurity and privacy
risks. Anomaly-based \acp{IDS} play an essential role in network security. In
this paper, we present a practical unsupervised anomaly-based deep learning
detection system called ARCADE (Adversarially Regularized Convolutional
Autoencoder for unsupervised network anomaly DEtection). With a convolutional
\ac{AE}, ARCADE automatically builds a profile of the normal traffic using a
subset of raw bytes of a few initial packets of network flows so that potential
network anomalies and intrusions can be efficiently detected before they cause
more damage to the network. ARCADE is trained exclusively on normal traffic. An
adversarial training strategy is proposed to regularize and decrease the
\ac{AE}'s capabilities to reconstruct network flows that are out-of-the-normal
distribution, thereby improving its anomaly detection capabilities. The
proposed approach is more effective than state-of-the-art deep learning
approaches for network anomaly detection. Even when examining only two initial
packets of a network flow, ARCADE can effectively detect malware infection and
network attacks. ARCADE presents 20 times fewer parameters than baselines,
achieving significantly faster detection speed and reaction time.
