ARCADE: Adversarially Regularized Convolutional Autoencoder for Network Anomaly Detection

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. 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). ARCADE exploits the property of 1D Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GAN) to automatically build a profile of the normal traffic based on a subset of raw bytes of a few initial packets of network flows so that potential network anomalies and intrusions can be effectively detected before they could cause any more damage to the network. A convolutional Autoencoder (AE) is proposed that suits online detection in resource-constrained environments, and can be easily improved for environments with higher computational capabilities. An adversarial training strategy is proposed to regularize and decrease the AE's capabilities to reconstruct network flows that are out of the normal distribution, and thereby improve its anomaly detection capabilities. The proposed approach is more effective than existing state-of-the-art deep learning approaches for network anomaly detection and significantly reduces detection time. The evaluation results show that the proposed approach is suitable for anomaly detection on resource-constrained hardware platforms such as Raspberry Pi.

Metaheuristics for the Online Printing Shop Scheduling Problem

In this work, the online printing shop scheduling problem introduced in (Lunardi et al., Mixed Integer Linear Programming and Constraint Programming Models for the Online Printing Shop Scheduling Problem, Computers & Operations Research, to appear) is considered. This challenging real scheduling problem, that emerged in the nowadays printing industry, corresponds to a flexible job shop scheduling problem with sequencing flexibility; and it presents several complicating specificities such as resumable operations, periods of unavailability of the machines, sequence-dependent setup times, partial overlapping between operations with precedence constraints, and fixed operations, among others. A local search strategy and metaheuristic approaches for the problem are proposed and evaluated. Based on a common representation scheme, trajectory and populational metaheuristics are considered. Extensive numerical experiments with large-sized instances show that the proposed methods are suitable for solving practical instances of the problem; and that they outperform a half-heuristic-half-exact off-the-shelf solver by a large extent. Numerical experiments with classical instances of the flexible job shop scheduling problem show that the introduced methods are also competitive when applied to this particular case.