Over the last years we witnessed a renewed interest towards Traffic Classification (TC)
captivated by the rise of Deep Learning (DL). Yet, the vast majority of TC literature lacks …

Traffic monitoring is a critical component of self-driving networks. In particular, any system
that seeks to automatically manage a network's operation must first be equipped with …

Lack of explainability is hindering the practical adoption of high-performance Deep
Reinforcement Learning (DRL) controllers. Prior work focused on explaining the controller …

Neural network (NN)-based network intrusion detection systems (NIDSs) are becoming
popular these days due to their notable advantages. This article reviews the current …

We develop a methodology for the analysis of machine learning (ML) models to detect and
understand biased decisions and apply it to two specific scenarios. In particular, we show …

Intrusion detection systems (IDS) play a critical role in protecting networks from cyber
threats. Currently, intrusion detection methods based on artificial intelligen (AI) stand as the …

Advanced Persistence Threats (APTs) have become one of the major threats to enterprise
security. In the past three years, over 70% of APT campaigns involved Data Exfiltration for …

Deciding which XAI technique is best depends not only on the domain, but also on the given
task, the dataset used, the model being explained, and the target goal of that model. We …

This work aims to enable self-driving networks by tackling the lack of trust that network
operators have in Machine Learning (ML) models. We assess and scrutinize the decision …

A large fraction of recent network management tasks rely on Internet traffic matrices, ranging
from planning and troubleshooting to routing and anomaly detection. Despite extensive …