Recent technological innovations along with the vast amount of available data worldwide have led to the rise of cyberattacks against network systems. Intrusion Detection Systems (IDS) play a crucial role as a defense mechanism in networks against adversarial attackers. Machine Learning methods provide various cybersecurity tools. However, these methods require plenty of data to be trained efficiently, which may be hard to collect or to use due to privacy reasons. One of the most notable Machine Learning tools is the Generative Adversarial Network (GAN), and it has great potential for tabular data synthesis. In this work, we start by briefly presenting the most popular GAN architectures, VanillaGAN, WGAN, and WGAN-GP. Focusing on tabular data generation, CTGAN, CopulaGAN, and TableGAN models are used for the creation of synthetic IDS data. Specifically, the models are trained and evaluated on an NSL-KDD dataset, considering the limitations and requirements that this procedure needs. Finally, based on certain quantitative and qualitative methods, we argue and evaluate the most prominent GANs for tabular network data synthesis.

Network intrusion detection is a key pillar towards the sustainability and normal operation of information systems. Complex threat patterns and malicious actors are able to cause severe damages to cyber-systems. In this work, we propose novel Deep Learning formulations for detecting threats and alerts on network logs that were acquired by pfSense, an open-source software that acts as firewall on FreeBSD operating system. pfSense integrates several powerful security services such as firewall, URL filtering, and virtual private networking among others. The main goal of this study is to analyse the logs that were acquired by a local installation of pfSense software, in order to provide a powerful and efficient solution that controls traffic flow based on patterns that are automatically learnt via the proposed, challenging DL architectures. For this purpose, we exploit the Convolutional Neural Networks (CNNs), and the Long Short Term Memory Networks (LSTMs) in order to construct robust multi-class classifiers, able to assign each new network log instance that reaches our system into its corresponding category. The performance of our scheme is evaluated by conducting several quantitative experiments, and by comparing to state-of-the-art formulations.

The diversity of applications that current and emerging Wireless Sensor Networks (WSNs) are called to support imposes different requirements on the underlying network with respect to delay and loss, while at the same time the WSN imposes its own intricacies. The satisfaction of these requirements highly depends on the metric upon which the forwarding routes are decided. In this view, the IETF ROLL group has proposed the RPL routing protocol, which can flexibly work on various routing metrics, as long as they hold specific properties. The system implementer/user is free to decide whether to use one or multiple routing metrics, as well as the way these metrics can be combined. In this paper, we provide ways to quantify the routing metrics so that they can be combined in an additive or lexical manner. We use extensive simulation results to evaluate the impact of several routing metrics on the achieved performance.

The energy sector represents undoubtedly one of the most significant “test cases” for 5G enabling technologies, due to the need of addressing a huge range of very diverse requirements to deal with across a variety of applications (stringent capacity for smart metering/AMI versus latency for supervisory control and fault localization). However, to effectively support energy utilities along their transition towards more decentralized renewable-oriented systems, several open issues still remain as to 5G networks management automation, security, resilience, scalability and portability. To face these issues, we outline a novel 5G PPP-compliant software framework specifically tailored to the energy domain, which combines i) trusted, scalable and lock-in free plug `n' play support for a variety of constrained devices; ii) 5G devices' abstractions to demonstrate mMTC (massive Machine Type Communications), uMTC (critical MTC) and xMBB (Extended Massive BroadBand) communications coupled with partially distributed, trusted, end-to-end security and MCM to enable secure, scalable and energy efficient communications; iii) extended Mobile Edge Computing (xMEC) micro-clouds to reduce backhaul load, increase the overall network capacity and reduce delays, while facilitating the deployment of generic MTC related NFVs (Network Function Virtualisation) and utility-centric VNFs (Virtual Network Functions).

Federated learning (FL) is an emerging machine learning technique where machine learning models are trained in a decentralized manner. The main advantage of this approach is the data privacy it provides because the data are not processed in a centralized device. Moreover, the local client models are aggregated on a server, resulting in a global model that has accumulated knowledge from all the different clients. This approach, however, is vulnerable to attacks because clients can be malicious or malicious actors may interfere within the network. In the first case, these types of attacks may refer to data or model poisoning attacks where the data or model parameters, respectively, may be altered. In this paper, we investigate the data poisoning attacks and, more specifically, the label-flipping case within a federated learning system. For an image classification task, we introduce two variants of data poisoning attacks, namely model degradation and targeted label attacks. These attacks are based on synthetic images generated by a generative adversarial network (GAN). This network is trained jointly by the malicious clients using a concatenated malicious dataset. Due to dataset sample limitations, the architecture and learning procedure of the GAN are adjusted accordingly. Through the experiments, we demonstrate that these types of attacks are effective in achieving their task and managing to fool common federated defenses (stealth). We also propose a mechanism to mitigate these attacks based on clean label training on the server side. In more detail, we see that the model degradation attack causes an accuracy degradation of up to 25%, while common defenses can only alleviate this for a percentage of ∼5%. Similarly, the targeted label attack results in a misclassification of 56% compared to 2.5% when no attack takes place. Moreover, our proposed defense mechanism is able to mitigate these attacks.