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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.

Cybersecurity in the Energy sector and relevant information sharing is at the foremost of European strategy towards the digital decade targeted by the EC for the years to come. The proposed Pan-European Incidents Information Sharing Platform (I2SP) offers a cyber-shield armour to European Electrical Power and Energy Systems (EPES) enabling cooperative detection of large scale, cyber-human security and privacy incidents and attacks. Via Incidents Information Sharing Platform, early detection and appropriate mitigation, guarantees the continuity of operations and minimization of cascading effects in the infrastructure itself, the environment, the citizens and the end-users.The Incidents’ Information Sharing Platform (I2SP) constitutes the software package which enables secure Cyber-Threat Intelligence (CTI) information sharing among EPES participants, as well as with trusted nominated entities, such as Information Sharing and Analysis Centers (ISACs), Computer Security Incident Response Teams (CSIRTs) and Security Operations Centers (SOC). I2SP facilitates technical information sharing in view of a warning system and incident reporting across the EU, aligning with the pillars of the new Network Code on Cybersecurity.

Food security is currently considered a huge societal challenge which technology providers, technology adopters, policy makers and consumers altogether are facing. A first step towards more secure and safe food has been attempted through the deployment mainly of Internet of Things based solutions and applications that gather information and provide it to the users. However, these systems are susceptible to attacks e.g. data leakage and information modification, keeping our societies away from the target of secure food. This paper explores the requirements of the farm to fork supply chain with respect to security and proposes a platform that aims at alleviating and mitigating a wide set of attacks.

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 VNF(Virtual Network Functions).