The interconnected and heterogeneous nature of the next-generation Electrical Grid (EG), widely known as Smart Grid (SG), bring severe cybersecurity and privacy risks that can also raise domino effects against other Critical Infrastructures (CIs). In this paper, we present an Intrusion Detection System (IDS) specially designed for the SG environments that use Modbus/Transmission Control Protocol (TCP) and Distributed Network Protocol 3 (DNP3) protocols. The proposed IDS called MENSA (anoMaly dEtection aNd claSsificAtion) adopts a novel Autoencoder-Generative Adversarial Network (GAN) architecture for (a) detecting operational anomalies and (b) classifying Modbus/TCP and DNP3 cyberattacks. In particular, MENSA combines the aforementioned Deep Neural Networks (DNNs) in a common architecture, taking into account the adversarial loss and the reconstruction difference. The proposed IDS is validated in four real SG evaluation environments, namely (a) SG lab, (b) substation, (c) hydropower plant and (d) power plant, solving successfully an outlier detection (i.e., anomaly detection) problem as well as a challenging multiclass classification problem consisting of 14 classes (13 Modbus/TCP cyberattacks and normal instances). Furthermore, MENSA can discriminate five cyberattacks against DNP3. The evaluation results demonstrate the efficiency of MENSA compared to other Machine Learning (ML) and Deep Learning (DL) methods in terms of Accuracy, False Positive Rate (FPR), True Positive Rate (TPR) and the F1 score.

Supervisory Control and Data Acquisition (SCADA) systems play a significant role in Critical Infrastructures (CIs) since they monitor and control the automation processes of the industrial equipment. However, SCADA relies on vulnerable communication protocols without any cybersecurity mechanism, thereby making it possible to endanger the overall operation of the CI. In this paper, we focus on the Modbus/TCP protocol, which is commonly utilised in many CIs and especially in the electrical grid. In particular, our contribution is twofold. First, we study and enhance the cyberattacks provided by the Smod pen-testing tool. Second, we introduce an anomaly-based Intrusion Detection System (IDS) capable of detecting Denial of Service (DoS) cyberattacks related to Modbus/TCP. The efficacy of the proposed IDS is demonstrated by utilising real data stemming from a hydropower plant. The accuracy and the F1 score of the proposed IDS reach 81% and 77% respectively.

The Industrial Control Systems (ICS) are the underlying monitoring and control components of critical infrastructures, which consist of a number of distributed field devices, such as Programmable Logic Controllers (PLCs), Remote Terminal Units (RTUs) and Human Machine Interfaces (HMIs). As modern ICS are connected to the Internet, in the context of their digitalization as a part of the Internet of Things (IoT) domain, a number of security threats are introduced, whose exploitation can lead to severe consequences. Honeypots and honeynets are promising countermeasures that attract attackers and mislead them from hacking the real infrastructure, while gaining valuable information about the attack patterns as well as the source of the attack. In this work, we implement an interactive, proofofconcept ICS honeypot, which is based on Conpot, that is able to emulate a physical ICS device, by replicating realistic traffic from the real device. As the honeypot runs inside a Virtual Machine, it is possible to emulate the entire organization's ICS infrastructure, a fact that is very important for the security of the modern critical infrastructure. In order to assess the proposed honeypot, a real-life demonstration scenario was designed, which involves a hydro power plant. The honeypot architecture is provided, while the structural components are presented in detail.

Honeypots are powerful security tools, which are developed to shield commercial and industrial networks from malicious activity. Honeypots act as passive and interactive decoys in a network by attracting malicious activity away from critical network devices. Given that the security incidents against industrial and critical infrastructure are getting sophisticated and persistent, advanced security systems are needed. In this paper, a novel industrial honeypot implementation is presented, which is based on the Modbus protocol, entitled NeuralPot. The presented NeuralPot honeypot is able to emulate industrial Modbus entities in order to actively confuse the intruders. It achieves this by introducing two distinct deep neural networks, a Generative Adversarial Network and an Autoencoder Network, which learn Modbus device behavior and generate realistic-looking traffic behavior. Based on the evaluation results, the proposed industrial honeypot performs well in terms of accuracy, similarity, and elapsed time of data generation.

In recent years and with the advancement of IoT networks, malicious intrusions aiming at disrupting the services and getting access to confidential information in medical environments is ever progressing. To that end, this paper proposes a Federated Layered Architecture to be used in Medical Cyber-Physical Systems (MCPS) Networks that entails the creation of multiple aggregation layers to induce further security to the model training process. Moreover, two Deep Adversarial Neural Networks (GANs) are presented for use with data found in the MCPS environment. The evaluation of the presented work showed that the models trained in the Federated system have an increase in their ability to detect possible intrusions in the MCPS network than the commonly trained models.