Smart grid brings various advantages such as increased automation in decision making, tighter coupling between production and consumption, and increased digitalization. Because of the many changes that the smart grid inflicts on the power grid as critical infrastructure, cyber security and robust resilience against cyberattacks are essential to handle. With an increased number of attack interfaces and more use of IP-enabled communication, digital stations or IEC 61850 substations need to operate according to a zero-trust security model. Cyber resilience needs to be an integrated part of the substation and its components. This paper presents an experiment utilizing a Hardware-In-the-Loop (HIL) Digital Station environment (enclave), where the focus is on attacking the SCADA protocol IEC 60870-5-104. We implemented 14 attacks, the attacks are described in detail, including the result of each attack action. Furthermore, the paper discusses the implications of the findings in the experiment and what power grid asset owners can do to protect their substations as part of their digitizing efforts.

The Internet of Things (IoT) is a reality that changes several aspects of our daily life, from smart home monitoring to the management of critical infrastructure. The “Routing Protocol for low power and Lossy networks” (RPL) is the only de-facto standardized routing protocol in IoT networks and is thus deployed in environmental monitoring, healthcare, smart building, and many other IoT applications. In literature, we can find several attacks aiming to affect and disrupt RPL-based networks. Therefore, it is fundamental to develop security mechanisms that detect and mitigate any potential attack in RPL-based networks. Current state-of-the-art security solutions deal with very few attacks while introducing heavy mechanisms at the expense of IoT devices and the overall network performance. In this work, we aim to develop an Intrusion Detection System (IDS) capable of dealing with multiple attacks while avoiding any RPL overhead. The proposed system is called DETONAR - DETector of rOutiNg Attacks in Rpl - and it relies on a packet sniffing approach. DETONAR uses a combination of signature and anomaly-based rules to identify any malicious behavior in the traffic (e.g., application and DIO packets). To the best of our knowledge, there are no exhaustive datasets containing RPL traffic for a vast range of attacks. To overcome this issue and evaluate our IDS, we propose RADAR - Routing Attacks DAtaset for Rpl: the dataset contains five simulations for each of the 14 considered attacks in 16 static-nodes networks. DETONAR’s attack detection exceeds 80% for 10 attacks out of 14, while maintaining false positives close to zero.

Recently, the increasing spread of Online Social Networks (OSNs) provided an unprecedented opportunity of analysing online traces of human behaviour to get insight on individuals and society. Among the others, the possibility of predicting users' political orientation relying on data extracted from OSNs received growing attention. In this study, we introduce and make publicly available a dataset composed of 6.685 unique Twitter users and 9.593.055 Tweets. Differently from most of the dataset currently available in the literature, here, each user was manually labeled according to their political orientation by a pool of human judges, using strict inclusion criteria. Further, we address the feasibility of the automatic classification of Italian Twitter users' political orientation based on their Tweets content. Our analysis focuses first on implementing a series of classifiers with the aim of predicting users' political preference as right- or left-oriented. The built models were then evaluated for inferring the political orientation of those users supporting “Movimento 5 Stelle” (M5S), an Italian political party with a still unclear political leaning. Results show high performances on the left-right classification task, with accuracy rates up to 93%. Finally, classification performances obtained on M5S supporters and possible applications of our findings are discussed.

Summary The cyber‐security threats to low‐cost end‐user devices could severely undermine the expected deployment of Internet of Thing (IoT) solutions in a range of real‐world applications such as environment monitoring, transportation, and manufacturing. Additionally, the huge amount of data generated by these devices posses new challenges concerning tasks such as efficient information acquisition and analysis, decision making, and action implementation. In this paper, we propose CENSOR, a novel cloud‐enabled secure IoT network architecture based on SDN paradigm. We discuss the significant benefits as well as challenges that are inherent while performing integration of SDN and IoT in CENSOR. We show that the emerging software‐based networking features combined with the cloud computing solutions can significantly improve the security and communication reliability in the target IoT scenarios. In particular, to provide the adequate security measures in the network, CENSOR uses a lightweight and scalable software remote attestation scheme, which ensures the integrity of the software that is being executed by the IoT devices to achieve the application specific goals in the network. We further discuss the improvements in data communication and data overhead that can be achieved in CENSOR due to its convergence with the cloud computing (at back‐end) and fog computing services (at edge routers or front‐end). A Smart City use‐case has been considered as a target IoT scenario to analyze the feasibility and effectiveness of CENSOR concerning the communication security and the network scalability parameters. Additionally, we provide future research directions along with the recent industry initiatives that include open issues in the integration and deployment of cloud‐enabled SDN‐based IoT networks.

Due to recent notorious security threats, like Mirai-botnet, it is challenging to perform efficient data communication and routing in low power and lossy networks (LLNs) such as Internet of Things (IoT), in which huge data collection and processing are predictable. The Routing Protocol for low power and Lossy networks (RPL) is recently standardized as a routing protocol for LLNs. However, the lack of scalability and the vulnerabilities towards various security threats still pose a significant challenge in the broader adoption of RPL in LLNs.To address these challenges, we propose SPLIT, a secure and scalable RPL routing protocol for IoT networks. SPLIT effectively uses a lightweight remote attestation technique to ensure software integrity of network nodes. To avoid additional overhead caused by attestation messages, SPLIT piggybacks attestation process on the RPL's control messages. Thus, SPLIT enjoys the low energy consumption and scalability features of RPL protocol, which are essential in resource-constrained large scale networks such as IoT. The simulation results for different IoT scenarios show the effectiveness of SPLIT compared to the state-of-the-art in presence of different types of attacks, concerning metrics such as packet delivery ratio and energy consumption.

In this paper, we present REMI, a reliable and secure multicast routing protocol for IoT networks. The main aim of REMI is to enable efficient communication in low-power and lossy networks such as IoT, by ensuring that a message will be received by all its intended destinations, irrespective of the network size and the presence of misbehaving nodes. REMI uses a cluster-based routing approach that triggers a faster multicast dissemination of messages within the network. We implemented REMI with Contiki, a multitasking operating system which is widely adopted by industry for deploying energy-constrained and memory-efficient wireless networks. To assess the effectiveness and efficiency of REMI, we run a thorough set of simulations. Our results show the effectiveness of our protocol over state-of-art protocols in terms of network throughput, propagation delay, and scalability at the cost of minimal overheads in terms of energy consumption and memory utilization.