Physical-layer security is regaining traction in the research community, due to the performance boost introduced by deep learning classification algorithms. This is particularly true for sender authentication in wireless communications via radio fingerprinting. However, previous research mainly focused on terrestrial wireless devices while, to the best of our knowledge, none of the previous work considered satellite transmitters. The satellite scenario is generally challenging because, among others, satellite radio transducers feature non-standard electronics (usually aged and specifically designed for harsh conditions). Moreover, the fingerprinting task is specifically difficult for Low-Earth Orbit (LEO) satellites (like the ones we focus in this paper) since they feature a low bit-rate and orbit at about 800 Km from the Earth, at a speed of around 25,000 Km/h, thus making the receiver experiencing a down-link with unique attenuation and fading characteristics. In this paper, we investigate the effectiveness and main limitations of AI-based solutions to the physical-layer authentication of LEO satellites. Our study is performed on massive real data—more than $100M$ I-Q samples—collected from an extensive measurements campaign on the IRIDIUM LEO satellites constellation, lasting 589 hours. Our results show that Convolutional Neural Networks (CNN) and autoencoders (if properly calibrated) can be successfully adopted to authenticate the satellite transducers, with an accuracy spanning between 0.8 and 1, depending on prior assumptions. However, the relatively high number of I-Q samples required by the proposed methodology, coupled with the low bandwidth of satellite link, might prevent the detection of the spoofing attack under certain configuration parameters.

MITRE ATT&CK is a comprehensive framework of adversary tactics, techniques, and procedures based on real-world observations. It has been used as a foundation for threat modeling in different sectors, such as government, academia, and industry. To the best of our knowledge, no previous work has been devoted to the comprehensive collection, study, and investigation of the current state of the art leveraging the MITRE ATT&CK framework. We select and inspect more than 50 major research contributions, while conducting a detailed analysis of their methodology and objectives in relation to the MITRE ATT&CK framework. We provide a categorization of the identified papers according to different criteria such as use cases, application scenarios, adopted methodologies, and the use of additional data. Finally, we discuss open issues and future research directions involving not only the MITRE ATT&CK framework but also the fields of threat analysis, threat modeling, and in general cyber-threat intelligence.

Cognitive Radio Networks (CRNs) are recognized as the enabling technology for improving future bandwidth utilization. CRNs have intrinsic security threats due to their opportunistic exploitation of the bandwidth. In particular, jamming is one of the most challenging security threats for CRNs. In this work we provide several contributions about jamming in CRNs: we classify the various jamming strategies, provide a review of recent anti-jamming countermeasures, and introduce a novel anti-jamming solution for CRNs. Our solution is based on a distributed, probabilistic protocol that, while on the one hand could introduce some (limited) delay in message propagation, on the other hand enjoys several features: it avoids control channels for the coordination of the secondary users; does not need any information related to the node neighborhood; and it does not leverage any knowledge or statistics about the channel usage. Given the novelty of the proposal and its potential to thwart jamming in CRNs, we believe it could pave the way for further research in the field.

In the recent years, cyberattacks to smart grids are becoming more frequent. Among the many malicious activities that can be launched against smart grids, the False Data Injection (FDI) attacks have raised significant concerns from both academia and industry. FDI attacks can affect the (internal) state estimation process—critical for smart grid monitoring and control—thus being able to bypass conventional Bad Data Detection (BDD) methods. Hence, prompt detection and precise localisation of FDI attacks are becoming of paramount importance to ensure smart grids security and safety. Several papers recently started to study and analyse this topic from different perspectives and address existing challenges. Data‐driven techniques and mathematical modelling are the major ingredients of the proposed approaches. The primary objective is to provide a systematic review and insights into FDI attacks joint detection and localisation approaches considering that other surveys mainly concentrated on the detection aspects without detailed coverage of localisation aspects. For this purpose, more than 40 major research contributions were selected and inspected, while conducting a detailed analysis of the methodology and objectives in relation to the FDI attacks detection and localisation. Key findings of the identified papers were provided according to different criteria, such as employed FDI attacks localisation techniques, utilised evaluation scenarios, investigated FDI attack types, application scenarios, adopted methodologies and the use of additional data. Finally, open issues and future research directions were discussed.

MITRE ATT&CK is a comprehensive knowledge-base of adversary tactics, techniques, and procedures (TTP) based on real-world attack scenarios. It has been used in different sectors, such as government, academia, and industry, as a foundation for threat modeling, risk assessment, and defensive strategies. There are valuable insights within MITRE ATT&CK knowledge-base that can be applied to various fields and applications, such as risk assessment, threat characterization, and attack modeling. No previous work has been devoted to the comprehensive collection and investigation of statistical insights of the MITRE ATT&CK dataset. Hence, this work aims to extract, analyze, and represent MITRE ATT&CK statistical insights providing valuable recommendations to improve the security aspects of Enterprise, Industrial Control Systems (ICS), and mobile digital infrastructures. For this purpose, we conduct a hierarchical analysis starting from MITRE ATT&CK threat profiles toward the list of techniques in the MITRE ATT&CK database. Finally, we summarize our key findings while providing recommendations that will pave the way for future research in the area.