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.

Robust chaotic systems, due to their inherent properties of mixing, ergodicity, and larger chaotic parameter space, constitute a perfect candidate for cryptography. This paper reports a novel method to generate random numbers using modified robust logistic map (MRLM). The non-smooth probability distribution function of robust logistic map (RLM) trajectories gives an un-even binary distribution in randomness test. To overcome this disadvantage in RLM, control of chaos (CoC) is proposed for smooth probability distribution function of RLM. For testing the proposed design, cryptographic random numbers generated by MRLM were vetted with National Institute of Standards and Technology statistical test suite (NIST 800-22). The results showed that proposed MRLM generates cryptographically secure random numbers (CSPRNG).

Power-Line Communication (PLC) allows data transmission through existing power lines, thus avoiding the expensive deployment of ad hoc network infrastructures. However, power line networks remain vastly unattended, which allows tampering by malicious actors. In fact, an attacker can easily inject a malicious signal (jamming) with the aim of disrupting ongoing communications. In this paper, we propose a new solution to detect jamming attacks before they significantly affect the quality of the communication link, thus allowing the detection of a jammer (geographically) far away from a receiver. We consider two scenarios as a function of the receiver’s ability to know in advance the impact of the jammer on the received signal. In the first scenario (jamming-aware), we leverage a classifier based on a Convolutional Neural Network, which has been trained on both jammed and non-jammed signals. In the second scenario (jamming-unaware), we consider a one-class classifier based on autoencoders, allowing us to address the challenge of jamming detection as a classical anomaly detection problem. Our proposed solution can detect jamming attacks on PLC networks with an accuracy greater than 99% even when the jammer is 68 m away from the receiver while requiring training only on traffic acquired during the regular operation of the target PLC network.

In the era of pervasive computing, a generation of lightweight ciphers was needed to provide near-optimal security; officially, NIST announced the standard lightweight cryptographic primitive. The newly announced standard, ASCON, is based on a 5-bit input and 5-bit output Substitution Box (S-box). This paper proposes a key-dependent S-box methodology for small-size S-box for Internet of Things (IoT) devices widely used in many applications such as Smart Grids, healthcare, etc. We aided this study with the chaotic logistic map to generate a robust S-box. The key-dependent S-box can add an extra layer of security to the cryptosystem specifically for lightweight devices at the minimal cost of generation time of a new S-box. We have investigated the proposed methodology with cryptographic properties of S-box, such as Differential Probability (DP), Linear Probability (LP), Nonlinearity, Strict Avalanche Criteria (SAC), Boomerang Connectivity Table (BCT), Key-space analysis, and implementation on Arduino. We also compared the proposed methodology with other state-of-the-art (SOTA) 5 – bit input and output S-boxes. Experimental results witness the robustness of the proposed study.

Power Line Communications (PLC) is a well-established technology that allows devices connected to the power line to communicate with each other. While the majority of research in this field is devoted to issues of availability, the topic of Denial of Service (DoS) attacks has not been sufficiently addressed. Typically, current solutions might detect a jammer when situated near the target devices, yet the equipment under jamming interference may face challenges in communicating an alarm. However, when these systems are placed at a significant distance from the jammer, the negligible impact of the jamming renders its detection hardly detectable. In this work, we propose a solution to identify the presence of a jammer in a PLC infrastructure even when deployed at a significant distance. We analyze the physical layer of the PLC link and adopt state-of-the-art Deep Learning techniques to detect jamming even at a distance where the jammer's effect is negligible, thus allowing the device to trigger an alarm. Considering a jammer featuring the same transmission power as legitimate devices, we prove that we can detect the presence of such a jammer with an overwhelming probability (higher than 0.99) even at a distance of 75 m from the source.