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The smart grid is one of the core technologies that enable sustainable economic and social developments. In recent years, various cyber attacks have targeted smart grid systems, which have led to severe, harmful consequences. It would be challenging to build a real smart grid system for cybersecurity experimentation and validation purposes. Hence, analytical techniques, with simulations, can be considered as a practical solution to make smart grid cybersecurity experimentation possible. This paper first provides a literature review on the current state-of-the-art in smart grid attack analysis. We then apply graphical security modeling techniques to design and implement a Cyber Attack Analysis Framework for Smart Grids, named GridAttackAnalyzer. A case study with various attack scenarios involving Internet of Things (IoT) devices is conducted to validate the proposed framework and demonstrate its use. The functionality and user evaluations of GridAttackAnalyzer are also carried out, and the evaluation results show that users have a satisfying experience with the usability of GridAttackAnalyzer. Our modular and extensible framework can serve multiple purposes for research, cybersecurity training, and security evaluation in smart grids.

Integration of information technologies with power systems has unlocked unprecedented opportunities in optimization and control fields. Increased data collection and monitoring enable control systems to have a better understanding of the pseudo-real-time condition of power systems. In this fashion, more accurate and effective decisions can be made. This is the key towards mitigating negative impacts of novel technologies such as renewables and electric vehicles and increasing their share in the overall generation portfolio. However, such extensive information exchange has created cybersecurity vulnerabilities in power systems that were not encountered before. It is imperative that these vulnerabilities are understood well, and proper mitigation techniques are implemented. This paper presents an extensive study of cybersecurity concerns in Smart grids in line with latest developments. Relevant standardization and mitigation efforts are discussed in detail and then the classification of different cyber-attacks in smart grid domain with special focus on false data injection (FDI) attack, due to its high impact on different operations. Different uses of this attack as well as developed detection models and methods are analysed. Finally, impacts on smart grid operation and current challenges are presented for future research directions.

The Internet of Things (IoT) network is a fast-growing technology, which is efficiently used in various applications. In an IoT network, the massive amount of connecting nodes is the existence of day-to-day communication challenges. The platform of IoT uses a cloud service as a backend for processing information and maintaining remote control. To manage the developing intricacy of cyberattacks, it is critical to have an effectual intrusion detection system (IDS), which can monitor computer sources and create data on suspicious or abnormal actions. The IoT network’s security can progressively become a critical concern as IoT technology obtains extensive use. Protecting IoT systems with traditional IDS is challenging due to the vast variety and volume of IoT devices. Currently, Machine Learning (ML) and Deep Learning (DL) techniques are utilized to address the security threats in IoT networks. This manuscript proposes a Cybersecurity through an Attention-based Stacked Autoencoder with a Pelican Optimization Algorithm for the Detection and Mitigation of Attacks (CASAE-POADMA) methodology on an IoT-assisted network. The main purpose of the CASAE-POADMA methodology is to identify and mitigate the presence of cybersecurity attack behavior in the IoT-assisted network. At first, the presented CASAE-POADMA approach utilizes min–max normalization to scale input data into a uniform design. Besides, the greylag goose optimization (GGO) method is employed for the feature selection process. For the detection and mitigation of attack, the presented CASAE-POADMA approach employs the attention-based stacked autoencoder (ASAE) method. Eventually, the hyperparameter tuning of the ASAE method is executed by using pelican optimization algorithm (POA) method. The simulation validation of the CASAE-POADMA approach is verified under a benchmark database. The experimental validation of the CASAE-POADMA approach exhibited a superior accuracy value of 99.50% over existing techniques.

The recent development and adoption of unmanned aerial vehicles (UAVs) is due to its wide variety of applications in public and private sector from parcel delivery to wildlife conservation. The integration of UAVs, 5G, and satellite technologies has prompted telecommunication networks to evolve to provide higher-quality and more stable service to remote areas. However, security concerns with UAVs are growing as UAV nodes are becoming attractive targets for cyberattacks due to enormously growing volumes and poor and weak inbuilt security. In this paper, we propose a UAV- and satellite-based 5G-network security model that can harness machine learning to effectively detect of vulnerabilities and cyberattacks. The solution is divided into two main parts: the model creation for intrusion detection using various machine learning (ML) algorithms and the implementation of ML-based model into terrestrial or satellite gateways. The system identifies various attack types using realistic CSE-CIC IDS-2018 network datasets published by Canadian Establishment for Cybersecurity (CIC). It consists of seven different types of new and contemporary attack types. This paper demonstrates that ML algorithms can be used to classify benign or malicious packets in UAV networks to enhance security. Finally, the tested ML algorithms are compared for effectiveness in terms of accuracy rate, precision, recall, F1-score, and false-negative rate. The decision tree algorithm performed well by obtaining a maximum accuracy rate of 99.99% and a minimum false negative rate of 0% in detecting various attacks as compared to all other types of ML classifiers.

A cyber-physical system (CPS) is a combination of physical system components with cyber capabilities that have a very tight interconnectivity. CPS is a widely used technology in many applications, including electric power systems, communications, and transportation, and healthcare systems. These are critical national infrastructures. Cybersecurity attack is one of the major threats for a CPS because of many reasons, including complexity and interdependencies among various system components, integration of communication, computing, and control technology. Cybersecurity attacks may lead to various risks affecting the critical infrastructure business continuity, including degradation of production and performance, unavailability of critical services, and violation of the regulation. Managing cybersecurity risks is very important to protect CPS. However, risk management is challenging due to the inherent complex and evolving nature of the CPS system and recent attack trends. This paper presents an integrated cybersecurity risk management framework to assess and manage the risks in a proactive manner. Our work follows the existing risk management practice and standard and considers risks from the stakeholder model, cyber, and physical system components along with their dependencies. The approach enables identification of critical CPS assets and assesses the impact of vulnerabilities that affect the assets. It also presents a cybersecurity attack scenario that incorporates a cascading effect of threats and vulnerabilities to the assets. The attack model helps to determine the appropriate risk levels and their corresponding mitigation process. We present a power grid system to illustrate the applicability of our work. The result suggests that risk in a CPS of a critical infrastructure depends mainly on cyber-physical attack scenarios and the context of the organization. The involved risks in the studied context are both from the technical and nontechnical aspects of the CPS.

This paper compares two literature reviews on cybersecurity issues focused on the mature organisations, business and management field, and the embryonic tourism and hospitality area. Hence, we use the general study on the former as a benchmark for the narrower review on the latter, to map the current trends and identify the corresponding gaps. Findings suggest the following topic clusters for future research: (1) machine learning, artificial intelligence, blockchain, big data; (2) fraud and reputation; (3) phishing and social engineering; (4) human security and user education. Este artículo compara dos revisiones bibliográficas sobre cuestiones de ciberseguridad centradas en el ámbito de las empresas y la gestión empresarial, por una parte, y en el sector del turismo y la hostelería por otra. De este modo, se utiliza el estudio general en el ámbito empresarial como punto de referencia para el análisis sobre el sector del turismo, con el fin de trazar las tendencias actuales e identificar las lagunas existentes. Los resultados sugieren los siguientes temas para futuras investigaciones: (1) aprendizaje automático, inteligencia artificial, blockchain y big data; (2) fraude y reputación; (3) phishing e ingeniería social; (4) seguridad y educación.

The convergence of data security and operational efficiency across various sectors, such as manufacturing, industry, logistics, agriculture, healthcare, and internet services, has been significantly enhanced using robotic-driven platforms and protocols. Notably, there has been a notable uptick in sophisticated cyberattacks targeting corporate and industrial robotic systems. These attacks are activated following the integration of the Internet of Things, the Internet, and organizational networks, as industrial units are interconnected. This study has formulated security-oriented criteria-based indicators for cyber-physical systems (CPS), encompassing industrial components and embedded sensors responsible for processing information logs and procedures. In this research, a robust security framework based on attack trees has been introduced, strategically focusing on addressing critical exploitable vulnerabilities rather than attempting to cover all CPS devices comprehensively. The systematic categorization of each physical device and its associated integrated sensors has been accomplished via data from logs and an information repository contained within a sensor index device library.

Nowadays, ransomware is considered one of the most critical cyber-malware categories. In recent years various malware detection and classification approaches have been proposed to analyze and explore malicious software precisely. Malware originators implement innovative techniques to bypass existing security solutions. This paper introduces an efficient End-to-End Ransomware Detection System (E2E-RDS) that comprehensively utilizes existing Ransomware Detection (RD) approaches. E2E-RDS considers reverse engineering the ransomware code to parse its features and extract the important ones for prediction purposes, as in the case of static-based RD. Moreover, E2E-RDS can keep the ransomware in its executable format, convert it to an image, and then analyze it, as in the case of vision-based RD. In the static-based RD approach, the extracted features are forwarded to eight various ML models to test their detection efficiency. In the vision-based RD approach, the binary executable files of the benign and ransomware apps are converted into a 2D visual (color and gray) images. Then, these images are forwarded to 19 different Convolutional Neural Network (CNN) models while exploiting the substantial advantages of Fine-Tuning (FT) and Transfer Learning (TL) processes to differentiate ransomware apps from benign apps. The main benefit of the vision-based approach is that it can efficiently detect and identify ransomware with high accuracy without using data augmentation or complicated feature extraction processes. Extensive simulations and performance analyses using various evaluation metrics for the proposed E2E-RDS were investigated using a newly collected balanced dataset that composes 500 benign and 500 ransomware apps. The obtained outcomes demonstrate that the static-based RD approach using the AB (Ada Boost) model achieved high classification accuracy compared to other examined ML models, which reached 97%. While the vision-based RD approach achieved high classification accuracy, reaching 99.5% for the FT ResNet50 CNN model. It is declared that the vision-based RD approach is more cost-effective, powerful, and efficient in detecting ransomware than the static-based RD approach by avoiding feature engineering processes. Overall, E2E-RDS is a versatile solution for end-to-end ransomware detection that has proven its high efficiency from computational and accuracy perspectives, making it a promising solution for real-time ransomware detection in various systems.

Autonomous vehicles (AVs) rely on a heterogeneous sensor suite of RGB cameras, LiDAR, GPS/IMU, and emerging event-based dynamic vision sensors (DVS) to perceive and navigate complex environments. However, these sensors can be deceived by realistic cyberattacks, undermining safety. In this work, we systematically implement seven attack vectors in the CARLA simulator—salt and pepper noise, event flooding, depth map tampering, LiDAR phantom injection, GPS spoofing, denial of service, and steering bias control—and measure their impact on a state-of-the-art end-to-end driving agent. We then equip each sensor with tailored defenses (e.g., adaptive median filtering for RGB and spatial clustering for DVS) and integrate a unsupervised anomaly detector (EfficientAD from anomalib) trained exclusively on benign data. Our detector achieves clear separation between normal and attacked conditions (mean RGB anomaly scores of 0.00 vs. 0.38; DVS: 0.61 vs. 0.76), yielding over 95% detection accuracy with fewer than 5% false positives. Defense evaluations reveal that GPS spoofing is fully mitigated, whereas RGB- and depth-based attacks still induce 30–45% trajectory drift despite filtering. Notably, our research-focused evaluation of DVS sensors suggests potential intrinsic resilience advantages in high-dynamic-range scenarios, though their asynchronous output necessitates carefully tuned thresholds. These findings underscore the critical role of multi-modal anomaly detection and demonstrate that DVS sensors exhibit greater intrinsic resilience in high-dynamic-range scenarios, suggesting their potential to enhance AV cybersecurity when integrated with conventional sensors.

The smart grid system is one of the key infrastructures required to sustain our future society. It is a complex system that comprises two independent parts: power grids and communication networks. There have been several cyber attacks on smart grid systems in recent years that have caused significant consequences. Therefore, cybersecurity training specific to the smart grid system is essential in order to handle these security issues adequately. Unfortunately, concepts related to automation, ICT, smart grids, and other physical sectors are typically not covered by conventional training and education methods. These cybersecurity experiences can be achieved by conducting training using a smart grid co-simulation, which is the integration of at least two simulation models. However, there has been little effort to research attack simulation tools for smart grids. In this research, we first review the existing research in the field, and then propose a smart grid attack co-simulation framework called GridAttackSim based on the combination of GridLAB-D, ns-3, and FNCS. The proposed architecture allows us to simulate smart grid infrastructure features with various cybersecurity attacks and then visualize their consequences automatically. Furthermore, the simulator not only features a set of built-in attack profiles but also enables scientists and electric utilities interested in improving smart grid security to design new ones. Case studies were conducted to validate the key functionalities of the proposed framework. The simulation results are supported by relevant works in the field, and the system can potentially be deployed for cybersecurity training and research.