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Digitization in healthcare systems, with the wid adoption of Electronic Health Records, connected medical devices, software and systems providing efficient healthcare service delivery and management. On the other hand, the use of these systems has significantly increased cyber threats in the healthcare sector. Vulnerabilities in the existing and legacy systems are one of the key causes for the threats and related risks. Understanding and addressing the threats from the connected medical devices and other parts of the ICT health infrastructure are of paramount importance for ensuring security within the overall healthcare ecosystem. Threat and vulnerability analysis provides an effective way to lower the impact of risks relating to the existing vulnerabilities. However, this is a challenging task due to the availability of massive data which makes it difficult to identify potential patterns of security issues. This paper contributes towards an effective threats and vulnerabilities analysis by adopting Machine Learning models, such as the BERT neural language model and XGBoost, to extract updated information from the Natural Language documents largely available on the web, evaluating at the same time the level of the identified threats and vulnerabilities that can impact on the healthcare system, providing the required information for the most appropriate management of the risk. Experiments were performed based on CS news extracted from the Hacker News website and on Common Vulnerabilities and Exposures (CVE) vulnerability reports. The results demonstrate the effectiveness of the proposed approach, which provides a realistic manner to assess the threats and vulnerabilities from Natural Language texts, allowing adopting it in real-world Healthcare ecosystems.

In this study, an observer based control strategy is proposed for load frequency control (LFC) scheme against cyber‐attack uncertainties. Most of research work focused on detection scheme or delay estimation scheme in presence of cyber‐attack vulnerabilities and paid less attention on design of counteractive robust control scheme for LFC problem. Thus, observer based control scheme is designed here and provides robust performance against unknown input attack uncertainty and communication time‐delay attack uncertainty. The generalized extended state observer (GESO) is used not only for state and disturbance estimation but also for disturbance rejection of the system. The said observer ensures accurate estimation of the actual states leading to convergence of estimation error to zero. So, the observer based linear quadratic regulator (LQR) is used to regulate the closed‐loop damping ratio against cyber‐attack uncertainty. In addition to fast response in terms of settling time and reduced over/undershoots, the proposed control scheme satisfactorily compensates the cyber‐attack uncertainties in power system cyber physical networks and also compared with existing traditional PI and PID controllers. The simulation results demonstrate the robustness in terms of stability and effectiveness in terms of system security with proposed controller when subjected to cyber‐attack uncertainties and load disturbances.

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.

Influence cascades are typically analyzed using a single metric approach, i.e., all influence is measured using one number. However, social influence is not monolithic; different users exercise different influences in different ways, and influence is correlated with the user and content-specific attributes. One such attribute could be whether the action is an initiation of a new post, a contribution to a post, or a sharing of an existing post. In this paper, we present a novel method for tracking these influence relationships over time, which we call influence cascades, and present a visualization technique to better understand these cascades. We investigate these influence patterns within and across online social media platforms using empirical data and comparing to a scale-free network as a null model. Our results show that characteristics of influence cascades and patterns of influence are, in fact, affected by the platform and the community of the users.

Background: Cyber security has turned out to be one of the main challenges of recent years. As the variety of system and application vulnerabilities has increased dramatically in recent years, cyber attackers have managed to penetrate the networks and infrastructures of larger numbers of companies, thus increasing the latter’s exposure to cyber threats. To mitigate this exposure, it is crucial for CISOs to have sufficient training and skills to help them identify how well security controls are managed and whether these controls offer the company sufficient protection against cyber threats, as expected. However, recent literature shows a lack of clarity regarding the manner in which the CISOs’ role and the companies’ investment in their skills should change in view of these developments. Therefore, the aim of this study is to investigate the relationship between the CISOs’ level of cyber security-related preparation to mitigate cyber threats (and specifically, the companies’ attitudes toward investing in such preparation) and the recent evolution of cyber threats. Methods: The study data are based on the following public resources: (1) recent scientific literature; (2) cyber threat-related opinion news articles; and (3) OWASP’s reported list of vulnerabilities. Data analysis was performed using various text mining methods and tools. Results: The study’s findings show that although the implementation of cyber defense tools has gained more serious attention in recent years, CISOs still lack sufficient support from management and sufficient knowledge and skills to mitigate current and new cyber threats. Conclusions: The research outcomes may allow practitioners to examine whether the companies’ level of cyber security controls matches the CISOs’ skills, and whether a comprehensive security education program is required. The present article discusses these findings and their implications.

A strategic phasor measurement unit (PMU) placement scheme is proposed in this work to reduce the cyber vulnerability of the power system against cyber attacks. A multi‐stage PMU placement strategy is developed to alleviate the power system vulnerability against the possible false data injection attacks using the forward dynamic programming to distribute the capital cost of PMUs over a time horizon. An index is also proposed to quantify the vulnerability of the nodes of a grid against false data injection attacks. This index is useful in selecting the optimal set of candidate buses for PMUs placement and prioritising the candidate buses for PMUs placement in a specific deployment stage. The proposed scheme will also ensure the critical observability of the grid in its first stage and increment in observability levels in subsequent stages. Simulation results are provided for IEEE 14‐bus, IEEE 39‐bus, and IEEE 118‐bus test systems, considering the impact of zero injection buses (ZIBs) on optimisation process, to justify the effectiveness of the proposed index and PMUs placement approach.

Malicious software, PC infections, and other unfriendly attacks may all impact a PC organization. Interruption location, which is a functioning guarded instrument, is a basic part of organization security. Conventional interruption recognition frameworks incorporate issues like low accuracy, unfortunate identification, a high level of false positives, and a failure to deal with inventive sorts of interruptions. We present another deep learning-based approach for identifying network safety weaknesses and breaks in digital actual frameworks to address these worries. The proposed worldview analyses discriminative procedures in view of unsupervised and deep learning. To distinguish cyber threats in IoT-driven IICs organizations, we present a generative ill-disposed network. The discoveries show an improvement in exactness, unwavering quality, and productivity in recognizing all types of attacks. On the three informational collections, NSL-KDD, KDDCup99, and UNSW-NB15, the result of notable cutting-edge DL classifiers accomplished the highest true rate (TNR) and highest detection the rate accompanying assaults: Brute Force XXS, Brute Force WEB, DoS_Hulk_Attack, the preparation and testing stages, it likewise guaranteed the privacy and honesty of delicate data having a place with clients and frameworks.

The onset of the COVID-19 pandemic prompted educational institutions to swiftly integrate e-learning software systems, including learning management systems (LMSs), as essential tools for online education. This study aims to probe the inherent security vulnerabilities of three widely utilized e-learning platforms, namely, Moodle, Chamilo, and Ilias, spanning the pre-pandemic, pandemic, and post-pandemic periods. The rapid adoption of these platforms during the pandemic revolutionized online education but also unveiled security risks. This paper delves into these security vulnerabilities, offering insights before, during, and after the pandemic. Through an analysis of existing patches and security measures, areas for improvement are identified. Furthermore, the paper considers emerging cybersecurity technologies and trends, providing comprehensive recommendations to enhance system resilience against evolving cyber threats. The results obtained here can provide educational institutions with a guide for action to enable effective mitigation of e-learning software security vulnerabilities and ensure the continued security and sustainability of online education systems.

The possible applications of communication based on big data have steadily increased in several industries, such as the autonomous vehicle industry, with a corresponding increase in security challenges, including cybersecurity vulnerabilities (CVs). The cybersecurity-related symmetry of big data communication systems used in autonomous vehicles may raise more vulnerabilities in the data communication process between these vehicles and IoT devices. The data involved in the CVs may be encrypted using an asymmetric and symmetric algorithm. Autonomous vehicles with proactive cybersecurity solutions, power-based cyberattacks, and dynamic countermeasures are the modern issues/developments with emerging technology and evolving attacks. Research on big data has been primarily focused on mitigating CVs and minimizing big data breaches using appropriate countermeasures known as security solutions. In the future, CVs in data communication between autonomous vehicles (DCAV), the weaknesses of autonomous vehicular networks (AVN), and cyber threats to network functions form the primary security issues in big data communication, AVN, and DCAV. Therefore, efficient countermeasure models and security algorithms are required to minimize CVs and data breaches. As a technique, policies and rules of CVs with proxy and demilitarized zone (DMZ) servers were combined to enhance the efficiency of the countermeasure. In this study, we propose an information security approach that depends on the increasing energy levels of attacks and CVs by identifying the energy levels of each attack. To show the results of the performance of our proposed countermeasure, CV and energy consumption are compared with different attacks. Thus, the countermeasures can secure big data communication and DCAV using security algorithms related to cybersecurity and effectively prevent CVs and big data breaches during data communication.

In order to protect Cyber-Physical Systems (CPS) against constantly changing cyberattacks, machine learning (ML) algorithms must be integrated. The goal of this survey is to investigate attack-resistant machine learning methods that improve CPS security. The limits of standard techniques are emphasized while discussing notable issues in CPS security. The survey thoroughly explores a range of machine learning methods, such as K-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Deep Neural Networks (DNN), that are utilized in CPS for behavior analysis, anomaly identification, and intrusion detection. We discuss the importance of having solid training data and the difficulties in ML model adaptation to the dynamic nature of CPS situations. We examine the trade-offs between responsiveness and precision as well as the effects of false positives and false negatives on attack detection. This papers aims to provide a quick overview of the strengths, limitations, and future prospects of these algorithms, enabling stakeholders to formulate effective strategies for CPS security.