The smart grid (SG), regarded as the complex cyber-physical ecosystem of infrastructures, orchestrates advanced communication, computation, and control technologies to interact with the physical environment. Due to the high rewards that threats to the grid can realize, adversaries can mount complex cyber-attacks such as advanced persistent threats-based and coordinated attacks to cause operational malfunctions and power outages in the worst scenarios: The latter of which was reflected in the Ukrainian power grid attack. Despite widespread research on smart grid security, the impact of targeted attacks on control and power systems is anecdotal. This article reviews the smart grid security from collaborative factors, emphasizing the situational awareness (SA). Specifically, we propose a threat modeling framework and review the nature of cyber-physical attacks to understand their characteristics and impacts on the smart grid’s control and physical systems. We examine the existing threats detection and defense capabilities, such as intrusion detection systems (IDSs), moving target defense (MTD), and co-simulation techniques, along with discussing the impact of attacks through situational awareness and power system metrics. We discuss the human factor aspects for power system operators in analyzing the impacts of cyber-attacks. Finally, we investigate the research challenges with key research gaps to shed light on future research directions.

Patient care is a key element of healthcare practices. The use of Healthcare IoT could enhance the lives of patients and the quality of services provided by medical practitioners, nurses, clinicians, pharmaceutical companies, and government in general. Wireless healthcare monitoring systems tremendously used in hospitals and other healthcare practices and witnessed as a revolution in the medical field. Nevertheless, security and privacy for interconnected things are mostly overlooked on the Internet of Things paradigm. In the context of healthcare and remote health monitoring, it is crucial a systematic approach for security and privacy measures must be used in device manufacturing, interconnecting things, communication, data handling and storage, and destruction of such devices and data. This article examines the current state of security and privacy of the Internet of Things in the healthcare system, the challenges encountered in implementing security frameworks and advocates security and privacy solutions.

With the evolution of the Internet of Things, mutual sharing of information becomes easy among various smart devices across the globe. In such an environment, smart healthcare also needs to be expedited with an aim to provide remote diagnosis to patients located across the globe. However, as the sensitive information about a patient travels using an open channel (i.e., the Internet), there are chances of misusing this information. Hence, there is a requirement for a strong crypto mechanism for securely processing smart healthcare data. Most of the solutions reported in the literature use heavy computation-based solutions for providing security to smart healthcare data, which may not be suitable in view of less resources for modern smart devices. In this article, we propose a novel lattice-based secure cryptosystem for smart healthcare (LSCSH) in future smart cities. LSCSH uses a lightweight key exchange and an authentication mechanism at several stages. The proposed scheme has been evaluated with respect to the communication and computation costs in comparison to the other competitive schemes. The results obtained prove the efficacy of the proposed scheme in comparison to the existing schemes.

The insider threat has consistently been identified as a key threat to organizations and governments. Understanding the nature of insider threats and the related threat landscape can help in forming mitigation strategies, including non-technical means. In this paper, we survey and highlight challenges associated with the identification and detection of insider threats in both public and private sector organizations, especially those part of a nation’s critical infrastructure. We explore the utility of the cyber kill chain to understand insider threats, as well as understanding the underpinning human behavior and psychological factors. The existing defense techniques are discussed and critically analyzed, and improvements are suggested, in line with the current state-of-the-art cyber security requirements. Finally, open problems related to the insider threat are identified and future research directions are discussed.

The smart grid, as the next generation of the power grid, is characterized by employing many different types of intelligent devices, such as intelligent electronic devices located at substations, smart meters positioned in the home area network, and outdoor field equipment deployed in the fields. In addition, there are various users in the smart grid network, including customers, operators, maintenance personnel, and so on, who use these devices for various purposes. Therefore, a secure and efficient mutual authentication and authorization scheme is needed in the smart grid to prevent various insider and outsider attacks on many different devices. In this paper, we propose an authentication and authorization scheme for mitigating outsider and insider threats in the smart grid by verifying the user authorization and performing the user authentication together whenever a user accesses the devices. The proposed scheme computes each user role dynamically using an attribute-based access control and verifies the identity of the user together with the device. Security and performance analysis show that the proposed scheme resists various insider as well as outsider attacks, and is more efficient in terms of communication and computation costs in comparison with the existing schemes. The correctness of the proposed scheme is also proved using BAN-Logic and Proverif.

Capturing images has been increasingly popular in recent years, owing to the widespread availability of cameras. Images are essential in our daily lives because they contain a wealth of information, and it is often required to enhance images to obtain additional information. A variety of tools are available to improve image quality; nevertheless, they are also frequently used to falsify images, resulting in the spread of misinformation. This increases the severity and frequency of image forgeries, which is now a major source of concern. Numerous traditional techniques have been developed over time to detect image forgeries. In recent years, convolutional neural networks (CNNs) have received much attention, and CNN has also influenced the field of image forgery detection. However, most image forgery techniques based on CNN that exist in the literature are limited to detecting a specific type of forgery (either image splicing or copy-move). As a result, a technique capable of efficiently and accurately detecting the presence of unseen forgeries in an image is required. In this paper, we introduce a robust deep learning based system for identifying image forgeries in the context of double image compression. The difference between an image’s original and recompressed versions is used to train our model. The proposed model is lightweight, and its performance demonstrates that it is faster than state-of-the-art approaches. The experiment results are encouraging, with an overall validation accuracy of 92.23%.