The importance of image security in the field of medical imaging is challenging. Several research works have been conducted to secure medical healthcare images. Encryption, not risking loss of data, is the right solution for image confidentiality. Due to data size limitations, redundancy, and capacity, traditional encryption techniques cannot be applied directly to e-health data, especially when patient data are transferred over the open channels. Therefore, patients may lose the privacy of data contents since images are different from the text because of their two particular factors of loss of data and confidentiality. Researchers have identified such security threats and have proposed several image encryption techniques to mitigate the security problem. However, the study has found that the existing proposed techniques still face application-specific several security problems. Therefore, this paper presents an efficient, lightweight encryption algorithm to develop a secure image encryption technique for the healthcare industry. The proposed lightweight encryption technique employs two permutation techniques to secure medical images. The proposed technique is analyzed, evaluated, and then compared to conventionally encrypted ones in security and execution time. Numerous test images have been used to determine the performance of the proposed algorithm. Several experiments show that the proposed algorithm for image cryptosystems provides better efficiency than conventional techniques.

Social engineering is an attack on information security for accessing systems or networks. Social engineering attacks occur when victims do not recognize methods, models, and frameworks to prevent them. The current research explains user studies, constructs, evaluation, concepts, frameworks, models, and methods to prevent social engineering attacks. Unfortunately, there is no specific previous research on preventing social engineering attacks that effectively and systematically analyze it. Current prevention methods, models, and frameworks of social engineering attacks include health campaigns, human as security sensor frameworks, user-centric frameworks, and user vulnerability models. The human as a security sensor framework needs guidance that will explore cybersecurity as super-recognizers, likely policing act for a secure system. This paper intends to critically and rigorously review prior literature on the prevention methods, models, and frameworks of social engineering attacks. We conducted a systematic literature review based on Bryman & Bell’s literature review method. We found a new approach in addition to methods, frameworks, models and evaluations to prevent social engineering attacks based on our review, which is using a protocol. We found the protocol to effectively prevent social engineering attacks, such as health campaigns, the vulnerability of social engineering victims, and co-utile protocol, which can manage information sharing on a social network. We present this systematic literature review to recommend ways to prevent social engineering attacks.

Ensuring the security of critical Industrial Internet of Things (IIoT) systems is of utmost importance, with a primary focus on identifying cyber-attacks using Intrusion Detection Systems (IDS). Deep learning (DL) techniques are frequently utilized in the anomaly detection components of IDSs. However, these models often generate high false-positive rates, and their decision-making rationale remains opaque, even to experts. Gaining insights into the reasons behind an IDS’s decision to block a specific packet can aid cybersecurity professionals in assessing the system’s effectiveness and creating more cyber-resilient solutions. In this paper, we offer an explainable ensemble DL-based IDS to improve the transparency and robustness of DL-based IDSs in IIoT networks. The framework incorporates Shapley additive explanations (SHAP) and Local comprehensible-independent Clarifications (LIME) methods to elucidate the decisions made by DL-based IDSs, providing valuable insights to experts responsible for maintaining IIoT network security and developing more cyber-resilient systems. The ToN_IoT dataset was used to evaluate the efficacy of the suggested framework. As a baseline intrusion detection system, the extreme learning machines (ELM) model was implemented and compared with other models. Experiments show the effectiveness of ensemble learning to improve the results.

There has been a persistent requirement for safeguarding documents and the data they contain, either in printed or electronic form. This is because the fabrication and faking of documents is prevalent globally, resulting in significant losses for individuals, societies, and industrial sectors, in addition to national security. Therefore, individuals are concerned about protecting their work and avoiding these unlawful actions. Different techniques, such as steganography, cryptography, and coding, have been deployed to protect valuable information. Steganography is an appropriate method, in which the user is able to conceal a message inside another message (cover media). Most of the research on steganography utilizes cover media, such as videos, images, and sounds. Notably, text steganography is usually not given priority because of the difficulties in identifying redundant bits in a text file. To embed information within a document, its attributes must be changed. These attributes may be non-displayed characters, spaces, resized fonts, or purposeful misspellings scattered throughout the text. However, this would be detectable by an attacker or other third party because of the minor change in the document. To address this issue, it is necessary to change the document in such a manner that the change would not be visible to the eye, but could still be decoded using a computer. In this paper, an overview of existing research in this area is provided. First, we provide basic information about text steganography and its general procedure. Next, three classes of text steganography are explained: statistical and random generation, format-based methodologies, and linguistics. The techniques related to each class are analyzed, and particularly the manner in which a unique strategy is provided for hiding secret data. Furthermore, we review the existing works in the development of approaches and algorithms related to text steganography; this review is not exhaustive, and covers research published from 2016 to 2021. This paper aims to assist fellow researchers by compiling the current methods, challenges, and future directions in this field.

This review describes a cloud-based intelligent power management system that uses analytics as a control signal and processes balance achievement pointer, and describes operator acknowledgments that must be shared quickly, accurately, and safely. The current study aims to introduce a conceptual and systematic structure with three main components: demand power (direct current (DC)-device), power mix between renewable energy (RE) and other power sources, and a cloud-based power optimization intelligent system. These methods and techniques monitor demand power (DC-device), load, and power mix between RE and other power sources. Cloud-based power optimization intelligent systems lead to an optimal power distribution solution that reduces power consumption or costs. Data has been collected from reliable sources such as Science Direct, IEEE Xplore, Scopus, Web of Science, Google Scholar, and PubMed. The overall findings of these studies are visually explained in the proposed conceptual framework through the literature that are considered to be cloud computing based on storing and running the intelligent systems of power management and mixing.