Blockchain networks are based on cryptographic notions that include asymmetric-key encryption, hash functions and consensus protocols. Despite their popularity, mainstream protocols, such as Proof of Work or Proof of Stake still have drawbacks. Efforts to enhance these protocols led to the birth of alternative consensus protocols, catering to specific areas, such as medicine or transportation. These protocols remain relatively unknown despite having unique merits worth investigating. Although past reviews have been published on popular blockchain consensus protocols, they do not include most of these lesser-known protocols. Highlighting these alternative consensus protocols contributes toward the advancement of the state of the art, as they have design features that may be useful to academics, blockchain practitioners and researchers. In this paper, we bridge this gap by providing an overview of alternative consensus protocols proposed within the past 3 years. We evaluate their overall performance based on metrics such as throughput, scalability, security, energy consumption, and finality. In our review, we examine the trade-offs that these consensus protocols have made in their attempts to optimize scalability and performance. To the best of our knowledge, this is the first paper that focuses on these alternative protocols, highlighting their unique features that can be used to develop future consensus protocols.

Although extant literature has thoroughly investigated the incorporation of cloud computing services, examining their influence on sustainable performance, particularly at the organizational level, is insufficient. Consequently, the present research aims to assess the factors that impact the integration of cloud computing within small and medium-sized enterprises (SMEs) and its subsequent effects on environmental, financial, and social performance. The data were collected from 415 SMEs and were analyzed using a hybrid SEM-ANN approach. PLS-SEM results indicate that relative advantage, complexity, compatibility, top management support, cost reduction, and government support significantly affect cloud computing integration. This study also empirically demonstrated that SMEs could improve their financial, environmental, and social performance by integrating cloud computing services. ANN results show that complexity, with a normalized importance (NI) of 89.14%, is ranked the first among other factors affecting cloud computing integration in SMEs. This is followed by cost reduction (NI = 82.67%), government support (NI = 73.37%), compatibility (NI = 70.02%), top management support (NI = 52.43%), and relative advantage (NI = 48.72%). Theoretically, this study goes beyond examining the determinants affecting cloud computing integration by examining their impact on SMEs’ environmental, financial, and social performance in a comprehensive manner. The study also provides several practical implications for policymakers, SME managers, and cloud computing service providers.

The infrastructure of and processes involved in a microgrid electrical system require advanced technology to facilitate connection among its various components in order to provide the intelligence and automation that can benefit users. As a consequence, the microgrid has vulnerabilities that can expose it to a wide range of attacks. If they are not adequately addressed, these vulnerabilities may have a destructive impact on a country’s critical infrastructure and economy. While the impact of exploiting vulnerabilities in them is understood, research on the cybersecurity of microgrids is inadequate. This paper provides a comprehensive review of microgrid cybersecurity. In particular, it (1) reviews the state-of-the-art microgrid electrical systems, communication protocols, standards, and vulnerabilities while highlighting prevalent solutions to cybersecurity-related issues in them; (2) provides recommendations to enhance the security of these systems by segregating layers of the microgrid, and (3) identifies the gap in research in the area, and suggests directions for future work to enhance the cybersecurity of microgrids.

Advanced persistent threats (APTs) pose significant security-related challenges to organizations owing to their sophisticated and persistent nature, and are inimical to the confidentiality, integrity, and availability of organizational information and services. This study systematically reviews the literature on methods of detecting APTs by comprehensively surveying research in the area, identifying gaps in the relevant studies, and proposing directions for future work. The authors provide a detailed analysis of current methods of APT detection that are based on multi-stage attack-related behaviors. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and conducted an extensive search of a variety of databases. A total of 45 studies, encompassing sources from both academia and the industry, were considered in the final analysis. The findings reveal that APTs have the capability to laterally propagate and achieve their objectives by identifying and exploiting existing systemic vulnerabilities. By identifying shortcomings in prevalent methods of APT detection, we propose integrating the multi-stage attack-related behaviors of APTs with the assessment of the presence of vulnerabilities in the network and their susceptibility to being exploited in order to improve the accuracy of their identification. Such an improved approach uses vulnerability scores and probability metrics to determine the probable sequence of targeted nodes, and visualizes the path of APT attacks. This technique of advanced detection enables the early identification of the most likely targets, which, in turn, allows for the implementation of proactive measures to prevent the network from being further compromised. The research here contributes to the literature by highlighting the importance of integrating multi-stage attack-related behaviors, vulnerability assessment, and techniques of visualization for APT detection to enhance the overall security of organizations.

Malware has recently grown exponentially in recent years and poses a serious threat to individual users, corporations, banks, and government agencies. This can be seen from the growth of Advanced Persistent Threats (APTs) that make use of advance and sophisticated malware. With the wide availability of computer-automated tools such as constructors, email flooders, and spoofers. Thus, it is now easy for users who are not technically inclined to create variations in existing malware. Researchers have developed various defense techniques in response to these threats, such as static and dynamic malware analyses. These techniques are ineffective at detecting new malware in the main memory of the computer and otherwise require considerable effort and domain-specific expertise. Moreover, recent techniques of malware detection require a long time for training and occupy a large amount of memory due to their reliance on multiple factors. In this paper, we propose a computer vision-based technique for detecting malware that resides in the main computer memory in which our technique is faster or memory efficient. It works by taking portable executables in a virtual environment to extract memory dump files from the volatile memory and transform them into a particular image format. The computer vision-based contrast-limited adaptive histogram equalization and the wavelet transform are used to improve the contrast of neighboring pixel and to reduce the entropy. We then use the support vector machine, random forest, decision tree, and XGBOOST machine learning classifiers to train the model on the transformed images with dimensions of 112 × 112 and 56 × 56. The proposed technique was able to detect and classify malware with an accuracy rate of 97.01%. Its precision, recall, and F1-score were 97.36%, 95.65%, and 96.36%, respectively. Our finding shows that our technique in preparing dataset with more efficient features to be trained by the Machine Learning classifiers has resulted in significant performance in terms of accuracy, precision, recall, F1-score, speed and memory consumption. The performance has superseded most of the existing techniques in its unique approach.

Neurological disorders affect communication ability, social interaction, and a person’s conduct. Early diagnosis and treatment of ASD during the early stages of a person’s life may result in better outcomes and a higher quality of life for patients. Current methods of diagnosis are based on behavioral observations and interviews, which are subjective, time-consuming, and costly. EEG does not include invasive techniques, and it is a safe and painless way of measuring electrical activity in the brain. EEG signals may reflect neural differences and abnormalities related to ASD and serve as a potential biomarker for diagnosis. Due to the increase in prevalence, there has been an increased need to develop more sensitive and unbiased diagnostic methods for ASD. ML and DL are two sophisticated methods that researchers developed for detecting ASD by doing neural network analyses. The review paper incorporates the analysis of previous studies; more than 200 works have been analyzed from top publishers like Elsevier, IEEE, MDPI, and Springer, specifically related to EEG signal analysis and feature extraction techniques. It considers significant methods for ASD detection, including SVMs, CNN, and other models like KNN, ResNet50, and ANFIS. Other datasets central in these studies are KAU, BCIAUT-P300, and ADOS-2. The performance metrics adopted in this research include accuracy, sensitivity, and specificity. For example, the cubic SVM realized an accuracy of 95.8%, while the CNN models reached 95%. Other models, like ResNet50, achieved 99.39%, while ANFIS reached 98.9%. Sensitivity and specificity also showed varying scores across the methods, between 85 and 100%, indicating the high potential of these approaches in ASD diagnosis. Future studies could pay more attention to dataset representativeness improvements and do the clinical validation of these models for better generalization and relevance toward early diagnosis in ASD.