This document provides a guide for preparing articles for IEEE Transactions, Journals, and Letters. Use this document as a template if you are using MicrosoftWord. Otherwise, use this as an instruction set. The electronic file of your article will be formatted further at IEEE. Titles should be written in uppercase and lowercase letters, not all uppercase. Avoid writing long formulas with subscripts in the title; short formulas that identify the elements are fine (e.g., "Nd–Fe–B"). Do not write “(Invited)” in the title. Full names of authors are preferred in the author field but are not required. Put a space between authors’ initials. ORCIDs can be provided here as well. In the title, all variables should appear lightface italic; numbers and units will remain bold. Abstracts must be a single paragraph. In order for an Abstract to be effective when displayed in IEEEXploreas well as through indexing services such as Compendex, INSPEC, Medline, ProQuest, and Web of Science, it must be an accurate, stand-alone reflection of the contents of the article. They shall not contain displayed mathematical equations, numbered reference citations, nor footnotes. They should include three or four different keywords or phrases, as this will help readers to find it. It is important to avoid over-repetition of such phrases as this can result in a page being rejected by search engines. Ensure that your abstract reads well and is grammatically correct.

The increasingly advanced forms of cyber-attacks have highlighted the importance of advanced threat hunting as a necessary skillset. The current research examines the effectiveness of using Elasticsearch, Kibana, and Lucene for an intelligence-driven threat hunting to identify attack infrastructure or a Command & Control (C2) server. By aggregating all system traffic logs and security artifacts into a single data lake/warehouse, organizations are able to leverage centralized analysis of information from different sources on a corporate scale. Utilizing Kibana’s ability to perform network and log analysis, using Lucene’s rich syntax to make sophisticated queries will empower individuals to make valuable findings from log and network traffic logs that identify behaviours and patterns typical of C2 activities. A novel intelligence-based threat hunting approach is presented here that utilizes Elasticsearch, with domain-specific language additions to refine search queries and investigate for C2 related activity. A detailed analysis of the research based on real-world datasets is conducted to evaluation the threat hunting framework’s abilities in detecting C2 servers and minimize true/false positives in relation to organizational security concerns.

Abstract Following the fourth industrial revolution and subsequent developments in information and communication technology, applying intelligent techniques in microgrid is gaining popularity in academia and business worldwide. A significant amount of data is continuously generated by the widespread use of internet of things (IoT) technologies and sensor networks in microgrids. This data includes essential information to progress the performance of microgrids. This paper includes a comprehensive review of IoT, cloud computing, big data, artificial intelligence, machine learning, blockchain in microgrid and the concepts of digital twin and metaverse and their applications. By aiding in the design, operation management, and maintenance of microgrids, these methods offer a potent tool for managing the massive historical data and real‐time data stream in a proficient and protected way. They also facilitate microgrids operation. Contextual awareness, security, and resilient operation are important for microgrids, therefore their possible improvement in light of these intelligent approaches is comprehensively examined. A theoretical implementation for managing robust operation of microgrids is then described. The discussion of recommendations in microgrids concludes.

Abstract Due to the realization of 5G technology, the Internet of things (IoT) has made remarkable advancements in recent years. However, security along with data balancing issues is proffered owing to IoT data's growth. The universally unique identifier short input pseudo‐random (SiP) hash‐based elliptic curve cryptography (UUDIS‐ECC) centred secure data transfer (DT) and linear scaling Rock Hyraxes swarm‐based convolutional neural network (LSRHS‐CNN) centred 5G IoT data balancing are proposed here to address those issues. Authentication, destination selection, validation, secure DT, and also load balancing are the proposed method's five phases. Initially, during the registration phase, the Length Nano ID (LNanoID) is created in the authentication. The user is permitted to further communicate if the LNanoID is matched with the already saved LNanoID. The destination is selected if the user is authorized. Utilizing the sender and the receiver's public key, the hash code is produced in the validation centre by the SiP hash function after destination selection. After that, by employing the UUDIS‐ECC algorithm, the IoT data is safely transmitted towards the destination. The 5G IoT data is balanced by using the LSRHS‐CNN algorithm during DT. Superior results are attained by the proposed methods analogized to existing research methods.