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Lasisi  \nDepartment of Computer and Information Sciences  \nVirginia Military Institute, USA  \n[LasisiRO@vmi.edu](LasisiRO@vmi.edu)  \nAbstract  \nUsing the United States National Initiative for Cybersecurity Education framework as a guide, we propose CyberReco - Cybersecurity Workforce Readiness Recommender System, an AI/ML model that attempts to address readiness gaps needed to prepare users lacking in some cybersecurity knowledge, skills, and activities to be workforce-ready. CyberReco is built using natural language processing. We present a hierarchical-based framework that is composed of four components including, text preparation and normalization, keywords extraction and processing, similarity scores and skills computation, and a recommender component.  \nIntroduction  \nOur day to day quality of life is affected by various threatsand attacks in the cyber space. This includes, among others, network disruption, loss of vital information, and inability to guarantee privacy control (Dipankar et al. 2010; LI 2018; Lieskovan and Hajn´y 2021) . The emergence of new cyber threats and the continuous rise in cyber attacks on key cyberphysical infrastructure suggest the need to increase the number of experts within the realm of cybersecurity (Lieskovanand Hajn´y 2021; Lasisi et al. 2022) . (Dawson and Thomson 2018) have identify the existence of readiness gap for future cyber workforce development, especially in the nontechnical KSAs and social attributes such as values, team organization, and civic duty. Furthermore,(Justice et al. 2022) stated that “currently, there are 464,420 unfilled cybersecurity jobs and the nature of the growth of these jobs suggests additional skills that are not being addressed in degrees cybersecurity programs.”  \nIn an attempt to address the workforce readiness gap problem identified above, this paper uses the National Initiative for Cybersecurity Education (NICE) framework, and propose a recommender system, tagged, CyberReco - Cybersecurity Workforce Readiness Recommender System. CyberReco is built on a core of artificial intelligence and machine learning module using natural language processing. CyberReco is able to recommend relevant contents to users based on cybersecurity job description of interest. Datasets for the system were scraped from the National Initiative for Cybersecurity Careers and Studies web  \nCopyright © 2023 by the authors. All rights reserved.  \npages. The scraped data were collected into two datasets named, nice framework work roles.csv and nice.json.  \nThe CyberReco System Design  \nCyberReco is composed of four components: text preparation and normalization, keywords extraction and processing, similarity scores and skills computation, and the recommender component. Each component consists of at least a sub component (or module) . Figure 1 shows the block diagram for the CyberReco recommender system.  \nFigure 1: CyberReco recommender system block diagram  \nText Preparation and Normalization: Textual data is unstructured and contains unwanted items such as non-ascii characters, punctuation, html tags, and links. This component prepares, preprocesses, and transforms raw textual data into clean form that machine learning algorithms can accept as input. There are three modules in this component as described below.  \n• Exploratory Data Analysis - this module loads the two datasets (nice framework work roles.csv and nice.json) into dataframes, drops irrelevant columns from thenice framework work roles.csv dataset, and then separates the KSA dictionary in the nice.json dataset into their respective ids and descriptions.  \n• Text preprocessing-this module takes a text such as the work role or job description and completes preprocessing of the text by removing any html tag, expands contractions, for example the word “didn’t” will be changed to did not,“doesn’t” will be changed to does not, etc, removes unnecessary nu","cbCaijOs62tzDPak","https://ap.wps.com/l/cbCaijOs62tzDPak","pdf",107607,"English","# Abstract\n# Introduction\n# CyberReco System Design\n## Text Preparation and Normalization\n## Keywords Extraction and Processing","[{\"question\":\"What problem does CyberReco aim to solve?\",\"answer\":\"CyberReco targets cybersecurity workforce readiness gaps by helping users who lack certain cybersecurity knowledge, skills, and activities become workforce-ready. It focuses on bridging both nontechnical KSA needs and job-relevant capabilities.\"},{\"question\":\"How does CyberReco transform raw text for modeling?\",\"answer\":\"It preprocesses and normalizes unstructured text by removing unwanted elements such as HTML tags and non-ASCII characters, expanding contractions, removing punctuation and stop words, and applying lemmatization after tokenization.\"},{\"question\":\"What are the main components of the CyberReco system?\",\"answer\":\"CyberReco includes four components: text preparation and normalization, keywords extraction and processing, similarity scores and skills computation, and a recommender component that uses job descriptions to suggest relevant content.\"}]","CyberReco - Cybersecurity Workforce Readiness Recommender System | PDF",1789365683]