Metaverse and Cybersecurity

Keep Every Student Safe Online

Mikyung Shin, PhD

West Texas A&M University

Assistant Professor, Dept. of Education

  @mshin_77       mshin77.net

Agenda

• Metaverse vs Cybersecurity Trends
  
• What is Metaverse?
  
• History of Metaverse
  
• Technologies Used in the Metaverse
  
• Cybersecurity Issues in the Metaverse
  
• News Article Analysis

Recorded Video:

Source: Office of Elementary & Secondary Education

Metaverse vs Cybersecurity Trends

Source: Google Trend

What is Metaverse?

• Meta (Beyond) + Verse (Universe)
• A network of 3D virtual worlds which are connected.
• Provide opportunities to connect and communicate with others.
• Feel like everyone is physically present with others while engaging in virtual experiences
• Digitally represent ourselves through an avatar (who we are, who we want to be)
• Establish interoperability (allow individuals to take things with them as they move around in the virtual spaces)

Source: TechRepublic

History of the Metaverse

Source: Ziaul (March, 2022)

Web 3.0, Metaverse Standards https://metaverse-standards.org/

Technologies Used in the Metaverse

Source: Gupta et al. (2023)

Augmented Reality (AR)

• Combine physical and digital worlds

• Overlay on the physical learning environment

• Adapt to the circumstances where it is found

• Interact with the physical elements found around in three dimensions

• Example: Face Filter App, QR code, Google Translate, and Google Maps

Source: GeoGebra

Virtual Reality (VR)

• Use technology to create a fully simulated environment
• Fully immersed with the use of headset
• Entire experiences are controlled by devices (e.g., head mounted display)
• Without AR or VR hardware: CoSpaces, ThingLink, Avantis World, Roblox and Minecraft

Source: https://builtin.com

Mixed Reality (MR)

• Blend elements of both AR and VR
• Provide a mixture of digital and real-world environments
• Use gestures to move digital content around on a virtual whiteboard
• Interactive with holographic objects
• Example: Immersive learning

Source: Microsoft Mesh

Artificial Intelligence (AI)

• Used extensively in face and speech recognition, virtual assistance (chatbots)

• Multilingual accessibility (translate content in real time) in the global environment

• Ready Player Me for a customized avatar

Game Engines

• Unreal & Unity3D
• Unity 3D Engine: Expo Dubai Xplorer and Hydrous
• Unreal Engine: Kid-friendly metaverse by Epic Games and Lego team

Source: Lepitak (November, 2022)

Blockchain, NFT, and Digital Wallet

• Blockchain: Technology that securely stores electronic information and provides proof of ownership
• NFT: Nonfungible tokens; unique digital assets representing real-world object/asset (e.g., photos, music)
• Digital wallet: Tool to securely save payment information (digital currency, [encrypted] cryptocurrency)

Source: Meta

South Korea’s Innovation Toward the Metaverse

Source: Google Trend

Online Classes in South Korea as a Responding to COVID

Phased Online-Learning 

Source: Ministry of Education, Republic of Korea

Metaverse Case: “South Korea launches online metaverse replica of capital city Seoul to improve public services”

• South Korean people and major companies (e.g., GS shop, home shopping) have integrated the metaverse into their everyday lives.

• City government promotes the metaverse ecosystem and ethical principles

Source: Forkast News

Cybersecurity Issues in the Metaverse

• Naver News: South Korean online platform operated by the Naver Corporation and launched in 1998 (news service, 2000-present, available open news search, 1990-present)

• Big KINDS: Integrated database for most major newspapers published in South Korea 1990-present

Text Mining of Naver News

• Search term: 메타버스 (Metaverse)
• Web scrapping using R (Naver API is available)
• Sentiment analysis and word network
• 10,677 after removing duplicates (in Jan. to Apr. 2023)
• 9,525 news after removing news less than 500 characters (n = 1,152
• min character = 500, max character = 13,634 per news

Pre-Process and Tokenize Texts (News Articles)

• R version 4.3.0

• Among at least 30 occurrences within each news

• At least .12 of correlations with “Cybersecurity”

• library(tidytext): unnest_tokens() to transform text data into a “tidy” format

• library(KoNLP): SimplePos09() to tokenize each into morphemes (the smallest meaningful unit)

• library(widyr): pairwise_cor() to find correlations of words associated with “Cybersecurity”

Word Network Analysis (“Cybersecurity”)

Code

suppressPackageStartupMessages({
  library(dplyr)
  library(tidygraph)
  library(ggraph)
  })

filter <- read.csv("https://raw.githubusercontent.com/mshin77/datasets/main/cybersecurity-workshop/filter.csv")

graph_meta <- filter %>%
    as_tbl_graph(directed = TRUE) %>%
    mutate(centrality = centrality_degree(mode = "out") / (44-1)) 

set.seed(2023)
layout_meta <- create_layout(graph_meta, layout = 'fr')

network_meta <- layout_meta %>% 
  ggraph() + 
  geom_edge_link(aes(edge_alpha = correlation, edge_width = correlation), alpha = 0.5, edge_colour = "skyblue") + 
  geom_node_point(aes(colour = centrality), size = 3) +
  geom_node_text(aes(label = name), size = 4, repel = TRUE) +
  scale_color_continuous(guide = 'legend', high = "#fa956b", low = "#ebc4b5") +
  theme_void(base_size = 6) +
  theme(legend.title=element_text(size = 11),
        legend.text=element_text(size = 11),
        legend.position = "right")

network_meta_g <- ggplot_build(network_meta)
network_meta_g$data[[3]] <- network_meta_g$data[[3]] %>%
  mutate(colour = if_else(label %in% c("Cybersecurity"), "red", "black"))
network_meta_gg <- ggplot_gtable(network_meta_g)

plot(network_meta_gg) 

Positive and Negative Words (“Disability” + “Cybersecurity”)

Code

suppressPackageStartupMessages({
  library(dplyr)
  library(ggplot2)
  library(tidytext)
})

sentiment_20 <- read.csv("https://raw.githubusercontent.com/mshin77/datasets/main/cybersecurity-workshop/sentiment_20.csv")

senti_plot <- sentiment_20 %>%
  mutate(word = reorder_within(word, n, sentiment)) %>%
  ggplot(aes(x = word, y = n, fill = sentiment)) +
  geom_col() +
  coord_flip() +
  geom_text(aes(label = n), hjust = -0.3) +
  facet_wrap(~ sentiment, scales = "free") +
  scale_x_reordered() +
  scale_y_continuous(expand = expansion(mult = c(0.05, 0.15))) +  
  labs(x = NULL) +
  theme_bw(base_size = 14) +
  theme(legend.title=element_text(size = 11),
        legend.text=element_text(size = 11),
        legend.position = "right")

senti_plot

Conclusion

• Advanced technology and its environments (e.g., metaverse) can support learners’ motivation and learning opportunities in various settings.

• Increased online and virtual opportunities and communications can also create new and unexpected security issues.

• We need to be proactive in utilizing and creating data and information.

• We need to consider limited access and resources for underrepresented groups.