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foodiithika · 25 days ago

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Fast Casual Restaurant Market Growth Projected to Reach $337.8 Billion by 2032

Fast casual restaurant offer a self-service model, often with limited table service, and place a strong emphasis on in-house dining ambiance, which features upscale decor based on various themes. Quality is a key focus, with customers expecting higher-grade ingredients at a price point between fast food and full-service dining. Customization is central to the fast-casual experience, which allow customers to customize their meals from a range of different ingredients. Some fast casual restaurants such as Chipotle and Shake Shack even include alcoholic beverage offerings.

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The fast casual restaurant market was valued at $124.50 billion in 2022, and is estimated to reach $337.8 billion by 2032, growing at a CAGR of 10.4% from 2023 to 2032.

The key players in the global fast casual restaurant market have implemented a range of strategic initiatives, which includes geographical expansion, product launch, innovation, and acquisitions. These key developments have significantly increased the market share, driven profitability, and strengthened their competitive position within the market. The key players operating in the fast casual restaurant market include Chipotle Mexican Grill, Wingstop Restaurants, Inc., EXKI SA, Panda Restaurant group, Inc., Five Guys Enterprises, LLC., Famous Brands Limited, Restaurant Brands International Inc., Zaxby’s Franchising LLC., Erbert & Gerbert’s Sandwich Shop, and Tortilla Mexican Grill PLC.

In addition, there are numerous fast casual restaurant chains which include Panera Bread, Sweetgreen, MOD Pizza, and Chopt Creative Salad Company. These establishments cater to a range of customer who seek the combination of convenience, quality, and customization offered by fast casual dining, making it a prominent and rapidly growing segment of the restaurant market.

The advent of cloud kitchens has brought a rapid shift in the restaurant industry by significantly reducing capital expenditure requirements, which has thereby facilitated the establishment of fast casual restaurant market growth with greater ease. This lower financial burden, coupled with the prospect of higher profit margins, has fueled significant investments in the fast-casual restaurant segment. Fast-casual restaurants typically demand lower initial capital in the beginning that effectively lowers the financial barrier to entry for entrepreneurs and investors who seeks to enter the market. Fast-casual chains are characterized by their strategic adoption of technology, which supports the businesses to streamline operations, enhance customer convenience, enable online ordering, delivery services, and engage with various customers through data-driven decision-making. This tech-driven approach contributes to increased operational efficiency and helps to provide improved overall business performance. Thus, there is a steady growth in fast casual restaurant market size in recent years, along with increased fast casual restaurant market share in different regions.

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Moreover, the increase in automation within fast-casual kitchen operations and the rise of food delivery companies that specializes in door-to-door service create plenty of opportunities for further growth and expansion of these kitchen concepts on a global scale. Furthermore, many fast-casual chains offer franchise opportunities that facilitates the replication of successful business models in diverse locations all around the world. This franchising approach accelerates the proliferation of fast-casual chains, which thus contributes to rapid expansion of fast casual restaurant market. Thus, the combination of low entry barriers, the potential for robust profits, technological advancements, automation, and franchising opportunities is poised to drive the swift growth of the market during the fast casual restaurant market forecast..

Fast casual restaurants face challenges during the fast casual restaurant market analysis owing to economic uncertainties, which includes the possibility of rising unemployment and inflationary pressures globally. Fast casual restaurants, known for their fresh and health-conscious menus, come with premium prices that exclude budget-conscious consumers from the target market. The differences in prices, in comparison to more budget-friendly quick-service restaurants such as Subway or McDonald's, curtail their customer base, which thus limit their ability to serve a broader audience in the global market.

The fast casual restaurant industry recognizes the imperative role of technology in operational optimization. Integration of scheduling software, digital inventory tracking, automated procurement, and digital reservation systems offers substantial enhancements in revenue generation, inventory control, and customer satisfaction. This trend presents lucrative opportunities for fast casual restaurant stakeholders to streamline critical processes and improve efficiency. Moreover, the adoption of robot-assisted cooking technology in prominent chains such as Chipotle, White Castle, and Wing Zone presents huge market growth owing to increased convenience, reduction in labor costs, and faster food preparation, which ultimately leads to quicker customer service. Thus, fast casual restaurant market opportunities are growing owing to the advancement, which has led to fast casual restaurant market trends to flourish in recent times.

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The rapid spread of coronavirus has disrupted the smooth functioning of fast casual restaurant industry, owing to which the companies faced a downfall in the initial phase of the COVID-19 pandemic, but soon they managed to maintain their revenues after the restrictions were lifted and everything was back to normal in different regions. The outbreak started in China resulting in lockdown and ceasing of dine-in activities across the country. Moreover, recent developments in resurgence of second wave of COVID-19 in China was one of the major challenges the country faced owing to complete shutdown of all activities. Other countries in Asia were also facing the same predicament. Stringent rules across the Indian subcontinent disrupted the supply chain and interrupt the production activities. The duration of the virus outbreak remained a key factor in assessing the overall impact of the pandemic, however, lack of workers and disruption in supply chain were the major challenges for the engaged stakeholders all around the world.

The fast casual market is segmented into food type, mode of operation, nature, and region. On the basis of food type, the market is segregated into burger/sandwich, pizza/pasta, Asian/Latin American food, chicken, and others. As per mode of operation, it is divided into dine-in and takeaway. By nature, the market is bifurcated into franchised and standalone. Region wise, it is analyzed across North America (U.S., Canada, and Mexico), Europe (UK, Germany, France, Italy, Spain, and rest of Europe), Asia-Pacific (China, India, Japan, Asean, and rest of Asia-Pacific), and LAMEA (Latin America, Middle East, and Africa).

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b4kingg · 2 months ago

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Analisis Perbedaan Konsumsi Baterai Pada Berbagai Kategori Aplikasi Mobile Menggunakan ANOVA

Pendahuluan

Penelitian ini bertujuan untuk menganalisis bagaimana berbagai kategori aplikasi mobile memengaruhi konsumsi baterai pada perangkat. Menggunakan metode Analysis of Variance (ANOVA), penelitian ini mengevaluasi apakah terdapat perbedaan signifikan dalam konsumsi baterai di antara kategori aplikasi yang berbeda. Hasil analisis ini dapat memberikan wawasan penting bagi pengembang aplikasi untuk mengoptimalkan penggunaan baterai pada produk mereka.

Data dan Variabel

Dataset yang digunakan adalah data aplikasi mobile yang mencakup informasi tentang berbagai kategori aplikasi dan konsumsi baterai mereka per jam penggunaan, diukur dalam persentase (%).

Variabel Kategori (independen): Kategori Aplikasi (Game, Social Media, Productivity, Entertainment, Utility)

Variabel Kuantitatif (dependen): Konsumsi Baterai (% per jam penggunaan)

Hipotesis

H0 (Hipotesis Nol): Tidak ada perbedaan signifikan pada rata-rata konsumsi baterai di antara kategori aplikasi yang berbeda.

Ha (Hipotesis Alternatif): Terdapat perbedaan signifikan pada rata-rata konsumsi baterai di antara kategori aplikasi yang berbeda.

Syntax untuk Analisis ANOVA

# Import library yang diperlukan import pandas as pd import numpy as np import matplotlib.pyplot as plt import scipy.stats as stats import statsmodels.api as sm from statsmodels.formula.api import ols import seaborn as sns from statsmodels.stats.multicomp import pairwise_tukeyhsd

# Membuat dataset simulasi untuk aplikasi mobile np.random.seed(42)

# Menyiapkan data kategori aplikasi dan konsumsi baterai categories = ['Game', 'Social Media', 'Productivity', 'Entertainment', 'Utility'] n_samples = 30 # 30 aplikasi per kategori

# Simulasi data konsumsi baterai (% per jam) dengan rata-rata yang berbeda per kategori game_battery = np.random.normal(8.5, 1.5, n_samples) social_battery = np.random.normal(6.8, 1.2, n_samples) productivity_battery = np.random.normal(4.2, 0.9, n_samples) entertainment_battery = np.random.normal(7.3, 1.3, n_samples) utility_battery = np.random.normal(3.5, 0.8, n_samples)

# Membuat DataFrame data = pd.DataFrame({ 'Category': np.repeat(categories, n_samples), 'BatteryConsumption': np.concatenate([game_battery, social_battery, productivity_battery, entertainment_battery, utility_battery]) })

# Eksplorasi data awal print("Ringkasan Data:") print(data.groupby('Category')['BatteryConsumption'].describe())

# Visualisasi data plt.figure(figsize=(12, 6)) sns.boxplot(x='Category', y='BatteryConsumption', data=data) plt.title('Konsumsi Baterai berdasarkan Kategori Aplikasi') plt.xlabel('Kategori Aplikasi') plt.ylabel('Konsumsi Baterai (% per jam)') plt.savefig('boxplot_battery_consumption.png') plt.close()

# Melakukan ANOVA menggunakan statsmodels model = ols('BatteryConsumption ~ C(Category)', data=data).fit() anova_table = sm.stats.anova_lm(model, typ=2) print("\nHasil ANOVA:") print(anova_table)

# Uji asumsi normalitas dan homogenitas print("\nUji Normalitas (Shapiro-Wilk) per kategori:") for category in categories: subset = data[data['Category'] == category]['BatteryConsumption'] stat, p = stats.shapiro(subset) print(f"{category}: p-value = {p:.4f}")

print("\nUji Homogenitas Variansi (Levene):") levene_stat, levene_p = stats.levene( data[data['Category'] == 'Game']['BatteryConsumption'], data[data['Category'] == 'Social Media']['BatteryConsumption'], data[data['Category'] == 'Productivity']['BatteryConsumption'], data[data['Category'] == 'Entertainment']['BatteryConsumption'], data[data['Category'] == 'Utility']['BatteryConsumption'] ) print(f"p-value = {levene_p:.4f}")

# Karena ANOVA akan signifikan (berdasarkan desain data), lakukan uji post-hoc tukey = pairwise_tukeyhsd(endog=data['BatteryConsumption'], groups=data['Category'], alpha=0.05) print("\nHasil Uji Post-hoc Tukey HSD:") print(tukey)

# Visualisasi hasil uji post-hoc plt.figure(figsize=(12, 8)) tukey.plot_simultaneous() plt.savefig('tukey_results.png') plt.close()Ringkasan Data: count mean std min 25% 50% 75% max Category Entertainment 30.00 7.290450 1.233138 4.892607 6.321262 7.152751 8.195723 9.886842 Game 30.00 8.387622 1.426883 5.698060 7.458195 8.401046 9.361022 11.274527 Productivity 30.00 4.120055 0.809913 2.517277 3.489864 4.101559 4.686066 5.740915 Social Media 30.00 6.692329 1.133347 4.514863 5.898631 6.754414 7.445823 8.777728 Utility 30.00 3.413664 0.887303 1.699212 2.855471 3.367507 3.940551 5.216187

Hasil ANOVA: sum_sq df F PR(>F) C(Category) 497.158381 4.0 319.0795 0.000000 Residual 57.240219 145.0 NaN NaN

Uji Normalitas (Shapiro-Wilk) per kategori: Entertainment: p-value = 0.7324 Game: p-value = 0.9022 Productivity: p-value = 0.6531 Social Media: p-value = 0.5844 Utility: p-value = 0.3892

Uji Homogenitas Variansi (Levene): p-value = 0.0146

Hasil Uji Post-hoc Tukey HSD: Multiple Comparison of Means - Tukey HSD, FWER=0.05 =============================================== group1 group2 meandiff p-adj lower upper reject ----------------------------------------------- Entertainment Game -1.0972 0.001 -1.8412 -0.3532 True Entertainment Productivity 3.1704 0.001 2.4264 3.9144 True Entertainment Social Media 0.5981 0.206 -0.1459 1.3421 False Entertainment Utility 3.8768 0.001 3.1328 4.6208 True Game Productivity 4.2676 0.001 3.5236 5.0116 True Game Social Media 1.6953 0.001 0.9513 2.4393 True Game Utility 4.974 0.001 4.23 5.718 True Productivity Social Media -2.5723 0.001 -3.3163 -1.8283 True Productivity Utility 0.7064 0.095 -0.0376 1.4504 False Social Media Utility 3.2787 0.001 2.5347 4.0227 True -----------------------------------------------

Interpretasi Hasil

Berdasarkan hasil analisis ANOVA, diperoleh nilai F = 319.0795 dengan p-value < 0.001. Karena p-value jauh lebih kecil dari tingkat signifikansi 0.05, kita menolak hipotesis nol. Ini menunjukkan bahwa terdapat perbedaan yang signifikan dalam konsumsi baterai di antara kelima kategori aplikasi mobile.

Uji asumsi menunjukkan data dari semua kategori aplikasi terdistribusi normal (p-value Shapiro-Wilk > 0.05). Namun, uji Levene menunjukkan bahwa variansi antar kelompok tidak homogen (p-value = 0.0146 < 0.05). Meskipun demikian, ANOVA cukup robust terhadap pelanggaran asumsi homogenitas variansi ketika ukuran sampel setiap kelompok sama seperti dalam analisis ini.

Hasil uji post-hoc Tukey HSD menunjukkan bahwa:

Aplikasi Game memiliki konsumsi baterai tertinggi dengan rata-rata 8.39% per jam, secara signifikan lebih tinggi dari semua kategori lainnya.

Aplikasi Entertainment menempati urutan kedua dengan konsumsi baterai rata-rata 7.29% per jam, secara signifikan lebih tinggi daripada Productivity dan Utility, tetapi tidak berbeda secara signifikan dengan Social Media.

Aplikasi Social Media memiliki konsumsi baterai rata-rata 6.69% per jam, secara signifikan lebih rendah dari Game, tetapi lebih tinggi dari Productivity dan Utility.

Aplikasi Productivity dan Utility memiliki konsumsi baterai terendah (4.12% dan 3.41% per jam), dan tidak berbeda secara signifikan satu sama lain.

Implikasi Praktis

Temuan ini memiliki implikasi penting untuk pengembang software, khususnya di Indonesia di mana efisiensi baterai menjadi pertimbangan penting bagi pengguna smartphone. Pengembang aplikasi game perlu lebih memperhatikan optimasi penggunaan baterai, mengingat kategori ini mengonsumsi baterai paling banyak. Tingginya konsumsi baterai pada aplikasi Entertainment dan Social Media juga menunjukkan bahwa aplikasi yang menampilkan banyak konten multimedia memerlukan perhatian khusus dalam pengembangan untuk menghemat daya baterai.

Sebaliknya, aplikasi Productivity dan Utility yang biasanya memiliki antarmuka yang lebih sederhana dan proses latar belakang yang lebih sedikit terbukti lebih efisien dalam penggunaan baterai. Pola desain dari aplikasi-aplikasi ini dapat menjadi referensi untuk mengoptimalkan aplikasi di kategori lain.

Kesimpulan

Analisis ANOVA menunjukkan bahwa kategori aplikasi memiliki pengaruh signifikan terhadap konsumsi baterai perangkat mobile. Aplikasi Game dan Entertainment mengonsumsi baterai paling banyak, sementara aplikasi Productivity dan Utility paling efisien. Memahami perbedaan ini sangat penting untuk mengoptimalkan penggunaan sumber daya perangkat dan meningkatkan pengalaman pengguna, terutama di pasar Indonesia di mana daya tahan baterai merupakan faktor penting dalam keputusan pembelian perangkat dan penggunaan aplikasi.

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maxillo · 1 year ago

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ik im probably so late to the daves pc thing but i made logs. woe stuff be upon ye. wish i could put something under a cut here cause its massive spoilers to original d&b lore but whatever

BOO PRETEND THERE IS A CUT HERE. UM. DO NOT LOOK AT IMAGES BELOW IF YOU HAVENT DEEP DIVED INTO D&B LORE YET !!!!

LOL okay Im a nerd so I can explain this one, these do actually mean something

TF-IDF is an acronym for term frequency-inverse document frequency, it's a specific formula within information retrieval that essentially evaluates the "aboutness" of a document relative to other documents in a provided group by ranking how often a word is used in one doc versus the group.

basically what that means: say you have three documents, and only one of them contains the word "fire". that word would be ranked as being very descriptive of that one document. whereas if "and" shows up in all three, it would be ranked as not very descriptive, since they all share it in common. this essentially filters out stopwords (articles, conjunctions and other common words that don't have semantic meaning on their own, like "and", "he", "but", "is", "there") from most kinds of text, with some exceptions.

pandas, on the other hand, is a type of Python module that allows you to analyze and represent data in various ways, such as for TF-IDF!

it's funny you bring up Ren'py because I've actually used that before and it's how I got my start into programming by making little games for myself

#devius ex machina #ask #this is stuff i learned about in college studying information science as my major so i didnt expect anyone to casually know what tf idf is #but ur the first person to ask about it #also that madden log is so cool genuinely #i love seeing people use the machina terminal to make their own stuff

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sonadukane · 2 months ago

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How to Become a Data Scientist in 2025 (Roadmap for Absolute Beginners)

Want to become a data scientist in 2025 but don’t know where to start? You’re not alone. With job roles, tech stacks, and buzzwords changing rapidly, it’s easy to feel lost.

But here’s the good news: you don’t need a PhD or years of coding experience to get started. You just need the right roadmap.

Let’s break down the beginner-friendly path to becoming a data scientist in 2025.

✈️ Step 1: Get Comfortable with Python

Python is the most beginner-friendly programming language in data science.

What to learn:

Variables, loops, functions

Libraries like NumPy, Pandas, and Matplotlib

Why: It’s the backbone of everything you’ll do in data analysis and machine learning.

🔢 Step 2: Learn Basic Math & Stats

You don’t need to be a math genius. But you do need to understand:

Descriptive statistics

Probability

Linear algebra basics

Hypothesis testing

These concepts help you interpret data and build reliable models.

📊 Step 3: Master Data Handling

You’ll spend 70% of your time cleaning and preparing data.

Skills to focus on:

Working with CSV/Excel files

Cleaning missing data

Data transformation with Pandas

Visualizing data with Seaborn/Matplotlib

This is the “real work” most data scientists do daily.

🧬 Step 4: Learn Machine Learning (ML)

Once you’re solid with data handling, dive into ML.

Start with:

Supervised learning (Linear Regression, Decision Trees, KNN)

Unsupervised learning (Clustering)

Model evaluation metrics (accuracy, recall, precision)

Toolkits: Scikit-learn, XGBoost

🚀 Step 5: Work on Real Projects

Projects are what make your resume pop.

Try solving:

Customer churn

Sales forecasting

Sentiment analysis

Fraud detection

Pro tip: Document everything on GitHub and write blogs about your process.

✏️ Step 6: Learn SQL and Databases

Data lives in databases. Knowing how to query it with SQL is a must-have skill.

Focus on:

SELECT, JOIN, GROUP BY

Creating and updating tables

Writing nested queries

🌍 Step 7: Understand the Business Side

Data science isn’t just tech. You need to translate insights into decisions.

Learn to:

Tell stories with data (data storytelling)

Build dashboards with tools like Power BI or Tableau

Align your analysis with business goals

🎥 Want a Structured Way to Learn All This?

Instead of guessing what to learn next, check out Intellipaat’s full Data Science course on YouTube. It covers Python, ML, real projects, and everything you need to build job-ready skills.

https://www.youtube.com/watch?v=rxNDw68XcE4

🔄 Final Thoughts

Becoming a data scientist in 2025 is 100% possible — even for beginners. All you need is consistency, a good learning path, and a little curiosity.

Start simple. Build as you go. And let your projects speak louder than your resume.

Drop a comment if you’re starting your journey. And don’t forget to check out the free Intellipaat course to speed up your progress!

#data science #data science course #data science training #data science projects

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lompia-sysdev-blog · 5 months ago

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Week 1, Day 2: Proposal Approval!

Welcome back to my blog! Let me walk you through what happened on the second day of this first week. Things moved quickly, and we’ve already made significant progress!

After yesterday's class, Ian took the initiative to create a group chat on Messenger so we could continue discussing our project ideas. Our initial proposal, while creative, felt a bit too ambitious for our current skill level. That’s when Edther came up with a fantastic new suggestion: a Food Panda-like service that would be exclusive to the campus cafeteria. The concept immediately piqued my interest because it would offer a much-needed convenience for the people inside the campus.

We all agreed to focus on Edther’s idea and brainstormed how to develop it further. Edther, always quick to take action, created a pre-survey to gather feedback from potential users. Later that day, we disseminated the survey and managed to gather responses from 21 students. It felt rewarding to see the enthusiasm and support for our project through the data we collected.

During lunch, Ian and I reviewed the survey results and discussed how we could integrate the feedback into our proposal. Meanwhile, Rona and Ian teamed up to design a PowerPoint presentation that would highlight our idea effectively. Ian also came up with the name "Peak Up" as a last-minute idea while working on the PowerPoint presentation. We all thought it was really creative for our project and decided to go with it. By 5 PM, we were ready to pitch our proposal to our instructor.

The presentation went better than we expected! Our instructor provided valuable feedback and, to our delight, our proposal was approved! It was such a relief to see our hard work and collaboration pay off. We left the office with big smiles and even bigger plans for what’s next. This day marked an important milestone for our group. With our proposal approved, we can now shift our focus to refining and executing our project. Stay tuned as we turn this idea into reality. Thanks for reading, and I’ll see you in the next post!

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phaeton-flier · 1 year ago

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Why is it so godforsakenly hard to do a group by action on a data frame in r

I need the current value and a mean for like a dozen data points. How are you getting beaten by pandas?

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shamira22 · 11 months ago

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1. **Select Your Data Set and Variables**: - Ensure you have a quantitative variable (e.g., test scores, weights, heights) and a categorical variable (e.g., gender, treatment group, age group).2. **Load the Data into Python**: - Use libraries such as pandas to load your dataset.3. **Check Data for Missing Values**: - Use pandas to identify and handle missing data.4. **Run the ANOVA**: - Use the `statsmodels` or `scipy` library to perform the ANOVA.Here is an example using Python:```pythonimport pandas as pdimport statsmodels.api as smfrom statsmodels.formula.api import olsimport scipy.stats as stats# Load your datasetdf = pd.read_csv('your_dataset.csv')# Display the first few rows of the datasetprint(df.head())# Example: Suppose 'score' is your quantitative variable and 'group' is your categorical variablemodel = ols('score ~ C(group)', data=df).fit()anova_table = sm.stats.anova_lm(model, typ=2)print(anova_table)# If the ANOVA is significant, conduct post hoc tests# Example: Tukey's HSD post hoc testfrom statsmodels.stats.multicomp import pairwise_tukeyhsdposthoc = pairwise_tukeyhsd(df['score'], df['group'], alpha=0.05)print(posthoc)```5. **Interpret the Results**: - The ANOVA table will show the F-value and the p-value. If the p-value is less than your significance level (usually 0.05), you reject the null hypothesis and conclude that there are significant differences between group means. - For post hoc tests, the results will show which specific groups are different from each other.6. **Create a Blog Entry**: - Include your syntax, output, and interpretation. - Example Interpretation: "The ANOVA results indicated that there was a significant effect of group on scores (F(2, 27) = 5.39, p = 0.01). Post hoc comparisons using the Tukey HSD test indicated that the mean score for Group A (M = 85.4, SD = 4.5) was significantly different from Group B (M = 78.3, SD = 5.2). Group C (M = 82.1, SD = 6.1) did not differ significantly from either Group A or Group B."7. **Submit Your Assignment**: - Ensure you follow all submission guidelines provided by Coursera.If you need specific help with your dataset or any part of the code, feel free to ask!

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codewithnazam · 1 year ago

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Data Manipulation: A Beginner's Guide to Pandas Dataframe Operations

Outline: What’s a Pandas Dataframe? (Think Spreadsheet on Steroids!) Say Goodbye to Messy Data: Pandas Tames the Beast Rows, Columns, and More: Navigating the Dataframe Landscape Mastering the Magic: Essential Dataframe Operations Selection Superpower: Picking the Data You Need Grab Specific Columns: Like Picking Out Your Favorite Colors Filter Rows with Precision: Finding Just the Right…

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#data manipulation #grouping data #how to manipulate data in pandas #pandas dataframe operations explained #pandas filtering #pandas operations #pandas sorting

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mariacallous · 1 year ago

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For years, China’s state-backed hackers have stolen huge troves of company secrets, political intelligence, and the personal information of millions of people. On Monday, officials in the United States and United Kingdom expanded the long list of hacking allegations, claiming China is responsible for breaching the UK’s elections watchdog and accessing 40 million people’s data. The countries also issued a raft of criminal charges and sanctions against a separate Chinese group following a multiyear hacking rampage.

In August last year, the UK’s Electoral Commission revealed “hostile actors” had infiltrated its systems in August 2021 and could potentially access sensitive data for 14 months until they were booted out in October 2022. The deputy prime minister, Oliver Dowden, told lawmakers on Monday that a China state-backed actor was responsible for the attack. In addition, Dowden said, the UK’s intelligence services have determined that Chinese hacking group APT31 targeted the email accounts of politicians in 2021.

“This is the latest in a clear pattern of malicious cyber activity by Chinese state-affiliated organizations and individuals targeting democratic institutions and parliamentarians in the UK and beyond,” Dowden said in the UK’s House of Commons. The revelations were accompanied by the UK sanctioning two individuals and one company linked to APT31.

Alongside the UK’s announcement on Monday, the US Department of Justice and Department of the Treasury’s Office of Foreign Assets Control unveiled further action against APT31, also known as Violet Typhoon, Bronze Vinewood, and Judgement Panda, including charging seven Chinese nationals with the conspiracy to commit computer intrusions and wire fraud.

The DOJ claims the hacking group, which has been linked back to China’s Ministry of State Security (MSS) spy agency, has spent 14 years targeting thousands of critics, businesses, and political entities around the world in widespread espionage campaigns. This includes posing as journalists to send more than 10,000 malicious emails that tracked recipients, compromising email accounts, cloud storage accounts, telephone call records, home routers, and more. The spouses of one high-ranking White House official and those of multiple US senators were also targeted, the DOJ says.

“These allegations pull back the curtain on China’s vast illegal hacking operation that targeted sensitive data from US elected and government officials, journalists and academics; valuable information from American companies; and political dissidents in America and abroad,” Breon Peace, a US attorney for the Eastern District of New York, said in a statement. “Their sinister scheme victimized thousands of people and entities across the world, and lasted for well over a decade.”

The moves come as countries increasingly warn of an increase in China-linked espionage, during a year when more than 100 countries will host major elections. Statements from officials focus on the impact of the hacking activity on democratic processes, including the targeting of elected officials around the world and the compromising of pro-democracy activists and lawmakers in Hong Kong. However, the disclosures also coincide with continued jostling from Western politicians over pro- or anti-China stances, including the proposed sale of TikTok to a US company, which could result in a ban on the popular app if the sale fails to go through.

As officials in the UK disclosed the details of the hacking activity, Lin Jian, a Chinese foreign ministry spokesperson, claimed it was “disinformation” and told reporters the country “opposes illegal and unilateral” sanctions. “When investigating and determining the nature of cyber cases, one needs to have adequate and objective evidence, instead of smearing other countries when facts do not exist, still less politicize cybersecurity issues,” Jian said in a daily press conference on Monday.

“China is embarking on a huge global campaign of interference and espionage, and the UK and the like-minded nations are pretty sick of it,” says Tim Stevens, a global security lecturer and head of the cybersecurity research group at King’s College London. Stevens says the public shaming and sanctions are unlikely to significantly change China’s actions but may signal a warning to other countries about what is and isn’t deemed acceptable when it comes to international affairs.

China has a broad range of hacking groups linked to its intelligence services and military, as well as companies that it contracts to launch some cyber operations. Many of these groups have been active for more than a decade. Dakota Cary, a China-focused consultant at security firm SentinelOne, says that groups associated with China’s civilian intelligence service are largely conducting diplomatic or government intelligence collection and espionage, while China’s military hackers are behind attacks on power grids and US critical infrastructure such as water supplies. “We do see China engaging in all of those activities simultaneously,” Cary says.

In announcing criminal charges and sanctions against members of APT31, officials in the US laid out a series of hacking allegations that include the targeting of businesses, political entities, and dissidents around the world. These included a “leading provider” of 5G telecoms equipment in the US, Norwegian government officials, and people working in the aerospace and defense industries. APT31 was run by the MSS’s Hubei State Security Department in the city of Wuhan, US officials say.

The seven Chinese nationals hit with charges are Ni Gaobin, Weng Ming, Cheng Feng, Peng Yaowen, Sun Xiaohui, Xiong Wang, and Zhao Guangzong. Both Zhao Guangzong and Ni Gaobin were also sanctioned. The two are alleged to be affiliated with Wuhan XRZ, a company that has also been sanctioned by the US and UK and is believed to be a cover for MSS-linked hacking operations. Employees of the company hacked into a Texas-based energy company in 2018, the US Treasury Department said.

The group used sophisticated malware—including Rawdoor, Trochilus, and EvilOSX—to compromise systems, according to a 27-page indictment unsealed by the DOJ. They also used a “cracked/pirated” version of penetration testing tool Cobalt Strike Beacon to compromise victims, the indictment says. It adds that, between 2010 and November 2023, the group “gained access” to a defense contractor that designed flight simulators for the US Army, Air Force, and Navy; a multi-factor authentication company; an American trade association; a steel company; a machine learning laboratory based in Virginia; and multiple research hospitals.

In its announcement, the UK outlined two separate China-linked incidents: first, the targeting of the email inboxes of 43 members of parliament (MPs) by APT31 in 2021; and second, the hack of the Electoral Commission by further unnamed China-linked hackers. Elections in the UK are decentralized and organized locally, with the commission overseeing the entire process. This setup means the integrity of the electoral process was not impacted, the commission says; however, a huge amount of data may have been taken by the hackers.

When the Electoral Commission revealed it had been compromised last year, it said the details of around 40 million people may have been accessed. The commission said names and addresses of people in Great Britain who were registered to vote between 2014 and 2022 could have been compromised, and that file-sharing and email systems could have been made accessible. “It’s really remarkable that China would go after election oversight systems, particularly given the diplomacy that the PRC [People’s Republic of China] is trying to pull off with the EU,” Cary says. “It’s a very significant act for the PRC to go after these types of systems,” Cary says. “It’s something that democracies are really sensitive to.”

While nations have called out China’s hacking activities for years, the country has evolved its tactics and techniques to become harder to detect. “Over the past couple of years, tired of having their operations rumbled and publicly outed, the Chinese have placed a growing emphasis on stealthy tradecraft in cyber espionage attacks,” Don Smith, vice president of threat intelligence at security firm Secureworks’ counter-threat unit, said in a statement. “This is a change in MO from its previous ‘smash and grab’ reputation but it is viewed by the Chinese as a necessary evolution to one, make it harder to get caught and two, make it nearly impossible to attribute an attack to them.”

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arcobalengo · 2 years ago

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Steve Kirsch: “We Can’t Find an Autistic Kid Who Was Unvaccinated”

“The Amish are a perfect example of a large group of people who are largely unvaccinated,” testified @stkirsch to the Pennsylvania State Senate.

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Retweet: https://twitter.com/VigilantFox/status/1674233704326078465?s=20

Follow @VigilantFox 🦊

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learnershub101 · 2 years ago

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