In 2024, the monetary damage caused by cybercrime reported to the United States' Internet Crime Complaint Center (IC3) saw a year-over-year increase, amounting to a historical peak of **** billion U.S. dollars. Overview of cybercrime in the U.S. Cybercrime continues to be one of the biggest challenges for governments around the world. In the United States, ****************** and ********* were among the most reported categories of cybercrime in 2024, with over ******* individuals falling victim to phishing attacks. Additionally, data breaches cost the U.S. organizations over ************ U.S. dollars on average as of February 2024. Fraud involving elderly Along with other reported internet crimes, online fraud is continuously growing. Targeting one of the most vulnerable groups, the elderly, cybercriminals show notorious skills in ************************************************************. Furthermore, individuals aged 60 and older, reported falling victims of extortion and personal data breach in 2024.

Over *********** cases of cyber crime across India were reported to the Indian Cyber Crime Coordination Centre (I4C) via the National Cyber Crime Reporting Portal (NCRP) in 2024. The number of cyber crimes in the country saw a massive spike between 2021 and 2022 and has been on the rise ever since. Roughly *** billion Indian rupees were lost in financial fraud cases spanning from 2023 to 2024.

Explore eye-opening cybercrime stats, uncover trends in hacking, data breaches, and digital threats impacting businesses, and governments!

The dataset "Cybersecurity Cases in India" is a comprehensive collection of real-world cybersecurity incidents reported across various cities in India. The dataset encapsulates the financial loss, incident types, and categories, providing a detailed overview of the cybercrime landscape in one of the world’s largest digital economies. With over 1000 records, it spans incidents from 2020 to 2024, covering various types of cybercrimes such as phishing, online fraud, malware attacks, ransomware, data breaches, DDoS attacks, identity theft, and more. Each record captures important attributes of the incidents, such as the year, date of occurrence, amount lost in INR, the type of incident, the city in which it occurred, and the category of the affected entity (e.g., financial, personal, corporate).

The dataset is structured to enable analysis of the trends in cybercrime over time, the financial impact of various cyberattacks, and the geographic distribution of incidents across Indian cities. It serves as a critical resource for cybersecurity professionals, policymakers, law enforcement agencies, and academic researchers seeking to understand the challenges posed by cybercrime in India and to identify strategies to combat these challenges.

1. Dataset Purpose and Scope

The dataset’s primary purpose is to provide an extensive, granular view of the nature and scope of cybersecurity incidents in India. It enables the analysis of the frequency, severity, and financial impact of cybercrimes across different types of attacks, cities, and time periods. As cybercrimes continue to rise globally, including in India, this dataset serves as an important tool for understanding the evolving threats and risks in cyberspace. Cybersecurity experts and analysts can leverage this dataset to identify patterns and trends, while government and law enforcement agencies can use it to devise more targeted interventions and preventive measures.

India, with its large and growing digital footprint, is a prime target for cybercriminals. The country's rapidly expanding internet user base, coupled with increasing digital adoption in various sectors like finance, healthcare, education, and e-commerce, makes it an attractive target for cyberattacks. This dataset allows stakeholders to understand how cybercrime evolves in response to these dynamics.

The dataset is a rich resource for understanding the following:

• Incident Frequency: How often different types of cybercrimes are reported in various Indian cities.
• Financial Impact: The monetary losses associated with each type of cybercrime.
• Geographic Distribution: The prevalence of specific types of cybercrimes in particular cities or states.
• Trend Analysis: How cybercrime has evolved over the years in terms of volume and impact.

2. Dataset Structure and Variables

The dataset includes the following key variables, each contributing valuable information to the analysis:

• Year: The year in which the cybercrime incident occurred. This variable helps track the growth or decline of cybercrime incidents over time.
• Date: The specific date of the cybercrime incident. This allows for time-series analysis of the data.
• Amount_Lost_INR: The financial loss associated with the cybercrime incident, expressed in Indian Rupees (INR). This variable highlights the economic impact of each cyberattack and can be used to assess the severity of different incidents.
• Incident_Type: The type of cybercrime incident. This can include phishing, online fraud, malware attacks, ransomware, data breaches, DDoS attacks, and identity theft. This variable is crucial for understanding which types of cybercrimes are most prevalent and how they differ in their impact.
• City: The city where the incident occurred. This allows for the geographic analysis of cybercrime, helping to identify high-risk areas and cities where certain types of cyberattacks are more common.
• Category: The category of the entity affected by the cybercrime, such as financial institutions, government bodies, corporations, educational institutions, or individuals. This variable provides insights into which sectors are more vulnerable to specific types of cyberattacks.

3. Cybersecurity Threat Landscape in India

India's digital transformation has made it a prime target for cybercriminals. As of 2023, India is one of the largest internet markets in the world, with over 600 million active internet users. The rapid growth of e-commerce, digital banking, social media, and government services has created new opportunities for cybercriminals to exploit vulnerabilities in digital systems. According to a 2022 report by the Indian Computer Emergency Response Team (CERT-In), India witnessed a significant increase in cybersecurity incidents, with millions of cyberattacks targeting individuals, b...

The global indicator 'Estimated Cost of Cybercrime' in the cybersecurity market was forecast to continuously increase between 2024 and 2029 by in total *** trillion U.S. dollars (+***** percent). After the eleventh consecutive increasing year, the indicator is estimated to reach ***** trillion U.S. dollars and therefore a new peak in 2029. Notably, the indicator 'Estimated Cost of Cybercrime' of the cybersecurity market was continuously increasing over the past years. Ransomware and manufacturing industry Cyberattacks remain a significant challenge for organizations worldwide, sectors such as manufacturing, finance, and insurance were the most affected by these attacks. Among the various types of cyber threats, ransomware was the most frequently detected, accounting for around ** percent of all incidents. The manufacturing industry, in particular, faced the highest number of ransomware attacks, making it the most targeted sector globally. Cybersecurity awareness and investments Organizations worldwide became increasingly aware of the dangers posed by cyberattacks, approximately ** percent of internet users became familiar terms such as with "ransomware." In response to these threats, companies invested more in cybersecurity. In 2024, the average annual increase in IT security budgets was expected to reach *** percent. In fact, for companies worldwide, enhancing the cyber resilience of their cybersecurity teams was the top spending priority in 2023.

The dataset contains the following columns , each described below :

Attack Type: Randomly selected from a broad set of attack types (e.g., phishing, DDoS, malware, etc.). Target System: Corporate IT systems such as servers, databases, user accounts, APIs, and more. Outcome: Whether the attack succeeded or failed. Timestamp: Time of the attack, randomly distributed over the past year. Attacker IP Address: Simulated attacker IP addresses. Target IP Address: Random IP addresses representing internal or external targets. Data Compromised: Amount of data compromised (in gigabytes) if the attack succeeded. Attack Duration: Time the attack lasted (in minutes). Security Tools Used: Various defense mechanisms like firewalls, IDS, antivirus, etc. User Role: The role of the user impacted by the attack (admin, employee, or external user). Location: Country or region where the attack originated or targeted. Attack Severity: Numerical indicator of the severity level (e.g., scale from 1-10). Industry: Type of industry targeted, such as healthcare, finance, government, etc. Response Time: Time taken by the security team to respond (in minutes). Mitigation Method: Steps taken to mitigate the attack (patching, containment, etc.)

Acknowledgement This dataset is a synthetic creation, generated using ChatGPT to simulate realistic cybersecurity incidents. It is designed to serve as a learning tool for beginners and data enthusiasts, offering a platform for practice and exploration in cybersecurity data analysis. By reflecting real-world cybercrime scenarios, this dataset encourages experimentation and deeper insights into various attack vectors, system vulnerabilities, and defense mechanisms. Its purpose is to promote hands-on learning in a controlled environment, enabling users to enhance their understanding of cybersecurity threats, analysis, and mitigation strategies.

This dataset provides a comprehensive overview of the financial losses due to various types of cybercrime in all 50 states and Washington D.C. in the United States for the years 2020 and 2021. The dataset is curated with detailed attention to demographic and regional variances, as well as the types of cybercrime that occurred. The data for individual crimes was extracted from the Internet Crime Complaint Centre, a unit under the FBI (Federal Bureau of Investigation).

The columns in this dataset are:

• s/n: Serial Number.
• State: The US state in which the cybercrimes occurred.
• Year: The year of the cybercrimes (2020 or 2021).
• Population: The population of the state for the given year.
• Totalcrime_count: The total count of all cybercrimes in the state for the given year.
• Totalcrime_loss: The total financial loss (in US dollars) due to all cybercrimes in the state for the given year.
• Bec_count: The count of Business Email Compromise (BEC) incidents in the state for the given year.
• Bec_loss: The total financial loss (in US dollars) due to BEC in the state for the given year.
• Romance_counts: The count of romance scam incidents in the state for the given year.
• Romance_loss: The total financial loss (in US dollars) due to romance scams in the state for the given year.
• Creditcard_count: The count of credit card fraud incidents in the state for the given year.
• Creditcard_loss: The total financial loss (in US dollars) due to credit card fraud in the state for the given year.
• Databreach_count: The count of data breach incidents in the state for the given year.
• Databreach_loss: The total financial loss (in US dollars) due to data breaches in the state for the given year.
• GovtImp_count: The count of government impersonation fraud incidents in the state for the given year.
• GovtImp_loss: The total financial loss (in US dollars) due to government impersonation fraud in the state for the given year.
• Age<20_count: The count of cybercrime victims under the age of 20.
• Age<20_loss: The total financial loss (in US dollars) for victims under the age of 20.
• Age<29_count: The count of cybercrime victims between the ages of 20 and 29.
• Age<29_loss: The total financial loss (in US dollars) for victims between the ages of 20 and 29.
• Age<39_count: The count of cybercrime victims between the ages of 30 and 39.
• Age<39_loss: The total financial loss (in US dollars) for victims between the ages of 30 and 39.
• Age<49_count: The count of cybercrime victims between the ages of 40 and 49.
• Age<49_loss: The total financial loss (in US dollars) for victims between the ages of 40 and 49.
• Age<59_count: The count of cybercrime victims between the ages of 50 and 59.
• Age<59_loss: The total financial loss (in US dollars) for victims between the ages of 50 and 59.
• Age>60_count: The count of cybercrime victims aged 60 and above.
• Age>60_loss: The total financial loss (in US dollars) for victims aged 60 and above.

This dataset is ideal for those who wish to investigate trends in cybercrime across different US states, the financial impact of various types of cybercrime, or the impact of cybercrime on different age groups. It can also be used to generate insights for developing strategies to combat cybercrime, implementing protective measures, and raising awareness about this growing issue. The crime data contained herein was extracted from the Internet Crime Complaint Centre, a unit under the FBI, which ensures its authenticity and reliability.

Description:-

The Cyber Crime in India dataset is a comprehensive collection of cybercrime statistics reported in India over a period of two decades, from 2003 to 2022. The dataset consists of two sheets, and in this analysis, we will focus on Sheet 1, which contains information related to cybercrime incidents at the State, Union Territory (UT), and City levels. This dataset provides valuable insights into the prevalence and variations of cyber crimes across different regions in India and is a valuable resource for researchers, law enforcement agencies, policymakers, and cybersecurity experts.

Content:

The dataset is presented in a structured format and includes the following key features:

Temporal Scope: The data spans from the year 2003 to 2022, allowing users to analyze cyber crime trends over the past twenty years. This extensive timeframe is crucial for identifying long-term patterns and understanding how cyber offenses have evolved over time.

Geographical Granularity: Cybercrime incidents are categorized based on their occurrence in various states and union territories of India. This geographical granularity enables users to conduct a detailed analysis of regional variations in cybercrime rates and identify areas that may require specific attention in terms of cybersecurity measures.

Classification by Acts: The dataset classifies cyber crime incidents into two categories based on the legal frameworks under which they were registered:

a) Information Technology (IT) Act: This category includes cyber crimes that fall under the purview of the IT Act. The IT Act was enacted to provide legal provisions for dealing with electronic transactions, digital signatures, and cyber offenses.

b) Indian Penal Code (IPC) Act: This category comprises cyber crimes registered under the IPC Act, which deals with a broader range of criminal offenses, including those committed through digital means.

Year-on-Year Comparisons: The dataset provides variations in cybercrime incidents between the current year and the year preceding it. This feature allows users to understand yearly fluctuations in cybercrime rates, identify trends, and assess the effectiveness of cyber security measures implemented over time.

Potential Analysis and Applications:

The Cyber Crime in India dataset offers a plethora of applications for different stakeholders: Trend Analysis: Researchers can conduct an in-depth trend analysis to gain insights into the changing nature of cyber crimes and identify potential emerging threats.

Geographical Patterns: Law enforcement agencies and policymakers can identify regional hotspots and allocate resources effectively to address cyber crime challenges in specific areas.

Legislative Efficacy: By comparing the number of cyber crimes under the IT Act and IPC Act, policymakers can assess the effectiveness of existing cybercrime legislation and identify areas for improvement.

Predictive Modeling: Machine learning practitioners can build predictive models to forecast cybercrime trends, helping stakeholders proactively prepare for potential threats.

In 2024, individuals over the age of 60 accounted for the highest number of recorded cybercrime victims in the United States. According to the latest data, more than 147,000 people reported cyber crimes in the year examined. The second-most targeted were individuals between 40 and 49 years, with over 112,000 complaints.

In 2024, the most common type of cybercrime reported to the United States internet Crime Complaint Center was phishing, with its variation, spoofing, affecting approximately 193,000 individuals. In addition, over 86,000 cases of extortion were reported to the IC3 during that year. Dynamic of phishing attacks Over the past few years, phishing attacks have increased significantly. In 2024, over 193,000 individuals fell victim to such attacks. The highest number of phishing scam victims since 2018 was recorded in 2021, approximately 324 thousand.Phishing attacks can take many shapes. Bulk phishing, smishing, and business e-mail compromise (BEC) are the most common types. With the recent development of generative AI, it has become easier to craft a believable phishing e-mail. This is currently among the top concerns of organizations leaders. Impact of phishing attacks Among the most targeted industries by cybercriminals are healthcare, financial, manufacturing, and education institutions. An observation carried out in the fourth quarter of 2024 found that software-as-a-service (SaaS) and webmail was most likely to encounter phishing attacks. According to the reports, almost a quarter of them stated being targeted by a phishing scam in the measured period.

Police-reported cybercrime, by cyber-related violation, number of incidents and year to date total, preliminary quarterly data, Canada and regions (Atlantic, Quebec, Ontario, Prairies, British Columbia and Territories), Q1 (January to March) 2024 to Q3 (July to September) 2025.

Between October 2024 and August 2025, organizations worldwide experienced 139,373 cyber incidents. Misuse accounted for 44.6 percent of these cases, while hacking incidents represented another 30.8 percent.

In 2025, the number of data compromises in the United States stood at 3,322 cases. Meanwhile, over 278.83 million individuals were affected in the same year by data compromises, including data breaches, leakage, and exposure. While these are three different events, they have one thing in common. As a result of all three incidents, the sensitive data is accessed by an unauthorized threat actor. Industries most vulnerable to data breaches Some industry sectors usually see more significant cases of private data violations than others. This is determined by the type and volume of the personal information organizations of these sectors store. Financial services, healthcare, and professional services were the industry sectors that recorded the most data breaches. Overall, the number of healthcare data breaches in some industry sectors in the United States has gradually increased within the past few years. However, some sectors saw a decrease.

This dataset presents a detailed overview of crime data recorded across Indian states in 2025. It categorizes incidents by crime type, including property crimes, violent offenses, cybercrime, and crimes against women and children. The data enables trend analysis, evaluation of law enforcement effectiveness, and informed decision-making for public safety and crime prevention policies.

In 2022, around four in ten internet users worldwide have ever experienced cybercrime. Based on a survey conducted between November and December 2022, internet users in India were most likely to have fallen victim to cybercrime, as nearly 70 percent of respondents claimed to have ever experienced cybercrime. The United States ranked second, with almost half of the respondents, 49 percent, saying they had experienced internet crime.

Police-reported cybercrime, number of incidents and rate per 100,000 population, Canada, provinces, territories, Census Metropolitan Areas and Canadian Forces Military Police, 2014 to 2024.

This dataset explores the relationship between various socioeconomic factors and crime rates across different districts in India from 2018 to 2023. It contains 700 samples, each representing a unique district-year combination, with synthetic but realistic values modeled after publicly available Indian government data sources like NCRB, MOSPI, and Wikipedia GDP statistics.

📂 Dataset Features

Column Name Description State/UT Name of the Indian State or Union Territory District Simulated district name within the state/UT Year Year of the observation (2018–2023) Population Total estimated population of the district GDP per Capita (INR) Average income per person in Indian Rupees Literacy Rate (%) Literacy rate of the population (in %) Poverty Rate (%) Percentage of the population below poverty line Unemployment Rate (%) Estimated unemployment rate (in %) Violent Crimes Number of violent crimes reported in that year Property Crimes Number of property-related crimes reported Cyber Crimes Number of cybercrime cases registered Total Crimes Reported Sum of all types of crimes reported Crime Rate per 100,000 Total crime rate normalized per 100,000 population 🔍 Use Cases 📈 Regression & Classification Modeling Predict crime rates using socioeconomic indicators or categorize districts into risk levels.

🧠 Feature Engineering Practice Great for practicing correlation analysis, feature scaling, and data preprocessing.

🗺️ Visualization Projects Build dashboards to explore crime trends across Indian districts and years.

🧑‍💻 ML & AI Applications Suitable for linear regression, decision trees, random forest, or time-series forecasting with feature expansion.

🏛️ Data Provenance While the data is synthetically generated, it is inspired by:

National Crime Records Bureau (NCRB)

Ministry of Statistics and Programme Implementation (MOSPI)

Wikipedia GDP per capita of Indian States

Explore the global landscape of cybercrime regulation with this clean, beginner-friendly dataset derived from the International Telecommunication Union (ITU) Global Cybersecurity Index. This dataset provides cybercrime regulation scores for 194 countries across two time periods (2020 and 2024), making it perfect for trend analysis, geographic comparisons, and policy research.

🎯 What's Inside?

• 388 records covering all UN-recognized countries
• Cybercrime Regulation Scores (0.0 to 1.0 scale)
• Geographic Regions (7 regions: Africa, Asia, Europe, Americas, Oceania, Middle East)
• Income Classifications (High, Upper Middle, Lower Middle, Low Income)
• Change Analysis dataset showing 2020→2024 improvements
• Zero missing values - completely clean and ready to use

📊 Perfect For:

• Beginner Data Analysts learning data visualization, choropleth maps, and correlation analysis
• Students studying international relations, cybersecurity, or public policy
• Researchers analyzing global governance trends
• Data Scientists practicing geographic data analysis
• Policy Makers benchmarking national performance

🔍 Key Insights You Can Discover:

1. Which regions lead in cybercrime regulation?
2. How does economic development correlate with cyber legal frameworks?
3. Which countries made the most progress from 2020 to 2024?
4. What percentage of countries have comprehensive cybercrime laws?
5. Are there regional patterns in regulatory development?

📈 Data Quality:

✅ Cleaned & Standardized: Reduced from 39 to 6 essential columns
✅ Well-Documented: Comprehensive README with data provenance
✅ Beginner-Friendly: Clear column names, no jargon, easy to understand
✅ Analysis-Ready: No preprocessing needed
✅ Extra Enrichment: Added region & income level classifications

🌟 Why This Dataset?

Unlike raw government data, this dataset has been: - Simplified for quick understanding - Enhanced with meaningful classifications - Validated for data quality - Documented with full methodology transparency

The Dataset "Cyber Security Indexes" includes four indicators which illustrate the current cyber security situation around the world. The data is provided on 193 countries and territories, grouped by five geographical regions - Africa, North America, South America, Europe and Asia-Pasific.

The Cybersecurity Exposure Index (CEI) defines the level of exposure to cybercrime by country from 0 to 1; the higher the score, the higher the exposure (provided by 10guard). The indicator was last updated in 2020.

The Global Cyber Security Index (GCI) is a trusted reference that measures the commitment of countries to cybersecurity at a global level – to raise awareness of the importance and different dimensions of the issue (provided by the International Telecommunication Union - ITU). The indicator was last updated in 2021.

The National Cyber Security Index (NCSI) measures a country's readiness to address cyber threats and manage cyber incidents. It is composed of categories, capacities, and indicators (provided by NCSI). The indicator was last updated in January 2023.

The Digital Development Level (DDL) defines the average percentage the country received from the maximum value of both indices (provided by NCSI). The indicator was last updated in January 2023.

The dataset can be used for practising data cleaning, data visualization (on maps and round/bar charts), finding correlations between the indexes and predicting the missing data.

The data was used in the analytical article research The Geography of Cybersecurity: Cyber Threats and Vulnerabilities

India saw a significant jump in cyber crimes reported in 2022 from the previous year. That year, over ****** cyber crime incidents were registered. Karnataka and Telangana accounted for the highest share during the measured period. Uttar Pradesh leads the way The northern state of Uttar Pradesh had the highest number of cyber crimes compared to the rest of the country, with over * thousand cases registered with the authorities in 2018 alone. India’s tech state, Karnataka, followed suite that year. A majority of these cases were registered under the IT Act with the motive to defraud, or sexually exploit victims. It's a numbers game It was estimated that in 2017, consumers in India collectively lost over 18 billion U.S. dollars due to cyber crimes. However, these were estimates based only on reported numbers. In a country like India, it is highly likely that the actual figures could be under-reported due to a lack of cyber crime awareness or the mechanisms to classify them. Recent government initiatives such as a dedicated online portal to report cyber crimes could very well be the main factor behind a sudden spike in online crimes from 2017 onwards.