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.

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.

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.

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.

Annual data on the nature of fraud and computer misuse offences from the Crime Survey for England and Wales (CSEW). Year ending March 2021 and March 2022 data are from the Telephone-operated Crime Survey for England and Wales (TCSEW).

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.

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.

Estimates from Crime Survey for England and Wales (CSEW) on fraud and computer misuse. Also data from Home Office police recorded crime on the number of online offences recorded by the police and Action Fraud figures broken down by police force area.

These tables were formerly known as Experimental tables.

Please note: This set of tables are no longer produced. All content previously released within these tables has, or will be, redistributed among other sets of tables.

Dataset

This dataset was created by JOEL2706

Contents

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.

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.

Dataset

This dataset was created by Razia Awais

Released under Other (specified in description)

Contents

India Cyber Crime: IT Act, 2000: Number of Cases Registered data was reported at 31,908.000 Unit in 2022. This records an increase from the previous number of 27,427.000 Unit for 2021. India Cyber Crime: IT Act, 2000: Number of Cases Registered data is updated yearly, averaging 2,876.000 Unit from Dec 2002 (Median) to 2022, with 21 observations. The data reached an all-time high of 31,908.000 Unit in 2022 and a record low of 60.000 Unit in 2003. India Cyber Crime: IT Act, 2000: Number of Cases Registered data remains active status in CEIC and is reported by National Crime Records Bureau. The data is categorized under India Premium Database’s Crime – Table IN.CRA001: Crime Statistics.

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

This dataset provides year, state and city wise police disposal data for cyber crimes in India's metropolitan cities, including chargesheeting, pendency and final reports for offences like cyber fraud, identity theft, cyber pornography and online cheating.

The annual number of complaints of cybercrime received annually on the U.S. Internet Crime Complaint Center (IC3) website increased significantly between 2000 and 2024. The center received around 859,530 complaints in the most recently reported year.

Dataset

This dataset was created by Nagesh Bait

Contents

Police-reported cybercrime, by cyber-related violation (homicide, invitation to sexual touching, sexual exploitation, luring a child via a computer, voyeurism, non-consensual distribution of intimate images, extortion, criminal harassment, indecent/harassing communications, uttering threats, fraud, identity theft, identity fraud, mischief, fail to comply with order, indecent acts, child pornography, making or distribution of child pornography, public morals, breach of probation), Canada (selected police services), 2014 to 2024.

This dataset contains year wise police disposal statistics by cyber crime head, covering investigation outcomes for offences like computer related crimes, cyber fraud, identity theft, cyber pornography, cyber stalking and misuse of digital platforms.

India Cyber Crime: IPC Section: Number of Persons Arrested data was reported at 33,798.000 Person in 2022. This records an increase from the previous number of 25,384.000 Person for 2021. India Cyber Crime: IPC Section: Number of Persons Arrested data is updated yearly, averaging 1,148.000 Person from Dec 2002 (Median) to 2022, with 21 observations. The data reached an all-time high of 33,798.000 Person in 2022 and a record low of 195.000 Person in 2008. India Cyber Crime: IPC Section: Number of Persons Arrested data remains active status in CEIC and is reported by National Crime Records Bureau. The data is categorized under India Premium Database’s Crime – Table IN.CRA001: Crime Statistics.