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.

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.

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.

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 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.

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).

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

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.

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

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.

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.

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.

Dataset

This dataset was created by Nagesh Bait

Contents

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.

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.

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.

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...