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.

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 Q2 (April to June) 2025.

This data set includes details about the HSI Cyber Crimes Center being redesignated as the DHS Cyber Crimes Center (C3). The redesignation aimed to enhance coordination and cooperation across DHS so that there is a coordinated, DHS-wide approach.

The largest reported data leakage as of January 2025 was the Cam4 data breach in March 2020, which exposed more than 10 billion data records. The second-largest data breach in history so far, the Yahoo data breach, occurred in 2013. The company initially reported about one billion exposed data records, but after an investigation, the company updated the number, revealing that three billion accounts were affected. The National Public Data Breach was announced in August 2024. The incident became public when personally identifiable information of individuals became available for sale on the dark web. Overall, the security professionals estimate the leakage of nearly three billion personal records. The next significant data leakage was the March 2018 security breach of India's national ID database, Aadhaar, with over 1.1 billion records exposed. This included biometric information such as identification numbers and fingerprint scans, which could be used to open bank accounts and receive financial aid, among other government services.

Cybercrime - the dark side of digitalization As the world continues its journey into the digital age, corporations and governments across the globe have been increasing their reliance on technology to collect, analyze and store personal data. This, in turn, has led to a rise in the number of cyber crimes, ranging from minor breaches to global-scale attacks impacting billions of users – such as in the case of Yahoo. Within the U.S. alone, 1802 cases of data compromise were reported in 2022. This was a marked increase from the 447 cases reported a decade prior. The high price of data protection As of 2022, the average cost of a single data breach across all industries worldwide stood at around 4.35 million U.S. dollars. This was found to be most costly in the healthcare sector, with each leak reported to have cost the affected party a hefty 10.1 million U.S. dollars. The financial segment followed closely behind. Here, each breach resulted in a loss of approximately 6 million U.S. dollars - 1.5 million more than the global average.

Cybercrime Forensic Dataset (7400 Observations)

This dataset contains 7,400 observations simulating various cyber activities and anomalies for cybercrime forensic analysis. This data contains information about when a user engage in network activities, file management, and system access, with potential security threats flagged for forensic investigation.

Key Features

1. Timestamp: The exact date and time of the recorded activity, which is crucial for tracking events in cybercrime forensics and correlating incidents across different timelines.

2. User_ID: A unique identifier assigned to each user interacting with the system, used to trace specific actions to individual users.

3. IP_Address: The IP address from which the user activity originates. This can help in geolocation tracing, identifying suspicious external or internal access points.

4. Activity_Type: The type of action performed by the user, including:

   • Login
   • File Access
   • File Modification
   • File Deletion
   • Network Traffic
   • Remote Login
   • USB Insert

5. Resource_Accessed: The file or system resource accessed during the activity, such as network logs, confidential files, or shared project files.

6. File_Name: For activities involving files (access, modification, deletion), the name of the file is recorded for forensic analysis.

7. Action: The outcome of the activity, such as successful logins, file reads, writes, deletions, or other actions like USB insertions.

8. Login_Attempts: If the activity involves a login (such as remote login), this field captures the number of attempts made by the user, which is often useful for detecting brute-force attacks.

9. File_Size: In cases involving file access or modification, this field captures the size of the file, helping in identifying potential data exfiltration attempts or sensitive file interactions.

10. Anomaly_Type: Flags certain suspicious or anomalous activities such as:

    • Brute_Force
    • DDoS_Attempt
    • Data_Exfil
    • USB_Access
    • None (for normal activities)

11. Label: A binary classification of the event's nature, where:

    • Normal: No suspicious behavior is detected.
    • Suspicious: The event has been flagged due to anomalies like brute-force attacks, data exfiltration, or unauthorized USB access.

Purpose:

This dataset is designed to provide a rich resource for developing machine learning models aimed at detecting anomalous behavior in network and user activities. It can be used for training cybercrime forensic tools, such as: - Intrusion Detection Systems (IDS) - Anomaly detection models - Forensic analysis to uncover suspicious activities in a system's operational logs

Potential Applications:

• Cybersecurity: Identifying security breaches, unauthorized access, and malicious activity within an organization's network.
• Incident Response: Providing forensic evidence for post-attack investigations.
• Machine Learning Models: Training models for threat detection, such as anomaly detection and classification algorithms, based on user behaviors and system activities.

This dataset is a valuable tool for researchers, cybersecurity professionals, and data scientists working on building predictive models to mitigate and analyze cybercrime incidents.

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 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.

In 2024, the number of data compromises in the United States stood at 3,158 cases. Meanwhile, over 1.35 billion 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. In 2024 the financial services, healthcare, and professional services were the three industry sectors that recorded 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 decrease. Largest data exposures worldwide In 2020, an adult streaming website, CAM4, experienced a leakage of nearly 11 billion records. This, by far, is the most extensive reported data leakage. This case, though, is unique because cyber security researchers found the vulnerability before the cyber criminals. The second-largest data breach is the Yahoo data breach, dating back to 2013. The company first reported about one billion exposed records, then later, in 2017, came up with an updated number of leaked records, which was three billion. In March 2018, the third biggest data breach happened, involving India’s national identification database Aadhaar. As a result of this incident, over 1.1 billion records were exposed.

The dataset contains year-, state- and city-wise historically compiled data on the number of cyber crimes committed in violation of Information Technology Act (IT Act) in Indian cities with over one million population. The different types of cyber crimes covered in the dataset include Tampering Computer Source documents, Cyber Terrorism, Publication or transmission of obscene or sexually explicit act in electronic form, Interception or Monitoring or decryption of Information, Unauthorized access or attempt to access to protected computer system, Abetment to Commit Offences, Publication or Transmission of Obscene or Sexually Explicit Act, etc. in Electronic Form, Breach of Confidentiality or Privacy and Disclosure of Information in Breach of Lawful Contract , Hacking, Obscene Publication or Transmission in Electronic Form, Failure in Obtaining Licence or Digital Signature by misrepresentation or suppression of fact, Publishing false digital Signature Certificate, Fraud Digital Signature, Breach of confidentiality or privacy, other computer related offences such as Ransomware, Offences other than Ransomware, Dishonestly receiving stolen computer resource or communication device, Identity Theft, Cheating by personation by using computer resource, Violation of Privacy, Failure Of compliance or orders of certifying Authority, To assist to decoy or the information in interception by Government Agency, Hacking crimes such as Loss or damage to computer resource or utility, Publication or transmission of Obscene or Sexually Explicit Act in Electronic Form involving Children and Adults, etc.

Between the third quarter of 2024 and the second quarter of 2025, the number of records exposed in data breaches in the United States decreased significantly. In the most recent measured period, over 16.9 million records were reported as leaked, down from around 494.17 million in the third quarter of 2024.

Dancho Danchev is the world's leading expert in the field of cybercrime fighting and threat intelligence gathering having actively pioneered his own methodlogy for processing threat intelligence leading to a successful set of hundreas of high-quality anaysis and research articles published at the industry's leading threat intelligence blog - ZDNet's Zero Day, Dancho Danchev's Mind Streams of Information Security Knowledge and Webroot's Threat Blog with his research featured in Techmeme, ZDNet, CNN, PCWorld, SCMagazine, TheRegister, NYTimes, CNET, ComputerWorld, H+Magazine currently producing threat intelligence at the industry's leading threat intelligence blog - Dancho Danchev's - Mind Streams of Information Security Knowledge.

With his research featured at RSA Europe, CyberCamp, InfoSec, GCHQ and Interpol the researcher continues to actively produce threat intelligence at the industry's leading threat intelligence blog - Dancho Danchev's - Mind Streams of Information Security Knowledge publishing a diverse set of hundreds of high-quality research analysis detailing the malicious and fraudulent activities at nation-state and malicious actors across the globe.

This Torrent is his official "Cybercrime Research USB Stick Compilation" which basically represents all of this public research conducted and distributed publicly throughout the period of December, 2005 up to 2021 which includes full offline copies of his research available in multiple E-Book reader format and include full offline copy of one of the security industry's most popular security publications which is his personal blog - Dancho Danchev's Blog - Mind Streams of Information Security Knowledge", all of his publicly accessible research for ZDNet's Zero Day Blog including all of his research for Webroot Inc. including a copy of his old Twitter account for reference and research purposes where he participated with it in a Top Secret U.K GCHQ Government program called "Lovely Horse" that aims to monitor hackers online including all of his cyber warfare articles for Unit-123.org including tons of personally identifiable information on the bad guys which could be used for cyber attack attribution and research purposes including a copy of his 2021 compilation on personally identifiable information on the bad guys which is basically a 230 pages OSINT analysis on some of the most prolific and most popular hacking groups and hacking teams internationally including copies of two of his highly popular research studies on Iran's Hacking Ecosystem including the associated Maltego SNA (Social Network Analysis) graphs.

Dancho Danchev can be reached at - https://ddanchev.blogspot.com or at dancho.danchev@hush.com

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.

In 2024, individuals aged 60 and older in the United States filed more than ****** complaints about phishing cases. Approximately ****** victims reported incidents of personal data breach. ********* was also frequently encountered by victims aged 60 and older in the measured year. Overall, the number of complaints about cryptocurrency crimes almost ******* compared to 2023.

Dataset

This dataset was created by JOEL2706

Contents

India Cyber Crime: IPC Section: Number of Cases Registered data was reported at 33,798.000 Unit in 2022. This records an increase from the previous number of 25,384.000 Unit for 2021. India Cyber Crime: IPC Section: Number of Cases Registered data is updated yearly, averaging 738.000 Unit from Dec 2002 (Median) to 2022, with 21 observations. The data reached an all-time high of 33,798.000 Unit in 2022 and a record low of 176.000 Unit in 2008. India Cyber Crime: IPC Section: 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.

Dataset

This dataset was created by Yash

Contents

This survey was based on an online platform, namely Google forms. To ensure data confidentiality, anonymous participation was enabled, and the results were stored in a local database for further analysis. During preprocessing, participants were asked if they agreed or disagreed with participating in the survey. If they agreed, they could access the questionnaire by logging in to Google forms using their Google accounts. They were allowed to submit their answers only one time. Following this phase, all responses were stored locally in order to process the data and further analyze the results. To perform these tasks, the software R studio was used.

Data Collection

Since the aim of the study is Saudi awareness, we used two methods. First, we browsed public and private university websites in Saudi Arabia, then created a list of faculty email addresses to contact and inform faculty members about our project and goals and provide an invitation to participate. The faculty members ranged from lecturers and teaching assistants to professors. Second, we utilized the WhatsApp application to locate people known to meet the requirements and asked them to forward the invitation to their friends, family members and colleagues who also met the requirements. We avoided posting the survey on social media to assure the validity of participants.

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.