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 explores the relationship between user behavior on social media platforms and their exposure to cybercrimes through malicious online advertisements. It simulates how demographic factors, device usage patterns, authentication habits, and browsing contexts influence the likelihood of clicking on harmful ads or being redirected to malicious sites.

The data aims to assist cybersecurity researchers, data scientists, and social media analysts in understanding online threat patterns, building predictive models, and designing preventive security mechanisms.

In the first half of 2023, investment-related scams accounted for the highest share of costs of scams, ** percent, performed on social media. Romance scams ranked second, with ** percent of financial losses from scams performed on social media.

In 2024, the Directorate for Investigating Organized Crime and Terrorism in Romania closed ***** cybercrime cases. However, the number of pending cases has increased by **** percent since 2023.       Cybercrime in Romania  Cybercrime poses a significant challenge in Romania, with distinct patterns observed across regions and varying attack methods. The Bucharest-Ilfov region stands out, as **** percent of respondents reported using software programs to limit online activity tracking in 2023, indicating a notable concern for digital privacy. In contrast, the North-West region had a lower adoption rate at *** percent. Regarding malware threats, Trojan.AgentACBD led the charts in 2023, registering ****** attack alerts, closely followed by Trojan.IoT.Mirai and InfoStealer.AGENTTESLA. Phishing attacks fluctuated, peaking at ****** incidents in April 2023 but dropping to ***** in December 2023. This dynamic landscape underscores the need for robust cybersecurity measures nationwide.       Online Video Games  The digital landscape is evolving, marked by notable shifts in women's internet usage. While the overall share of internet users has risen for both genders, the gender gap has significantly narrowed. In 2024, female internet users lagged males by only *** percent, a substantial improvement from the *** percent gap in 2019. Social media usage remained the most popular online activity for women. However, a noteworthy trend is women's substantial use of the internet to gather health-related information, showcasing a diversified gender digital presence.

The datasert contains year- and state-wise historiclally compiled data on the number of cyber crimes committed in violation of Indian Penal Code (IPC). The different types of cyber crimes covered in the dataset include Abetment of Suicide - Online, Cyber Stalking or Bullying of Women or Children, Data theft, Cheating, Forgery, Defamation or Morphing (IPC r/w Indecent Representation of Women Act), Fake Profile (IPC r/w SLL), Counterfeiting, Cyber Blackmailing or Threatening, Fake News on Social Media, Other Offences (r/w IT Act), Fabrication of False Evidence/Destruction of Electronic Records for, Evidence, Offences By or Against Public Servant, False Electronic Evidence, Destruction of Electronic Evidence, Crimes of Property or Mark such as Counterfeiting, Tampering, Currency or Stamps, Crimes of Fraud such as Crimes related to Credit or Debit Card, Any Time Machines (ATMs), Online Banking Fraud, OTP Frauds, Crimes of Criminal Breach of Trust or Fraud such as crimes of Credit or Debit card, Crimes of Counterfeiting of Currecy, Stamps and Tampering, etc.

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...

This case study is part of a series on the ethics of social media data use by third parties.

In the United States, a number of challenges prevent an accurate assessment of the prevalence of hate crimes in different areas of the country. These challenges create huge gaps in knowledge about hate crime--who is targeted, how, and in what areas--which in turn hinder appropriate policy efforts and allocation of resources to the prevention of hate crime. In the absence of high-quality hate crime data, online platforms may provide information that can contribute to a more accurate estimate of the risk of hate crimes in certain places and against certain groups of people. Data on social media posts that use hate speech or internet search terms related to hate against specific groups has the potential to enhance and facilitate timely understanding of what is happening offline, outside of traditional monitoring (e.g., police crime reports). This study assessed the utility of Twitter data to illuminate the prevalence of hate crimes in the United States with the goals of (i) addressing the lack of reliable knowledge about hate crime prevalence in the U.S. by (ii) identifying and analyzing online hate speech and (iii) examining the links between the online hate speech and offline hate crimes. The project drew on four types of data: recorded hate crime data, social media data, census data, and data on hate crime risk factors. An ecological framework and Poisson regression models were adopted to study the explicit link between hate speech online and hate crimes offline. Risk terrain modeling (RTM) was used to further assess the ability to identify places at higher risk of hate crimes offline.

As of January 2025, the most significant data privacy violation fine worldwide was for social media giant Meta. In May 2023, the Data Protection Commission (DPC) of Ireland decided to fine the company with 1.2 billion euros or 1.3 billion U.S. dollars. The Chinese vehicle-for rent company Didi Global ranked second. In July 2022, China's data privacy regulator fined the company 8.026 billion Chinese yuan, or 1.19 billion U.S. dollars. The 2021 Amazon fine issued by Luxembourg's data privacy regulation authorities was 877 million U.S. dollars and was the third-biggest data breach fine as of the measured month. The 2019 fine of 575 million U.S. dollars to Equifax followed. In this incident, because of unpatched vulnerabilities, nearly 150 million people were affected, which caused the American consumer credit reporting agency to pay at least 575 million U.S. dollars.

The COVID-19 pandemic has resulted in the scapegoating of Asians, leading to a rise in hate crimes against them and adversely affecting their mental health and well-being. This project seeks to visualize the connection between structural racism, health, and anti-Asian Hate Crime (AHC) incidents reported in both the Federal Bureau of Investigation (FBI) and news and social media collected by the the Asian American Foundation (TAAF) during the period of 2020-2021. The project will first visualize the pattern of AHC incidents reported in FBI and media data, considering variations by states, counties, and months. This project will then identify state- or county-factors associated with AHC incidents and visualize them. The project will further examine changes in mental health and coping behaviors, following AHC incidents. The project will finally explore whether community-level AAPI organizations availability can mitigate the adverse effects of AHC incidents on mental health and coping behaviors.The project's target audience includes social change networks, media outlets, academic society, federal/state legislators, and national-level professional organizations. The resulting visualizations will facilitate policy debates surrounding various aspects, such as addressing underlying risk factors related to AHC, improving data collection and reporting methods within law enforcement agencies to accurately document AHC incidences in Federal/State archives, implementing policies and interventions to mitigate adverse effects on people's health, promoting understanding of cultural differences and inclusivity.

Crime Analytics Market Outlook

According to our latest research, the global crime analytics market size reached USD 9.2 billion in 2024, reflecting robust demand from both public and private sectors. The market is expected to grow at a strong CAGR of 14.6% during the forecast period, reaching a projected value of USD 29.1 billion by 2033. This impressive growth is primarily driven by the increasing adoption of advanced analytics and artificial intelligence by law enforcement agencies and other organizations seeking to enhance public safety, prevent crime, and improve operational efficiency. The proliferation of digital data, rising concerns about security threats, and the need for real-time actionable insights are further fueling the expansion of the crime analytics market globally.

One of the key growth factors propelling the crime analytics market is the rapid digital transformation across law enforcement and public safety organizations. As cities and communities become more interconnected and digitalized, the volume of data generated from surveillance systems, social media, financial transactions, and other sources has surged exponentially. This vast data landscape presents both challenges and opportunities for crime prevention and investigation. Crime analytics solutions leverage advanced technologies such as artificial intelligence, machine learning, and big data analytics to process, analyze, and interpret these massive datasets, enabling stakeholders to identify crime patterns, predict potential threats, and deploy resources more effectively. The increasing reliance on data-driven decision-making in policing and security operations is expected to continue driving market growth over the coming years.

Another significant driver for the crime analytics market is the growing need for proactive security measures in response to evolving and sophisticated criminal activities. With the rise of cybercrime, organized crime, and terrorism, traditional reactive approaches are no longer sufficient to ensure public safety. Crime analytics tools empower agencies to shift from reactive to proactive strategies by enabling predictive policing, real-time incident monitoring, and rapid response coordination. Advanced solutions such as geospatial analysis and social network analysis help authorities uncover hidden relationships, track criminal networks, and anticipate criminal behavior, thereby enhancing their ability to prevent crimes before they occur. The integration of crime analytics into homeland security, financial institutions, and other sectors further broadens the market’s scope and impact.

The increasing emphasis on inter-agency collaboration and information sharing is also contributing to the expansion of the crime analytics market. Governments and organizations worldwide recognize the importance of breaking down silos and fostering cross-functional partnerships to combat complex and transnational crimes. Crime analytics platforms facilitate seamless data exchange and collaborative investigations by providing centralized, secure, and interoperable solutions. These platforms support multi-agency task forces, joint operations, and intelligence-led policing initiatives, thereby improving overall crime-fighting capabilities. The adoption of cloud-based deployment models further enhances accessibility, scalability, and cost-effectiveness, making crime analytics solutions attractive to organizations of all sizes and resource levels.

Regionally, North America continues to dominate the global crime analytics market, accounting for the largest share in 2024, driven by substantial investments in public safety infrastructure, advanced technology adoption, and the presence of leading solution providers. Europe and Asia Pacific are also witnessing rapid growth, fueled by increasing security concerns, government initiatives, and the digitalization of law enforcement agencies. Emerging economies in Latin America and the Middle East & Africa are gradually embracing crime analytics solutions to address rising crime rates and improve public safety outcomes. The regional landscape is expected to remain dynamic, with tailored strategies and localized offerings playing a crucial role in market expansion.

Component Analysis

The crime analytics market is segmented by component into software, hardware, and services, each playing a pivotal role in the deployment and functionality of crime analytics solutions. Software forms the core of the marke

According to a survey conducted in Britain in August 2024, 33 percent of British adults aged between 18 and 24 years felt that social media companies should not be held responsible for posts that users made inciting criminal behavior during the recent riots in England. Brits aged over 65 years were most likely to say that social media companies should be held responsible for such posts during the far-right, anti-immigration riots that took place in July and August 2024. In the aftermath of the riots, violent disorder was the most common type of crime that offenders were charged with.

This data describes the use of the social media platform Facebook (http://www.facebook.com) by five (5) Massachusetts police departments over a three (3) month period from May 1st through July 31st, 2014. The five (5) police departments represented the towns/cities of Billerica, Burlington, Peabody, Waltham, and Wellesley. In addition to portraying these local trends, they demonstrate a methodology for systematically measuring social media use by government agencies or other organizations. This data was taken directly from Facebook using API’s provided by Facebook. The data includes all “wall posts” made by the representative police departments during this time period and includes data variables such as the text of the posting, the number of “likes” and “shares” (likes/shares represent features available on the Facebook social media platform), information about who performed the “like” or “share”, and comments others made in response to the “wall post”. There are 5 data files, one for each town represented. The number of variables vary per town depending on the post with the maximum number of certain features found in the row (for example, the top number of comments for one police department could be 20 while another could be 30 – the latter dataset would contain 10 more columns per row to account for the maximum possible). The data collected included the time from May 1st, 2014 through July 31st, 2014.

Crime Analytics Market Outlook

According to our latest research, the global Crime Analytics Market size in 2024 stood at USD 8.7 billion, demonstrating robust momentum across diverse industry verticals. The sector is experiencing a compound annual growth rate (CAGR) of 13.2% and is forecasted to reach a remarkable USD 25.2 billion by 2033. This sustained expansion is driven by a confluence of factors, including increasing investments in public safety infrastructure, the proliferation of advanced analytics technologies, and the escalating sophistication of criminal activities that necessitate innovative, data-driven solutions.

The primary growth catalyst for the Crime Analytics Market is the rapid digital transformation within law enforcement and public safety agencies. With the surge in urbanization and the emergence of smart cities, there is a heightened need for real-time monitoring, predictive analytics, and data integration to address complex crime patterns. The adoption of big data analytics, artificial intelligence, and machine learning algorithms has empowered agencies to identify crime hotspots, forecast criminal behavior, and allocate resources more efficiently. Furthermore, the rising incidence of cybercrime and financial fraud has compelled both government and private organizations to invest in sophisticated crime analytics platforms, thereby fueling market growth.

Another significant driver is the increasing focus on inter-agency collaboration and information sharing to combat organized crime and terrorism. Governments worldwide are implementing integrated crime analytics systems that enable seamless data exchange between various departments and jurisdictions. This interoperability not only enhances situational awareness but also accelerates investigative processes, leading to higher crime resolution rates. The availability of cloud-based crime analytics solutions has further democratized access to advanced analytical tools, allowing even resource-constrained agencies and small enterprises to leverage their benefits. Such developments are expected to sustain the upward trajectory of the market over the forecast period.

The proliferation of IoT devices, surveillance cameras, and social media platforms has exponentially increased the volume of data available for analysis. This data deluge presents both opportunities and challenges for crime analytics vendors. On one hand, it enables the development of more accurate predictive models and facilitates the early detection of criminal activities. On the other hand, it necessitates robust data management, security, and privacy protocols to ensure compliance with regulatory standards. As organizations strive to balance innovation with ethical considerations, the demand for scalable and secure crime analytics solutions is set to rise, further propelling market expansion.

From a regional perspective, North America currently dominates the Crime Analytics Market due to its advanced technological infrastructure and proactive government initiatives aimed at enhancing public safety. However, Asia Pacific is emerging as a high-growth region, driven by rapid urbanization, increasing crime rates, and significant investments in smart city projects. Europe, with its stringent data protection regulations and focus on cross-border security collaboration, also represents a substantial share of the global market. As countries across the globe prioritize crime prevention and response, the demand for comprehensive crime analytics solutions is expected to witness sustained growth across all regions.

Component Analysis

The Crime Analytics Market is segmented by component into Software, Hardware, and Services, with each playing a pivotal role in shaping the industry landscape. Software solutions constitute the largest share of the market, driven by the increasing adoption of advanced analytics platforms, visualization tools, and predictive modeling applications. These softw

This is the replication data for "Police agencies on Facebook overreport on Black suspects"

This data was obtained from https://mappingpoliceviolence.us/.

Mapping Police Violence is a 501(c)(3) organization that publishes the most comprehensive and up-to-date data on police violence in America to support transformative change.

This is a database set on openly sharing information on police violence in America.

Some information on this data according to their website: Our data has been meticulously sourced from official police use of force data collection programs in states like California, Texas and Virginia, combined with nationwide data from The Gun Violence Archive and the Fatal Encounters database, two impartial crowdsourced databases. We've also done extensive original research to further improve the quality and completeness of the data; searching social media, obituaries, criminal records databases, police reports and other sources to identify the race of 90 percent of all victims in the database.

We believe the data represented on this site is the most comprehensive accounting of people killed by police since 2013. Note that the Mapping Police Violence database is more comprehensive than the Washington Post police shootings database: while WaPo only tracks cases where people are fatally shot by on-duty police officers, our database includes additional incidents such as cases where police kill someone through use of a chokehold, baton, taser or other means as well as cases such as killings by off-duty police. A recent report from the Bureau of Justice Statistics estimated approximately 1,200 people were killed by police between June, 2015 and May, 2016. Our database identified 1,100 people killed by police over this time period. While there are undoubtedly police killings that are not included in our database (namely, those that go unreported by the media), these estimates suggest that our database captures 92% of the total number of police killings that have occurred since 2013. We hope these data will be used to provide greater transparency and accountability for police departments as part of the ongoing work to end police violence in America.

Law Enforcement Analytics Market Outlook

According to our latest research, the global law enforcement analytics market size reached USD 13.4 billion in 2024, driven by increasing adoption of advanced analytics and AI technologies in public safety operations. The market is expected to grow at a robust CAGR of 10.2% from 2025 to 2033, reaching a projected value of USD 34.3 billion by 2033. This impressive growth trajectory is underpinned by the rising need for data-driven decision-making in law enforcement, the proliferation of digital evidence, and the continuous evolution of cyber and physical crime landscapes.

A key growth factor for the law enforcement analytics market is the exponential increase in digital data generated from diverse sources, including surveillance cameras, social media, sensors, and mobile devices. Law enforcement agencies are increasingly leveraging analytics to process and interpret this massive influx of data, enabling them to identify patterns, predict criminal activity, and allocate resources more efficiently. The integration of artificial intelligence and machine learning into analytics platforms has further enhanced the ability to detect anomalies and correlate disparate data points, resulting in more proactive and informed policing. As criminal tactics become more sophisticated, the demand for advanced analytics tools that can quickly process and analyze complex datasets is expected to rise significantly, fueling market expansion.

Another major driver is the growing emphasis on public safety and homeland security, especially in urban areas experiencing rapid population growth and urbanization. Governments worldwide are investing heavily in upgrading law enforcement infrastructure, including digital forensics, predictive policing, and real-time surveillance systems. The adoption of cloud-based analytics solutions is also accelerating, as agencies seek scalable and flexible platforms that can support remote operations and facilitate inter-agency collaboration. Additionally, the increasing frequency of cybercrimes, terrorism, and organized crime has underscored the importance of timely intelligence and data sharing, further boosting the adoption of law enforcement analytics solutions across various jurisdictions.

Furthermore, regulatory mandates and compliance requirements are compelling law enforcement agencies to modernize their data management and reporting capabilities. The need to adhere to privacy laws, maintain accurate records, and ensure transparency in investigations is driving investments in comprehensive analytics platforms. These solutions not only streamline case management and evidence handling but also support accountability and public trust by providing auditable and traceable data trails. As public scrutiny of law enforcement practices intensifies, agencies are increasingly turning to analytics to demonstrate compliance, improve operational efficiency, and deliver measurable outcomes.

From a regional perspective, North America continues to dominate the law enforcement analytics market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States, in particular, has been at the forefront of adopting advanced analytics technologies, driven by substantial government funding, strong technology infrastructure, and a high incidence of both cyber and physical crimes. Europe is also witnessing steady growth, propelled by cross-border security initiatives and stringent regulatory frameworks. Meanwhile, Asia Pacific is emerging as a high-growth region, supported by rapid urbanization, increasing government investments in smart city projects, and a heightened focus on public safety and surveillance. Latin America and the Middle East & Africa are gradually catching up, with growing awareness of the benefits of analytics in combating organized crime and terrorism.

Component Analysis

The law enforcement analytics market is segmented by component into software and services, each playing a pivotal role in enhancing the capabilities of law enforcement agencies. The software segment encompasses a wide range of analytics platforms, including crime mapping, predictive analytics, social network analysis, and digital forensics tools. These software solutions are designed to aggregate, process, and analyze vast amounts of structured and unstructured data from multiple sources, providing actionable insights to investigators and decision-makers. The growing complexity of criminal activities and the increasing volu

Abstract The purpose of this article is to examine the influence of the media on perceptions of fear of crime among residents of the Brazilian Federal District. The study explores the influence of audience characteristics on the relationship between the media and fear of crime. We also analyzed the impact of different types of media (television, newspapers, and social media) on fear of crime. Research shows that the media's influence on fear of crime depends on the type of media, due to the differences in content and the characteristics of the audience.

QualifiedLeads: Lower cost per qualified lead when using social platforms with intent Cornerstone Content: 1-2 cornerstone videos per month LiveEvents: Quarterly live Q&As with recruiters and recent graduates