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.

Version 3 with 517M hashes and counts of password usage ordered by most to least prevalent Pwned Passwords are 517,238,891 real world passwords previously exposed in data breaches. This exposure makes them unsuitable for ongoing use as they re at much greater risk of being used to take over other accounts. They re searchable online below as well as being downloadable for use in other online system. The entire set of passwords is downloadable for free below with each password being represented as a SHA-1 hash to protect the original value (some passwords contain personally identifiable information) followed by a count of how many times that password had been seen in the source data breaches. The list may be integrated into other systems and used to verify whether a password has previously appeared in a data breach after which a system may warn the user or even block the password outright.

Data breaches cost companies and businesses a lot of money. The average cost of a data breach is $3.86 million.

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 average cyber attack takes 280 days to identify and contain and it costs an average of about $3.86 million to deal with properly.

In this document, comprehensive datasets are presented to advance research on information security breaches. The datasets include data on disclosed information security breaches affecting S&P500 companies between 2020 and 2023, collected through manual search of the Internet. Overall, the datasets include 504 companies, with detailed information security breach and financial data available for 97 firms that experienced a disclosed information security breach. This document will describe the datasets in detail, explain the data collection procedure and shows the initial versions of the datasets. Contact at Tilburg University Francesco Lelli

As of 2024, the mean number of days to identify the data breaches was *** days, four percent less than in the previous year. The mean time companies needed to contain the breaches in 2024 was ** days. In comparison, in 2022, it took organizations *** days to identify and ** days to address the data breaches.

Between 2004 and October 2024, the United States recorded the highest number of data points leaked online. Overall, more than 17 billion data points were leaked in the country during the measured period. Russia ranked second, with more than four billion leaked data points.

These cybersecurity statistics will help you understand the state of online security and give you a better idea of what it takes to protect yourself.

Analysis of ‘List of Top Data Breaches (2004 - 2021)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/hishaamarmghan/list-of-top-data-breaches-2004-2021 on 14 February 2022.

--- Dataset description provided by original source is as follows ---

This is a dataset containing all the major data breaches in the world from 2004 to 2021

As we know, there is a big issue related to the privacy of our data. Many major companies in the world still to this day face this issue every single day. Even with a great team of people working on their security, many still suffer. In order to tackle this situation, it is only right that we must study this issue in great depth and therefore I pulled this data from Wikipedia to conduct data analysis. I would encourage others to take a look at this as well and find as many insights as possible.

This data contains 5 columns: 1. Entity: The name of the company, organization or institute 2. Year: In what year did the data breach took place 3. Records: How many records were compromised (can include information like email, passwords etc.) 4. Organization type: Which sector does the organization belong to 5. Method: Was it hacked? Were the files lost? Was it an inside job?

Here is the source for the dataset: https://en.wikipedia.org/wiki/List_of_data_breaches

Here is the GitHub link for a guide on how it was scraped: https://github.com/hishaamarmghan/Data-Breaches-Scraping-Cleaning

--- Original source retains full ownership of the source dataset ---

500k User Session Collection This collection is distributed for NON-COMMERCIAL RESEARCH USE ONLY. Any application of this collection for commercial purposes is STRICTLY PROHIBITED. #### Brief description: This collection consists of ~20M web queries collected from ~650k users over three months. The data is sorted by anonymous user ID and sequentially arranged. The goal of this collection is to provide real query log data that is based on real users. It could be used for personalization, query reformulation or other types of search research. The data set includes AnonID, Query, QueryTime, ItemRank, ClickURL. AnonID - an anonymous user ID number. Query - the query issued by the user, case shifted with most punctuation removed. QueryTime - the time at which the query was submitted for search. ItemRank - if the user clicked on a search result, the rank of the item on which they clicked is listed. ClickURL - if the user clicked on a search result, the domain portion of the URL i

Objective: The rapid adoption of health information technology (IT) coupled with growing reports of ransomware, and hacking has made cybersecurity a priority in health care. This study leverages federal data in order to better understand current cybersecurity threats in the context of health IT.

Materials and Methods: Retrospective observational study of all available reported data breaches in the United States from 2013 to 2017, downloaded from a publicly available federal regulatory database.

Results: There were 1512 data breaches affecting 154 415 257 patient records from a heterogeneous distribution of covered entities (P < .001). There were 128 electronic medical record-related breaches of 4 867 920 patient records, while 363 hacking incidents affected 130 702 378 records.

Discussion and Conclusion: Despite making up less than 25% of all breaches, hacking was responsible for nearly 85% of all affected patient records. As medicine becomes increasingly interconnected and ...

Hurricane Sandy made U.S. landfall, coincident with astronomical high tides, near Atlantic City, New Jersey, on October 29, 2012. The storm, the largest on historical record in the Atlantic basin, affected an extensive area of the east coast of the United States. The highest waves and storm surge were focused along the heavily populated New York and New Jersey coasts. At the height of the storm, a record significant wave height of 9.6 meters (m) was recorded at the wave buoy offshore of Fire Island, New York. During the storm an overwash channel opened a breach in the _location of Old Inlet, in the Otis Pike High Dunes Wilderness Area. This breach is referred to as the wilderness breach (fig 1). Fire Island, New York is the site of a long term coastal morphologic change and processes project conducted by the U.S. Geological Survey (USGS). One of the objectives of the project was to understand the morphologic evolution of the barrier system on a variety of time scales (days - years - decades - centuries). In response to Hurricane Sandy, this effort continued with the intention of resolving storm impact and the response and recovery of the beach. The day before Hurricane Sandy made landfall (October 28, 2012), a USGS field team conducted differential global positioning system (DGPS) surveys at Fire Island to quantify the pre-storm morphologic state of the beach and dunes. The area was re-surveyed after the storm, as soon as access to the island was possible. In order to fully capture the recovery of the barrier system, the USGS Hurricane Sandy Supplemental Fire Island Study was established to include collection in the weeks, months, and years following the storm. As part of the USGS Hurricane Sandy Supplemental Fire Island Study, the beach is monitored periodically to enable better understanding of post-Sandy recovery. The alongshore state of the beach is recorded using a DGPS to collect data around the mean high water elevation (MHW; 0.46 meter North American Vertical Datum of 1988) to derive a shoreline, and the cross-shore response and recovery are measured along a series of 15 profiles. Monitoring continued in the weeks following Hurricane Sandy with additional monthly collection through April 2013 and repeat surveys every 2–3 months thereafter until October 2014. Bi-annual surveys have been collected through September 2016. Beginning in October 2014 the USGS also began collecting shoreline data at the Wilderness breach. The shoreline collected was an approximation of the MHW shoreline. The operator walked an estimated MHW elevation above the water line and below the berm crest, using knowledge of tides and local conditions to interpret a consistent shoreline. See below for survey collection dates for all data types. This shapefile FIIS_Breach_Shorelines.shp consists of Fire Island, NY breach shorelines collected following an interpreted MHW shoreline as identified in the field. Oct 28 2012 (MHW shoreline/Cross-shore data) Nov 01 2012 (MHW shoreline/Cross-shore data) Nov 04 2012 (Cross-shore data only) Dec 01 2012 (MHW shoreline/Cross-shore data) Dec 12 2012 (MHW shoreline/Cross-shore data) Jan 10 2013 (MHW shoreline/Cross-shore data) Feb 13 2013 (MHW shoreline/Cross-shore data) Mar 13 2013 (MHW shoreline/Cross-shore data) Apr 09 2013 (MHW shoreline/Cross-shore data) Jun 24 2013 (MHW shoreline/Cross-shore data) Sep 18 2013 (MHW shoreline/Cross-shore data) Dec 03 2013 (MHW shoreline/Cross-shore data) Jan 29 2014 (MHW shoreline/Cross-shore data) Jun 11 2014 (Cross-shore data only) Sep 09 2014 (MHW shoreline/Cross-shore data) Oct 07 2014 (Cross-shore data/MHW Breach shoreline) Jan 21 2015 (MHW shoreline/Cross-shore data/Breach shoreline) Mar 19 2015 (MHW shoreline/Cross-shore data) May 16 2015 (MHW shoreline/Cross-shore data/Breach shoreline) Set 28 2015 (MHW shoreline/Cross-shore data/Breach shoreline) Jan 21 2016 (MHW shoreline/Cross-shore data) Jan 25 2016 (MHW shoreline/Cross-shore data) Apr 06 2016 (Cross-shore data only) Apr 11 2016 (MHW shoreline/Cross-shore data/Breach shoreline) Jun 16 2016 (Cross-shore data only) Sep 27 2016 (MHW shoreline/Cross-shore data/Breach shoreline)

Full title: Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine Mark Schwabacher, NASA Ames Research Center Robert Aguilar, Pratt & Whitney Rocketdyne Fernando Figueroa, NASA Stennis Space Center Abstract The goal of this work was to use data-driven methods to automatically detect and isolate faults in the J-2X rocket engine. It was decided to use decision trees, since they tend to be easier to interpret than other data-driven methods. The decision tree algorithm automatically “learns” a decision tree by performing a search through the space of possible decision trees to find one that fits the training data. The particular decision tree algorithm used is known as C4.5. Simulated J-2X data from a high-fidelity simulator developed at Pratt & Whitney Rocketdyne and known as the Detailed Real-Time Model (DRTM) was used to “train” and test the decision tree. Fifty-six DRTM simulations were performed for this purpose, with different leak sizes, different leak locations, and different times of leak onset. To make the simulations as realistic as possible, they included simulated sensor noise, and included a gradual degradation in both fuel and oxidizer turbine efficiency. A decision tree was trained using 11 of these simulations, and tested using the remaining 45 simulations. In the training phase, the C4.5 algorithm was provided with labeled examples of data from nominal operation and data including leaks in each leak location. From the data, it “learned” a decision tree that can classify unseen data as having no leak or having a leak in one of the five leak locations. In the test phase, the decision tree produced very low false alarm rates and low missed detection rates on the unseen data. It had very good fault isolation rates for three of the five simulated leak locations, but it tended to confuse the remaining two locations, perhaps because a large leak at one of these two locations can look very similar to a small leak at the other location. Introduction The J-2X rocket engine will be tested on Test Stand A-1 at NASA Stennis Space Center (SSC) in Mississippi. A team including people from SSC, NASA Ames Research Center (ARC), and Pratt & Whitney Rocketdyne (PWR) is developing a prototype end-to-end integrated systems health management (ISHM) system that will be used to monitor the test stand and the engine while the engine is on the test stand[1]. The prototype will use several different methods for detecting and diagnosing faults in the test stand and the engine, including rule-based, model-based, and data-driven approaches. SSC is currently using the G2 tool http://www.gensym.com to develop rule-based and model-based fault detection and diagnosis capabilities for the A-1 test stand. This paper describes preliminary results in applying the data-driven approach to detecting and diagnosing faults in the J-2X engine. The conventional approach to detecting and diagnosing faults in complex engineered systems such as rocket engines and test stands is to use large numbers of human experts. Test controllers watch the data in near-real time during each engine test. Engineers study the data after each test. These experts are aided by limit checks that signal when a particular variable goes outside of a predetermined range. The conventional approach is very labor intensive. Also, humans may not be able to recognize faults that involve the relationships among large numbers of variables. Further, some potential faults could happen too quickly for humans to detect them and react before they become catastrophic. Automated fault detection and diagnosis is therefore needed. One approach to automation is to encode human knowledge into rules or models. Another approach is use data-driven methods to automatically learn models from historical data or simulated data. Our prototype will combine the data-driven approach with the model-based and rule-based appro

Did the COVID-19 pandemic really affect cybersecurity? Short answer – Yes. Cybercrime is up 600% due to COVID-19.

Some industries are affected by cyber attacks more than others. These next cybersecurity statistics detail specifically who is affected by cyber-attacks and why they are.

Over 1.1 billion personal data points were exposed during breaches in Russia in 2023. That was the highest figure over the observed period. To compare, in the previous year, the number of data points exposed stood at approximately 770 million.

The Asia-Pacific data center physical security market is experiencing robust growth, projected to reach $0.53 billion in 2025 and expand at a Compound Annual Growth Rate (CAGR) of 14.50% from 2025 to 2033. This surge is fueled by the increasing adoption of cloud computing, the proliferation of data centers across the region, and heightened concerns about data breaches and cyber threats. Key drivers include stringent government regulations mandating enhanced data security, the rising adoption of advanced security technologies like video surveillance, access control systems, and integrated security solutions, and the growing need for robust physical security infrastructure to protect against both internal and external threats. The market is segmented by solution type (video surveillance, access control, and others), service type (consulting, professional, and system integration services), and end-user sectors (IT & telecommunications, BFSI, government, healthcare, and others). The Asia-Pacific region, particularly countries like China, Japan, South Korea, and India, is witnessing significant investments in data center infrastructure, creating lucrative opportunities for physical security vendors. Market leaders like Axis Communications, Dahua Technology, and Bosch are actively expanding their presence in this rapidly evolving landscape. While the market presents significant opportunities, challenges remain. The high initial investment costs associated with implementing sophisticated security systems can act as a restraint, particularly for smaller data centers. Furthermore, the complexities of integrating various security technologies and managing a diverse range of security solutions pose challenges for data center operators. However, the increasing awareness of data security risks and the availability of cost-effective financing options are likely to mitigate these restraints. The market's future growth is expected to be driven by the adoption of AI-powered security solutions, the growing demand for managed security services, and the increasing focus on improving operational efficiency and minimizing downtime through integrated security solutions. The strong economic growth and rising digitalization in the Asia-Pacific region will further accelerate this market's expansion in the forecast period. This comprehensive report provides an in-depth analysis of the Asia-Pacific data center physical security market, covering the period 2019-2033. With a focus on the estimated year 2025 and a forecast period extending to 2033, this study offers invaluable insights for businesses operating in this rapidly expanding sector. The report leverages data from the historical period (2019-2024) to provide a robust understanding of market trends and future projections, valued in millions of units. Key search terms like data center security, Asia-Pacific security market, physical security solutions, access control systems, and video surveillance are integrated throughout to ensure maximum search engine visibility. Recent developments include: August 2023: Securitas signed an expanded 5-year agreement to provide data center security for Microsoft in 31 countries (including APAC countries), solidifying a strong relationship. The global agreement includes risk management, comprehensive security technology as a system integrator, specialised safety, and security resources, guarding services and digital interfaces. Securitas ensures that the data center physical security program remains innovative, robust, and effective. This demonstrates stability as a collaborator, assisting in navigating the challenges of Microsoft's expanding business., August 2023: Metrasens announced its partnership with Convergint. Through this partnership, Metrasens will provide its advanced detection systems through Convergint’s portfolio offering to its customers.. Key drivers for this market are: Increased Data Center Activities and Investment by the Hyperscale and Colocation Operators, Advancements in Video Surveillance Systems Connected to Cloud Systems. Potential restraints include: Increased Data Center Activities and Investment by the Hyperscale and Colocation Operators, Advancements in Video Surveillance Systems Connected to Cloud Systems. Notable trends are: The IT & Telecom Segment is Expected to Hold Significant Share.

Passwords that were leaked or stolen from sites. The Rockyou Dataset is about 14 million passwords.