The largest reported data leakage as of January 2024 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.

View Data Breach Notification Reports, which include how many breaches are reported each year and the number of affected residents.

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

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.

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.

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.

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 Data files: 6 raw Microsoft Excel files (.xls) Supplemental material: Data_Publication_Package.pdf Detailed description of the data has been released in the following preprint: [Preprint in progress] Structure data package The folder contains the 6 .xls documents, the data publication package. Link to the preprint describing the dataset is in the description of the dataset itself. The six .xls documents are also present in their preferred file format csv (see Notes for further explanation). Production date: 01-2024---- 05-2024 Method: Data on information security breaches through manual search of the Internet, financial data through Refinitiv (LSEG). (Approval obtained from Refinitiv to publish these data) Universe: S&P500 companies Country / Nation: USA

With the surge in data collection and analytics, concerns are raised with regards to the privacy of the individuals represented by the data. In settings where the data is distributed over several data holders, federated learning offers an alternative to learn from the data without the need to centralize it in the first place. This is achieved by exchanging only model parameters learned locally at each data holder. This greatly limits the amount of data to be transferred, reduces the impact of data breaches, and helps to preserve the individual’s privacy. Federated learning thus becomes a viable alternative in IoT and Edge Computing settings, especially if the data collected is sensitive. However, risks for data or information leaks still persist, if information can be inferred from the models exchanged. This can e.g. be in the form of membership inference attacks. In this paper, we investigate how successful such attacks are in the setting of sequential federated learning. The cyclic nature of model learning and exchange might enable attackers with more information to observe the dynamics of the learning process, and thus perform a more powerful attack.

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)

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.

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

The reverse phone number lookup market is experiencing robust growth, driven by increasing concerns over online safety, identity theft, and the need for efficient background checks. The market, estimated at $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several factors, including the rising adoption of smartphones, increased online interactions, and the growing sophistication of online scams and harassment. The cloud-based segment dominates the market due to its scalability, accessibility, and cost-effectiveness compared to on-premise solutions. The enterprise application segment is also showing significant traction, driven by businesses' need to verify customer identities and screen potential employees. While data privacy regulations pose a challenge, the demand for enhanced security and verification services continues to propel market expansion. The diverse range of service providers, from established players like TruthFinder and Intelius to newer entrants, fosters competition and innovation within the sector. Geographical expansion, particularly in developing economies with rapidly increasing internet penetration, offers considerable growth opportunities. The North American market currently holds the largest share due to high internet penetration and a strong awareness of identity theft prevention. However, Asia-Pacific and Europe are projected to witness significant growth in the coming years, fueled by rising smartphone adoption and increasing concerns about online security. The market is segmented by deployment (cloud-based and on-premise) and application (personal and enterprise). The competitive landscape features a mix of established players and emerging companies, continuously innovating to enhance accuracy, speed, and data privacy features. While challenges exist around data privacy and regulatory compliance, the overall market outlook remains optimistic, with continued growth anticipated throughout the forecast period.

The world has entered the era of the Code War where every digital device, however small and innocuous, can be “weaponised” – as the recent Dyn cyber-attack aptly illustrated – to send “rogue code” deep into the Internet's engine room to create mayhem.
Cybersecurity is critical to almost every business. Yet it is a non-core competence for most boards. The frequency of high profile corporate data breaches may accelerate because CEOs are not sufficiently trained in cyber risks.
Almost every cyber-breach is an “inside job” – whether malicious or accidental – so real-time behavioural analytics is becoming increasingly important as a defense.
Insidt this report, we look at the evolution, nature, growth in cybersecurity technologies and threat. Read More

2768 Global exporters importers export import shipment records of Helium leak detector with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

264 Global exporters importers export import shipment records of Leak detector and Hsn Code 3403 with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

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

20 to 30% of drinking water produced is lost due to leaks in water distribution pipes. In times of water scarcity, losing so much treated water comes at a significant cost, both environmentally and economically. In this paper, we propose a hybrid leak localization approach combining both model-based and data-driven modeling. Pressure heads of leak scenarios are simulated using a hydraulic model, and then used to train a machine-learning based leak localization model. A key element of our approach is that discrepancies between simulated and measured pressures are accounted for using a dynamically calculated bias correction, based on historical pressure measurements. Data of in-field leak experiments in operational water distribution networks were produced to evaluate our approach on realistic test data. Two problematic settings for leak localization were examined. In the first setting, an uncalibrated hydraulic model was used. In the second setting, an extended version of the water distribution network was considered, where large parts of the network were insensitive to leaks. Our results show that the leak localization model is able to reduce the leak search region in parts of the network where leaks induce detectable drops in pressure. When this is not the case, the model still localizes the leak but is able to indicate a higher level of uncertainty with respect to its leak predictions.

The global Data Privacy Compliance Services market is experiencing robust growth, driven by increasingly stringent data protection regulations like GDPR, CCPA, and others worldwide. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This expansion is fueled by rising cyber threats, heightened consumer awareness of data privacy, and the escalating penalties for non-compliance. Key market segments include Privacy Risk Assessment, Technical Assurance Assessment, Breach Response Assessment, and Privacy Compliance Consulting Services, with large enterprises currently dominating the application segment. However, the increasing adoption of cloud-based solutions and growing data volumes among SMEs are expected to boost demand for these services within this segment. Leading companies like RSM, ACA Group, and Clarip are at the forefront of this expanding market, leveraging their expertise in regulatory compliance and cybersecurity to offer comprehensive solutions. North America and Europe currently hold significant market share, owing to advanced technological infrastructure and stringent data protection laws; however, other regions, especially Asia-Pacific, are exhibiting strong growth potential as data privacy regulations mature and digitalization accelerates. The significant growth trajectory of the Data Privacy Compliance Services market is further propelled by the increasing demand for specialized services tailored to specific industries, such as healthcare and finance, which handle sensitive personal information. The market's expansion is also closely linked to evolving technological advancements, such as artificial intelligence (AI) and machine learning (ML), used to enhance data privacy and security solutions. While the market faces restraints such as the high cost of implementation and a shortage of skilled professionals, the escalating consequences of data breaches and regulatory fines serve as compelling incentives for organizations to invest in robust data privacy compliance strategies, thus driving market growth. The competitive landscape is characterized by both established players and emerging niche providers, leading to increased innovation and the diversification of services offered. This dynamic environment ensures the continued expansion of the Data Privacy Compliance Services market in the coming years. I cannot provide direct hyperlinks to company websites due to my limitations as a large language model. However, I can provide the report description you requested, incorporating the company names and segmentations you supplied. You can easily find their websites using a search engine.

Pipelines transport natural gas (NG) in all stages between production and the end user. The NG composition, pipeline depth, and pressure vary significantly between extraction and consumption. As methane (CH4Â), the primary component of NG is both explosive and a potent greenhouse gas, NG leaks from underground pipelines pose both a safety and environmental threat. Leaks are typically found when an observer detects a CH4 enhancement as they pass through the downwind above-ground NG plume. The likelihood of detecting a plume depends, in part, on the size of the plume, which is contingent on both environmental conditions and intrinsic characteristics of the leak. To investigate the effects of leak characteristics, this study uses controlled NG release experiments to observe how the above-ground plume width changes with changes in the gas composition of the NG, leak rate, and depth of the subsurface emission. Results show that plume width generally decreases when heavier hydrocarbons are pr...

Revenue in the cybersecurity industry worldwide reached $146.32 billion in 2022.