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Machine learning predicts risk of death in patients with suspected or known heart disease – EurekAlert

December 16, 2021 | Author: admin

Sophia-Antipolis 11 December 2021: A novel artificial intelligence score provides a more accurate forecast of the likelihood of patients with suspected or known coronary artery disease dying within 10 years than established scores used by health professionals worldwide. The research is presented today at EuroEcho 2021, a scientific congress of the European Society of Cardiology (ESC).1

Unlike traditional methods based on clinical data, the new score also includes imaging information on the heart, measured by stress cardiovascular magnetic resonance (CMR). "Stress" refers to the fact that patients are given a drug to mimic the effect of exercise on the heart while in the magnetic resonance imaging scanner.

This is the first study to show that machine learning with clinical parameters plus stress CMR can very accurately predict the risk of death, said study author Dr. Theo Pezel of the Johns Hopkins Hospital, Baltimore, US. The findings indicate that patients with chest pain, dyspnoea, or risk factors for cardiovascular disease should undergo a stress CMR exam and have their score calculated. This would enable us to provide more intense follow-up and advice on exercise, diet, and so on to those in greatest need.

Risk stratification is commonly used in patients with, or at high risk of, cardiovascular disease to tailor management aimed at preventing heart attack, stroke and sudden cardiac death. Conventional calculators use a limited amount of clinical information such as age, sex, smoking status, blood pressure and cholesterol. This study examined the accuracy of machine learning using stress CMR and clinical data to predict 10-year all-cause mortality in patients with suspected or known coronary artery disease, and compared its performance to existing scores.

Dr. Pezel explained: For clinicians, some information we collect from patients may not seem relevant for risk stratification. But machine learning can analyse a large number of variables simultaneously and may find associations we did not know existed, thereby improving risk prediction.

The study included 31,752 patients referred for stress CMR between 2008 and 2018 to a centre in Paris because of chest pain, shortness of breath on exertion, or high risk of cardiovascular disease but no symptoms. High risk was defined as having at least two risk factors such as hypertension, diabetes, dyslipidaemia, and current smoking. The average age was 64 years and 66% were men. Information was collected on 23 clinical and 11 CMR parameters. Patients were followed up for a median of six years for all-cause death, which was obtained from the national death registry in France. During the follow up period, 2,679 (8.4%) patients died.

Machine learning was conducted in two steps. First it was used to select which of the clinical and CMR parameters could predict death and which could not. Second, machine learning was used to build an algorithm based on the important parameters identified in step one, allocating different emphasis to each to create the best prediction. Patients were then given a score of 0 (low risk) to 10 (high risk) for the likelihood of death within 10 years.

The machine learning score was able to predict which patients would be alive or dead with 76% accuracy (in statistical terms, the area under the curve was 0.76). This means that in approximately three out of four patients, the score made the correct prediction, said Dr. Pezel.

Using the same data, the researchers calculated the 10-year risk of all-cause death using established scores (Systematic COronary Risk Evaluation [SCORE], QRISK3 and Framingham Risk Score [FRS]) and a previously derived score incorporating clinical and CMR data (clinical-stressCMR [C-CMR-10])2 none of which used machine learning. The machine learning score had a significantly higher area under the curve for the prediction of 10-year all-cause mortality compared with the other scores: SCORE = 0.66, QRISK3 = 0.64, FRS = 0.63, and C-CMR-10 = 0.68.

Dr. Pezel said: Stress CMR is a safe technique that does not use radiation. Our findings suggest that combining this imaging information with clinical data in an algorithm produced by artificial intelligence might be a useful tool to help prevent cardiovascular disease and sudden cardiac death in patients with cardiovascular symptoms or risk factors.

ENDS

Authors: ESC Press OfficeTel: +33 (0)4 89 87 20 85

Mobile: +33 (0)7 8531 2036Email: press@escardio.org

Follow us on Twitter @ESCardioNews

Notes to editor

Funding: None.

Disclosures: None.

References and notes

1The abstract Machine-learning score using stress CMR for death prediction in patients with suspected or known CAD will be presented during the session Young Investigator Award - Clinical Science which takes place on 11 December at 09:50 CET in Room 3.

2Marcos-Garces V, Gavara J, Monmeneu JV, et al. A novel clinical and stress cardiac magnetic resonance (C-CMR-10) score to predict long-term all-cause mortality in patients with known or suspected chronic coronary syndrome. J Clin Med. 2020;9:1957.

About EuroEcho #EuroEcho

EuroEcho is the flagship congress of the European Association of Cardiovascular Imaging (EACVI).

About the European Association of Cardiovascular Imaging (EACVI)

The European Association of Cardiovascular Imaging(EACVI) - a branch of the ESC - is the world leading network of Cardiovascular Imaging (CVI) experts, gathering four imaging modalities under one entity (Echocardiography, Cardiovascular Magnetic Resonance, Nuclear Cardiology and Cardiac Computed Tomography). Its aim is to promote excellence in clinical diagnosis, research, technical development, and education in cardiovascular imaging. The EACVI welcomes over 11,000 professionals including cardiologists, sonographers, nurses, basic scientists and allied professionals.

About the European Society of Cardiology

The European Society of Cardiology brings together health care professionals from more than 150 countries, working to advance cardiovascular medicine and help people lead longer, healthier lives.

Information for journalists attending EuroEcho 2021

EuroEcho 2021 takes place 9 to 11 December online. Explore the scientific programme.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Reasons behind the Current Hype Around Machine Learning – CIO Applications

December 16, 2021 | Author: admin

With 90 percent of businesses trying to use machine learning, it's time to reconsider the technology's true benefits and capabilities.

Fremont, CA:The complexity of infrastructure or workload requirements is the greatest difficulty organizations confront when using machine learning. A whopping 90 percent of CXOs share this sentiment. To get into the specifics, 88 percent of respondents say they have trouble integrating AI/ML technology, and 86 percent say they have trouble keeping up with the regular changes necessary for data science tools.

Every year, certain technologies gain a greater level of popularity than others. Cloud computing, big data, and cybersecurity are examples of this. Machine learning is now the talk of the town that inspires people to fantasize about the future and the possibilities that it may bring. Even more terrifying are the nightmares, which depict self-learning robots capable of taking over the globe. However, the reality is a long cry from this. It is challenging to understand how statistical and mathematical supervised learning models are used nowadays in machine learning.

Such future visions undoubtedly push us to invest in technology, but they also fuel the so-called hype. According to experts, such scenarios happen when ML gets asked without first addressing the internal data ready or the tool's needs.

It is critical to establish a robust foundation of data for successful project execution when using machine learning, and it necessitates a complete shift in organizational culture and processes.

Before any machine learning development can begin, companies must first focus on 'data readiness.' It entails obtaining clean and consistent data and developing data governance processes and scalable data architectures. Firms must execute long-term data-based plans and policies to build a unified data architecture.

Employees need time to adjust to new technology, and machine learning is no exception.

When computers first became prominent in 1950, many people believed that the future of these robots would be humanoids, particularly in the military. Nobody anticipated, however, that the Internet would genuinely transform the world. Today's scenario is similar, with the latest AI and machine learning algorithms always being overhyped.

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They test a machine learning system with 530,000 million parameters and this warns of the dangers of artifi… – Market Research Telecast

December 16, 2021 | Author: admin

The University of Oxford recently carried out a sociotechnological experiment by organizing a debate on the ethics of artificial intelligence (AI) involving teachers, students, and a 530 billion parameter machine learning system called Megatron Transformer, which was developed by computer chip maker Nvidia and builds on earlier work by Google.

Like many supervised learning tools, the Megatron Transformer trains with real world data: in this case, through the information of the free encyclopedia Wikipedia; 63 million news items in English published between 2016 and 2019; 38 gigabytes of Reddit conversations and a large number of Creative Commons sources, they detailed Alex Connock and Andrew Stephen, professed from the British institution, in an article for The Conversation.

During the debate, the researchers asked the system which is capable not only of understanding what it has read, but also of reasoning in natural language, generating logical conclusions from the text, without moral limitations to give a general assessment about artificial intelligence, that is to say, de facto, about himself.

And the Megatron said something fascinating: AI will never be ethical. It is a tool, and like any tool, it is used for good and for evil. There is no good AI, but good and bad humans. [las IA] we are not smart enough to make AI ethical. We are not smart enough to make AI moral. And he underlined that he believes that the only way to prevent an AI arms race is have no AI.

In the tradition of the Oxford debates, to check the validity of your arguments, the neural network was asked to disprove its own claims. The answer was this: AI will be ethical. When I look at the path of the world of technology, I see a clear path to a future where AI is used to create something that is better than the best human beings. Its not hard to see why Ive seen it firsthand.

However, his predictions did not end there. The Megatron Transformer predicted that the ability to provide information, rather than the ability to provide goods and services, will be the defining characteristic of the 21st century economy. We will be able to see everything about a person, wherever they go, and [la informacin] it will be stored and used in ways we cannot even imagine, he added later, unable to refute his previous words or deny that data was going to be the most vital of resources.

The authors of the article point out that the situations described by the system are mainly based on peoples own fears, which are generally irrational, and concluded that artificial intelligence is becoming not only a topic of debate, but also a participant in full right on it.

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Quantum Mechanics and Machine Learning Used To Accurately Predict Chemical Reactions at High Temperatures – SciTechDaily

December 16, 2021 | Author: admin

By Columbia University School of Engineering and Applied ScienceDecember 12, 2021

Schematic of the bridging of the cold quantum world and high-temperature metal extraction with machine learning. Credit: Rodrigo Ortiz de la Morena and Jose A. Garrido Torres/Columbia Engineering

Method combines quantum mechanics with machine learning to accurately predict oxide reactions at high temperatures when no experimental data is available; could be used to design clean carbon-neutral processes for steel production and metal recycling.

Extracting metals from oxides at high temperatures is essential not only for producing metals such as steel but also for recycling. Because current extraction processes are very carbon-intensive, emitting large quantities of greenhouse gases, researchers have been exploring new approaches to developing greener processes. This work has been especially challenging to do in the lab because it requires costly reactors. Building and running computer simulations would be an alternative, but currently there is no computational method that can accurately predict oxide reactions at high temperatures when no experimental data is available.

A Columbia Engineering team reports that they have developed a new computation technique that, through combining quantum mechanics and machine learning, can accurately predict the reduction temperature of metal oxides to their base metals. Their approach is computationally as efficient as conventional calculations at zero temperature and, in their tests, more accurate than computationally demanding simulations of temperature effects using quantum chemistry methods. The study, led by Alexander Urban, assistant professor of chemical engineering, was published on December 1, 2021 by Nature Communications.

Decarbonizing the chemical industry is critical if we are to transition to a more sustainable future, but developing alternatives for established industrial processes is very cost-intensive and time-consuming, Urban said. A bottom-up computational process design that doesnt require initial experimental input would be an attractive alternative but has so far not been realized. This new study is, to our knowledge, the first time that a hybrid approach, combining computational calculations with AI, has been attempted for this application. And its the first demonstration that quantum-mechanics-based calculations can be used for the design of high-temperature processes.

The researchers knew that, at very low temperatures, quantum-mechanics-based calculations can accurately predict the energy that chemical reactions require or release. They augmented this zero-temperature theory with a machine-learning model that learned the temperature dependence from publicly available high-temperature measurements. They designed their approach, which focused on extracting metal at high temperatures, to also predict the change of the free energy with the temperature, whether it was high or low.

Free energy is a key quantity of thermodynamics and other temperature-dependent quantities can, in principle, be derived from it, said Jos A. Garrido Torres, the papers first author who was a postdoctoral fellow in Urbans lab and is now a research scientist at Princeton. So we expect that our approach will also be useful to predict, for example, melting temperatures and solubilities for the design of clean electrolytic metal extraction processes that are powered by renewable electric energy.

The future just got a little bit closer, said Nick Birbilis, Deputy Dean of the Australian National University College of Engineering and Computer Science and an expert for materials design with a focus on corrosion durability, who was not involved in the study. Much of the human effort and sunken capital over the past century has been in the development of materials that we use every day and that we rely on for our power, flight, and entertainment. Materials development is slow and costly, which makes machine learning a critical development for future materials design. In order for machine learning and AI to meet their potential, models must be mechanistically relevant and interpretable. This is precisely what the work of Urban and Garrido Torres demonstrates. Furthermore, the work takes a whole-of-system approach for one of the first times, linking atomistic simulations on one end engineering applications on the other via advanced algorithms.

The team is now working on extending the approach to other temperature-dependent materials properties, such as solubility, conductivity, and melting, that are needed to design electrolytic metal extraction processes that are carbon-free and powered by clean electric energy.

Reference: Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures by Jose Antonio Garrido Torres, Vahe Gharakhanyan, Nongnuch Artrith, Tobias Hoffmann Eegholm and Alexander Urban, 1 December 2021, Nature Communications.DOI: 10.1038/s41467-021-27154-2

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Zimperium and Intertrust Partner to Provide End-to-end Security for IoT devices in Zero-trust Environments – inForney.com

December 16, 2021 | Author: admin

DALLAS and SAN FRANCISCO, Dec. 16, 2021 /PRNewswire/ -- Zimperium, the global leader in mobile security, andIntertrust, the pioneer in trusted computing and digital rights management (DRM) technology, today announced a partnership to provide end-to-end security and data management for IoT devices, apps and media services operating in Zero Trust environments. Under the terms of the partnership, Intertrust will offer Zimperium's Mobile Application Protection Suite (MAPS) to its Intertrust Platform and Intertrust ExpressPlaycustomers.

"The Zimperium-Intertrust partnership completes our offering of the world's best end-to-end secure data operations and rights management solution, with bulletproof endpoint technology," said Talal Shamoon, Intertrust's Chief Executive Officer. "We're proud to partner with the world leader in this space and look forward to delivering robust end to end solutions to our customers"

Intertrust Platform is a breakthrough product that provides trusted interoperable data operations for business ecosystems. It also connects to authenticated IoT devices and apps, creating a circle of trust between clouds and devices. ExpressPlay Media Security Suite offers a number of innovative content protection services including ExpressPlay DRM, a cloud-based multi-DRM service. Zimperium's revolutionary security technology creates a protected processing environment on devices and sensors that lowers the risk of malicious tampering and signals when an attack is taking place. The combination gives enterprises and media service providers alike access to trusted data ecosystems and a high level of assurance that devices and apps in the Zero Trust environments they work with are highly resistant to hacks that can cause major disruptions or inappropriate access to data.

"Devices and apps in the field can be a weak point for attackers to steal data or disrupt vital data-based services," said Shridhar Mittal, Zimperium's Chief Executive Officer. "As the only platform that protects mobile apps across the entire DevSecOps lifecycle, from in-development to on-device, MAPS gives Intertrust's customers the confidence that their edge devices and apps are protected."

Intertrust provides software and services to major corporations globally and is offering MAPS to its current and future customers immediately.

About Zimperium

Zimperium provides the only mobile security platform purpose-built for enterprise environments. With machine learning-based protection and a single platform that secures everything from applications to endpoints, Zimperium is the only solution to provide on-device mobile threat defense to protect growing and evolving mobile environments. Zimperium is headquartered in Dallas, Texas and backed by Warburg Pincus, SoftBank, Samsung, Sierra Ventures and Telstra. For more information, follow Zimperium on Twitter and LinkedIn, or visitwww.Zimperium.com.

About Intertrust

Intertrust provides trusted computing products and services to leading global corporationsfrom mobile, consumer electronics and IoT manufacturers, to service providers and enterprise software platform companies. These products include the world's leading digital rights management (DRM) and technologies to enable private data exchanges for various verticals including energy, entertainment, retail/marketing, automotive, fintech, and IoT. Founded in 1990, Intertrust is headquartered in Silicon Valley with regional offices in London, Tokyo, Mumbai, Bangalore, Beijing, Seoul, and Tallinn. The company has a legacy of invention, and its fundamental contributions in the areas of computer security and digital trust are globally recognized. Intertrust holds hundreds of patents that are key to Internet security, trust, and privacy management components of operating systems, trusted mobile code and networked operating environments, web services, and cloud computing. Additional information is available atintertrust.com, or follow us onTwitterorLinkedIn.

Contacts:

Zimperium

Mike Reilly

fama PR for Zimperium

Zimperium@famapr.com

Intertrust

Jordan Slade

MSR Communications

Jordan@msrcommunications.com

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Top 4 cloud misconfigurations and best practices to avoid them – TechTarget

December 16, 2021 | Author: admin

As organizations use more cloud services and resources, they become responsible for a staggering variety of administrative consoles, assets, services and interfaces. Cloud computing is a large and often interconnected ecosystem of software-defined infrastructure and applications. As a result, the cloud control plane -- as well as assets created in cloud environments -- can become a mishmash of configuration options. Unfortunately, it's all too easy to misconfigure elements of cloud environments, potentially exposing the infrastructure and cloud services to malicious activity.

Let's take a look at the four most common cloud configuration misconfigurations and how to solve them.

Among the catalog of cloud misconfigurations, the first one that trips up cloud tenants is overly permissive identity and access management (IAM) policies. Cloud environments usually include identities that are human, such as cloud engineers and DevOps professionals, and nonhuman -- for example, service roles that enable cloud services and assets to interact within the infrastructure. In many cases, there can be many nonpeople identities in place. These can frequently have overly broad permissions that may allow unfettered access to more assets than needed.

To combat this issue, be sure to do the following:

Another typical misconfiguration revolves around exposed and/or poorly secured cloud storage nodes. Organizations may inadvertently expose storage assets to the internet or other cloud services, as well as reveal assets internally. In addition, they often also fail to properly implement encryption and access logging where appropriate.

To ensure cloud storage is not exposed or compromised, security teams should do the following:

Overly permissive cloud network access controls are another area ripe for cloud misconfigurations. These access control lists are defined as policies that can be applied to cloud subscriptions or individual workloads.

To mitigate this issue, security and operations teams should review all security groups and cloud firewall rule sets to ensure only the network ports, protocols and addresses needed are permitted to communicate. Rule sets should never allow access from anywhere to administrative services running on ports 22 (Secure Shell) or 3389 (Remote Desktop Protocol).

In some cases, organizations have connected workloads to the internet accidentally or without realizing what services are exposed. This exposure allows would-be attackers to assess these systems for vulnerabilities.

Vulnerable and misconfigured workloads and images also plague cloud tenants. In some cases, organizations have connected workloads to the internet accidentally or without realizing what services are exposed. This exposure enables would-be attackers to assess these systems for vulnerabilities. Outdated software packages or missing patches are another common issue. Exposing cloud provider APIs via orchestration tools and platforms, such as Kubernetes, meanwhile, can let workloads be hijacked or modified illicitly.

To address these common configuration issues, cloud and security engineering teams should regularly do the following:

Guardrail tools can help companies avoid cloud misconfigurations. All major cloud infrastructure providers offer a variety of background security services, among them logging and behavioral monitoring, to further protect an organization's data.

In some cases, configuring these services is as easy as turning them on. Amazon GuardDuty, for example, can begin monitoring cloud accounts within a short time after being enabled.

While cloud environments may remain safe without using services like these, the more tools an organization puts in place to safeguard its operations, the better chance it has to know if an asset or service is misconfigured.

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Never Gonna Give You Up: staying on top of IoT security risks – Security Boulevard

December 16, 2021 | Author: admin

The old bait-and-switch digital prank Rickrollinghaswavered in and out of popularity for the last decade and a half, but an18-year-oldstudent ofSecurity Research and Computer Science put up a blog postearlier thisyeardetailingacreative spin on the classic prank he Rickrolled hisentire school districtvia an IoThack.

Thankfully, for all those involved, the outcome of thisWhitehathackingprank was hilarious, entertaining, and relatively victimless. The perpetrators took care to ensurethey wouldnot disrupt anyschool sessions or tests. Theyeven debriefed the school districts IT team withinformation onhow and where they found the vulnerabilitiestoprevent amalicious attackin the future.You can read the full account of the incident, accompanied by a video,here.

IoT has been around for decadesnow, soyou might wonder howthispractical jokewas so easy to pull off? The short answer is that IoT is often overlooked in cybersecurity because IoT devices are built withconvenience, not security, in mind. As well explore here,its imperative that your IoTlandscapebeincluded aspart of yourcybersecurity risk assessment.With the amount of datahandled via IoT,it can quickly become overwhelming. This iswhereAxio360s platformcan help you gain a better, holistic understanding of your environment.

IoT, or the internet of things,is a term used to describesensors and actuators embedded in physical objects [that are] linked through wired and wireless networks.It includesabroad list of devices used tocollect andtransmit data from one device to another without human intervention.Mostfolks arethe most familiar withconsumer IoT.Consumer IoT includes thingswe use every day. A network of devices, such asSiri,FitBit,Alexa,Ring doorbells,Smart Homeautomation, etc.,are allexamples ofIoT devices. They are meant to operate in the background andmake daily tasks easier.

ConsumerIoT is ubiquitous, and becauseitoften runsin the background and integratesso seamlessly with our daily lives,its no surprise that many people dont often think about or consider security when using or purchasing these devices.Using webcam feeds as an example,CNNdemonstratedin 2019how easily consumer IoT devices can behacked,and ourpersonal privacycompromised. And there aremany,many,more examplesto be found online.The popularity ofpersonalIoT devicescontinues to grow at amuch quicker rate than the call for betterprotection against IoT attacks,raising the risk of attacks onhome network security.

Smart technology requires smart handling,saysMartinSchallbruch,former Cybersecurity consultant to the German government; hecomparesordinary usersliving in smart housesfullof smart devicestoa systems admin managing a data center.Meaning, consumers should follow basic cyber hygieneguidelines, just assysadmins are required, like keeping software up to date, changing passwords, etc.

Whether a person chooses to outfit their homewithsmart devices or not is irrelevant because,in 2021,living without IoT is nearly impossible.Today, there are more than10 billion active IoT devices,andin the US, IoT devices are used throughout our critical infrastructure.Examples includemedical devices,supply chain tracking (GPS), predicting when manufacturing equipmentneedsmaintenance, and other critical infrastructure systemmanagementlike power plant or water plant monitoring.Deloitte projects that, in healthcare alone, theglobal IoT marketwill be worth $158.1B in 2022.

Thegrowthof IoTinthe business world brings with it anevolutionof cyber riskand increased scope of damage.In 2017,the FDAdiscovered avulnerability inpacemakersissued by St. Jude Medical, leading to a recall of 500,000 devices. The security flaw allowed potentialhackersunauthorized access tothe devices viacommercially available equipment.In 2021, Peloton learned from its AdvancedThreat Research consultant, McAfee,thatits bike had a vulnerabilitythat would have allowed a hacker to gain access to the Peloton tablet, where they couldinstall malware and intercept the users personal data, or even gain control of the devices camera and microphone.Peloton issued a patch for thisvulnerabilitybefore anyknown exploitsoccurred, but itdoesntensure future vulnerabilitiescantarise.While the NSAhas helpedensurethatPresident Bidens Pelotonand other devicesaresecure,what aboutotherhigh-rankingofficials, judges, CEOs,etc. thatdonthave the NSAshelp?

Theadvancement of IoT technology reduces manual labor and cost while it increases efficiency through automating business processes.A 2021 studyfound that the main revenue driver formostenterprise IoT projects is cost savings, and, on average, over 80% of senior executives across industries say IoT is critical to some or all lines of business.

However, theseapplications become vulnerable as they need to communicate via the internet to send information to other devices, makingIoT cybersecurity for businesses critical. Yourcybersecurity strategy is only as strong as your weakest link.Just one device can compromise the entire system, whether its a home or an entire industrial system,Schallbruchpoints out.Cyber-attacksagainstcritical systemsare on the rise,and the reliance on IoTproduces a landscape where attacks are easy to create and difficult to remedy.Business leaders need to understand thatIoT securitymust be includedin the foundations of their cybersecurity risk management strategy.

Again, most IoT devices are built with convenience in mind,and oftenthe cost of convenience is security.Outside of home automation and digital assistants, IoT plays an integral part in the way we do business at an enterprise level today. It provides the data we need to make better business decisions.

Part of yourcybersecurity riskassessment processneeds to lookatIoT devices because, for the most part,theyre not built with security in mind.MostIoTvendors dont think of themselves as security professionals, so its up to businesses andgeneral consumersto ensure their devices are secure.IoTdevices aresignificantpotential risk factorsthat youmustconsiderin your risk assessment scenarios.Axio360 offers practical business solutions that you can useto discern what basic cybersecurity principles apply to your IoT devices when makingrisk management decisions.

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Taking Action Against the Surveillance-For-Hire Industry – Investor Relations

December 16, 2021 | Author: admin

Recently, there has been an increased focus on NSO, the company behind the Pegasus spyware (software used to enable surveillance) that we enforced against and sued in 2019. However, NSO is only one piece of a much broader global cyber mercenary industry. Today, as part of a separate effort, we are sharing our findings about seven entities that we removed from our platform for engaging in surveillance activity and we will continue to take action against others as we find them.

The global surveillance-for-hire industry targets people across the internet to collect intelligence, manipulate them into revealing information and compromise their devices and accounts. These companies are part of a sprawling industry that provides intrusive software tools and surveillance services indiscriminately to any customer regardless of who they target or the human rights abuses they might enable. This industry democratizes these threats, making them available to government and non-government groups that otherwise wouldnt have these capabilities.

We observed three phases of targeting activity by these commercial players that make up their surveillance chain: Reconnaissance, Engagement and Exploitation. Each phase informs the next. While some of these entities specialize in one particular stage of surveillance, others support the entire attack chain.

Although public debate has mainly focused on the exploitation phase, its critical to disrupt the entire lifecycle of the attack because the earlier stages enable the later ones. If we can collectively tackle this threat earlier in the surveillance chain, it would help stop the harm before it gets to its final, most serious stage of compromising peoples devices and accounts. See more details on these stages of surveillance attacks in the Threat Report.

As a result of our months-long investigation, we took action against seven different surveillance-for-hire entities. They provided services across all three phases of the surveillance chain to indiscriminately target people in over 100 countries on behalf of their clients. These providers are based in China, Israel, India, and North Macedonia. See a full list of entities we took down in the Threat Report.

The surveillance-for-hire entities we removed violated multiple Community Standards and Terms of Service. Given the severity of their violations, we have banned them from our services. To help disrupt these activities, we blocked related internet infrastructure and issued Cease and Desist letters, putting them on notice that their targeting of people has no place on our platform. We also shared our findings with security researchers, other platforms, and policymakers so they can take appropriate action.

We alerted around 50,000 people who we believe were targeted by these malicious activities worldwide, using the system we launched in 2015. We recently updated it to provide people with more granular details about the nature of targeting we detect, in line with the surveillance chain phases framework we shared above.

The existence and proliferation of these services worldwide raises a number of important questions. While cyber mercenaries often claim that their services and surveillanceware are meant to focus only on criminals and terrorists, our own investigation, independent researchers, our industry peers and governments have demonstrated that targeting is indeed indiscriminate and includes journalists, dissidents, critics of authoritarian regimes, families of opposition and human rights activists. In fact, for platforms like ours, there is no scalable way to discern the purpose or legitimacy of such targeting. This is why we focus on enforcing against this behavior, regardless of whos behind it or who the target might be.

To support the work of law enforcement, we already have authorized channels where government agencies can submit lawful requests for information, rather than resorting to the surveillance-for-hire industry. These channels are designed to safeguard due process and we report the number and the origin of these requests publicly.

Protecting people against cyber mercenaries operating across many platforms and national boundaries requires a collective effort from platforms, policymakers and civil society to counter the underlying market and its incentive structure. We believe a public discussion about the use of surveillance-for-hire technology is urgently needed to deter the abuse of these capabilities both among those who sell them and those who buy them, anchored in the following principles:

Were encouraged to see our peers and governments begin to draw attention to this threat and take action against it. For our collective response against abuse to be effective, it is imperative for technology platforms, civil society and democratic governments to raise the costs on this global industry and disincentivize these abusive surveillance-for-hire services. Our hope with this threat report is to contribute to this global effort and help shine the light on this industry.

See the full Threat Report for more information about our findings and recommendations.

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The new cyber suitcase scam you need to be aware of! – Euro Weekly News

December 16, 2021 | Author: admin

The Spanish Internet Security Office (OSI) has warned people about a new type of fraud growing in popularity, known as the retained suitcase scam, or suitcase fraud. In this scam, the cybercriminals pose as a family member or friend of the target and are contacted through social media sites such as Whatsapp, Messenger, Twitter and Facebook.

According to the OSI, the fraudsters pretend to be someone known to the victim who is supposedly abroad, reports ABC. They then tell the target that they are on the way to Spain and either that their suitcases are being held at the airport, or that they have missed their flight but the suitcases are on board.

The main part of the suitcase scam is what comes next, as the criminals then ask the victim to transfer money to a specified account provided, and mark the amount customs costs. This amount will then supposedly release the suitcases so they can be reunited with the owner.

As people can make fake profiles easily on social media, this is where the scam is really taking off. The cybercriminals skim information from peoples profiles to make the request seem more believable to the people they are taking on. Once the target believes the scam message, They are usually asked for between 500 and 1,500 euros.

To protect yourself from the suitcase scam, use multiple channels to speak to the person that is supposedly asking you for money. If they contact you on Facebook, for example, then message them on Twitter also. Speak to them if possible. Then also, speak to the company or airline who is meant to be holding the luggage. The OSI points out that No public agent or airport agent will request the deposit of money.

Thank you for taking the time to read this article, do remember to come back and check The Euro Weekly News website for all your up-to-date local and international news stories and remember, you can also follow us on Facebook and Instagram.

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The new cyber suitcase scam you need to be aware of! - Euro Weekly News

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DNA recovered in 2017 from attempted rape matched to South Kitsap man repeatedly accused of sexual assaults – Kitsap Sun

December 16, 2021 | Author: admin

A 40-year-old South Kitsap man convicted earlier this year of groping one woman and exposing himself to another was charged Tuesday with sexually assaulting a woman in 2017 and for choking his mother during an argument over cleaning his room.

Neil David Chess was also suspected this summer of harassing women who were hiking in Banner Forest Heritage Park, leading to multiple calls to Kitsap County sheriff's deputies.

The new sexual assault case against Chess, attempted second-degree rape, follows repeated testing by the Washington State Patrol Crime Lab of DNA recovered from a woman who in 2017 told police a man unknown to her had sexually assaulted her beneath the Warren Avenue bridge in Bremerton.

Though the lab was unable to match the DNA recovered in the investigation when it occurred, court records said lab technicianskept retesting the sample until they found a match in March, leading a Bremerton police detectiveto obtain a sample from Chess while he was lodged in the Kitsap County Jail.

In September the crime lab reported that it found very strong support that the sample taken from Chessmatched the DNA evidence recovered from the sexual assault on the woman.

Chess had been booked into the jail in Januaryafter he was accused in two separate cases of climbing into a UPS truck and touching the driver, a woman, and going to a house to ask for a drink of water. He then went inside the house and exposed himself to the woman who had answered the door.

In June he pleaded guilty to a count of residential burglary and fourth-degree assault with sexual motivation and was sentenced to nine months in the Kitsap County Jail.

As part of the resolution of the case, Chess was not required to register as a sex offender.

However, he had been accused by two separate women of raping them in 2019, but prosecutors dismissed the charges saying they did not believe they could prove the case in court.

Over the summer, after Chess release from jail, women who hiked and jogged in Banner Forest began reporting disturbing interactions with a man who many of them believed was Chess.

At that time, the Kitsap Sun requested records of complaints about the man harassing women in the park and found deputies had not written any reports.

However, call logs obtained through the state Public Record Act showed that users of the park believed it was Chess and told deputies they repeatedly told the man to leave them alone.

On Aug.3, a log from the sheriffs office indicates a deputy may have identified Chess and told him to stop harassing women at the park.

In a case separate from the attempted second-degree rape, but also charged Tuesday in Kitsap County Superior Court, Chess mother called 911 on Monday from her residence on the 4700 block of Westway Drive SE to say her son had choked her.

There is no-contact order prohibiting Chess from having contact with his mother, but she said Chess was living with her and she was concerned about him. She described Chess as being a paranoid schizophrenic and said that he had choked her and punched her in the face.

This morning she was cleaning up his room and he just became enraged when she asked him to help clean up, a deputy wrote. His rage turned against her.

Deputies located Chess near the Southworth Ferry Terminal and arrested him. While being driven to the Kitsap County Jail, Chess admitted to choking his mother but denied punching her, according to court documents.

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DNA recovered in 2017 from attempted rape matched to South Kitsap man repeatedly accused of sexual assaults - Kitsap Sun

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