CAMBRIDGE, Mass., June 16, 2021 /PRNewswire/ -- Akamai Technologies, Inc. (NASDAQ: AKAM), the world's most trusted solution for protecting and delivering digital experiences, today announces platform security enhancements to strengthen protection for web applications, APIs, and user accounts. Akamai's machine learning derives insight on malicious activity from more than 1.3 billion daily client interactions to intelligently automate threat detections, time-consuming tasks, and security logic to help professionals make faster, more trustworthy decisions regarding cyberthreats.

In its May 9 report Top Cybersecurity Threats in 2021, Forrester estimates that due to reasons "exacerbated by COVID-19 and the resulting growth in digital interactions, identity theft and account takeover increased by at least 10% to 15% from 2019 to 2020." The leading global research and advisory firm notes that we should "anticipate another 8% to 10% increase in identity theft and ATO [account takeover] fraud in 2021." With threat actors increasingly using automation to compromise systems and applications, security professionals must likewise automate defenses in parallel against these attacks to manage cyberthreats at pace.

New Akamai platform security enhancements include:

Adaptive Security Engine for Akamai's web application and API protection (WAAP) solutions, Kona Site Defender and Web Application Protector, is designed to automatically adapt protections with the scale and sophistication of attacks, while reducing the effort to maintain and tune policies. The Adaptive Security Engine combines proprietary anomaly risk scoring with adaptive threat profiling to identify highly targeted, evasive, and stealthy attacks. The dynamic security logic intelligently adjusts its defensive aggressiveness based on threat intelligence automatically correlated for each customer's unique traffic. Self-tuning leverages machine learning, statistical models, and heuristics to analyze all triggers across each policy to accurately differentiate between true and false positives.

Audience Hijacking Protection has been added to Akamai Page Integrity Manager to detect and block malicious activity in real time from client-side attacks using JavaScript, advertiser networks, browser plug-ins, and extensions that target web clients. Audience Hijacking Protection is designed to use machine learning to quickly identify vulnerable resources, detect suspicious behavior, and block unwanted ads, pop-ups, affiliate fraud, and other malicious activities aimed at hijacking your audience.

Bot Score and JavaScript Obfuscation have been added to Akamai Bot Manager, laying the foundation for ongoing innovations in adversarial bot management, including the ability to take action against bots aligned with corporate risk tolerance. Bot Score automatically learns unique traffic and bot patterns, and self-tunes for long-term effectiveness; JavaScript Obfuscation dynamically changes detections to prevent bot operators from reverse engineering detections.

Akamai Account Protector is a new solution designed to proactively identify and block human fraudulent activity like account takeover attacks. Using advanced machine learning, behavioral analytics, and reputation heuristics, Account Protector intelligently evaluates every login request across multiple risk and trust signals to determine if it is coming from a legitimate user or an impersonator. This capability complements Akamai's bot mitigation to provide effective protection against both malicious human actors and automated threats.

"At Akamai, our latest platform release is intended to help resolve the tension between security and ease of use, with key capabilities around automation and machine learning specifically designed to intelligently augment human decision-making," said Aparna Rayasam, senior vice president and general manager, Application Security, Akamai. "Smart automation adds immediate value and empowers users with the right tools to generate insight and context to make faster and more trustworthy decisions, seamlessly all while anticipating what attackers might do next."

For more information about Akamai's Edge Security solutions, visit our Platform Update page.

About Akamai

Akamai secures and delivers digital experiences for the world's largest companies. Akamai's intelligent edge platform surrounds everything, from the enterprise to the cloud, so customers and their businesses can be fast, smart, and secure. Top brands globally rely on Akamai to help them realize competitive advantage through agile solutions that extend the power of their multi-cloud architectures. Akamai keeps decisions, apps, and experiences closer to users than anyone and attacks and threats far away. Akamai's portfolio of edge security, web and mobile performance, enterprise access, and video delivery solutions is supported by unmatched customer service, analytics, and 24/7/365 monitoring. To learn why the world's top brands trust Akamai, visit http://www.akamai.com, blogs.akamai.com, or @Akamai on Twitter. You can find our global contact information at http://www.akamai.com/locations.

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twhitman@akamai.com

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While the maritime industry has been hesitant engaging in use of data insight and machine learning, the table is now about to turn. Today, an increasing number of maritime companies actively use data insights to improve sales, supply chain activities, and increase revenues among these the worlds largest ship supplier, Wrist Ship Supply.

The need for efficiency in the maritime sector has led companies to actively use data as a measure to optimize the supply chain. This has paved the way for new ship supply services centered around data insights and machine learning to increase top and bottom-line figures.

According to the leading data and analytics firm, GateHouse Maritime, data insights can make a noticeable difference in the maritime sector. With a combination of historic and real-time ocean data, machine learning, and smart algorithms, maritime supply companies can predict vessel destinations and arrivals with high precision.

Traditionally, vessel tracking has been a time consuming, manual process characterized by imprecise predictions and uncertainty. But today, the process can be automated and turn large amounts of data into tangible leads and sales:

With the help of data insights, it is possible to predict arrivals several days in advance with almost 100 percent accuracy. This allows maritime supply companies to obtain an obvious competitive advantage, as they can operate proactively and sell services to potential customers days before a given vessel calls into port, says CEO at GateHouse, Maritime, Martin Dommerby Kristiansen.

Data analytics strengthen the worlds largest ship supplierFour years ago, the worlds largest ship supplier, Wrist Ship Supply, realized a strategy that would integrate data analytics in numerous business areas. The global ship supplier is a full-service provider, providing service for marine, offshore and navy operations, such as supplying consumables, handling of owners goods and spare parts storage and forwarding.

Today, Wrist Ship Supply works strategically with data analytics and business intelligence to improve internal processes and increase value for customers:

In recent years, we have experienced an increasing pull from the market and as a market leader within ship supply, we feel obliged to take part in the digital transformation. Data analysis has proven to be a cornerstone and a very important tool for measuring and improving performances across our own as well as customers supply chain. Now, our business model is infused with data analytics and business intelligence that strengthen efficiency and reliability in both internal and external operations, explains Business Analysis Director at Wrist Ship Supply, Birthe Boysen.

For Birthe Boysen and Wrist Ship Supply, data analytics has especially proven its worth within sales:

It is crucial for us to know where potential customer vessels are heading and when they arrive in different ports. This allows us to coordinate our sales efforts and establish contact in advance. Not only does this make us more efficient, but it also creates value for customers, because all service activities can be planned several days ahead of arrival.

While the data-driven sales approach has increased the focus on KPIs, it has also become an important part of budgeting. Therefore, it has been a key priority for Wrist Ship Supply to be able to navigate in the ocean of available data:

We have an almost endless amount of data available, and it easily becomes exhausting to keep track of numbers and figures. Therefore, we prioritize to make sure that both internal and external stakeholders can make sense of the conclusions in our data insights. If employees or customers cannot fathom the overall lines in our data results, it will be difficult to use analytics in any way, Nadia Hay Kragholm, Senior Business Analyst in Wrist remarks.

According to Martin Dommerby Kristiansen, data insight has the potential to transform the entire maritime industry because efficiency has never been more important:

The maritime industry is indeed reliant on efficiency across the value chain. Recently, we have seen how a vessel stuck in the Suez Canal for only a few days can impact not only the maritime industry, but the entire transportation and logistics sector. This goes to show how important data insight and analytics can prove to be for companies that wish to operate proactively and minimize disorder in the supply chain.

GateHouse Maritime is a leader in Ocean Visibility solutions. We help global maritime service providers, cargo owners and logistic companies with transparent and accurate location data and predictions, cargo transport status, and offshore asset protection and surveillance. Our powerful maritime data foundation consists of 273 billion datapoints and +30 analysis and predictive models used for data-driven decisions by maritime operators worldwide. GateHouse Maritime is a subsidiary of GateHouse Holding, founded in 1992 and headquartered in Denmark, and which also holds the subsidiaries GateHouse SatCom and GateHouse Igniter.Source: GateHouse Maritime A/S

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This article is excerpted from the course "Fundamental Machine Learning," part of the Machine Learning Specialist certification program from Arcitura Education. It is the ninth part of the 13-part series, "Using machine learning algorithms, practices and patterns."

This article explores the numerical prediction and category prediction supervised learning techniques. These machine learning techniques are applied when the target whose value needs to be predicted is known in advance and some sample data is available to train a model. As explained in Part 4, these techniques are documented in a standard pattern profile format.

A data set may contain a number of historical observations (rows) amassed over a period of time where the target value is numerical in nature and is known for those observations. An example is the number of ice creams sold and the temperature readings, where the number of ice creams sold is the target variable. To obtain value from this data, a business use case might require a prediction of how much ice cream will be sold if the temperature reading is known in advance from the weather forecast. As the target is numerical in nature, supervised learning techniques that work with categorical targets cannot be applied (Figure 1).

The historical data is capitalized upon by first finding independent variables that influence the target dependent variable and then quantifying this influence in a mathematical equation. Once the mathematical equation is complete, the value of the target variable is predicted by inputting the values of the independent values.

The data set is first scanned to find the best independent variables by applying the associativity computation pattern to find the relationship between the independent variables and the dependent variable. Only the independent variables that are highly correlated with the dependent variable are kept. Next, linear regression is applied.

Linear regression, also known as least squares regression, is a statistical technique for predicting the values of a continuous dependent variable based on the values of an independent variable. The dependent and independent variables are also known as response and explanatory variables, respectively. As a mathematical relationship between the response variable and the explanatory variables, linear regression assumes that a linear correlation exists between the response and explanatory variables. A linear correlation between response and explanatory variables is represented through the line of best fit, also called a regression line. This is a straight line that passes as closely as possible through all points on the scatter plot (Figure 2).

Linear regression model development starts by expressing the linear relationship. Once the mathematical form has been established, the next step is to estimate the parameters of the model via model fitting. This determines the line of best fit achieved via least squares estimation that aims to reduce the sum of squared error (SSE). The last stage is to evaluate the model either using R squared or mean squared error (MSE).

MSE is a measure that determines how close the line of best fit is to the actual values of the response variable. Being a straight line, the regression line cannot pass through each point; it is an approximation of the actual value of the response variable based on estimated values. The distance between the actual and the estimated value of response variable is the error of estimation. For the best possible estimate of the response variable, the errors between all points, as represented by the sum of squared error, must be minimized. The line of best fit is the line that results in the minimum possible sum of squares errors. In other words, MSE identifies the variation between the actual value and the estimated value of the response variable as provided by the regression line (Figure 3).

The coefficient of determination, called R squared, is the percentage of variation in the response variable that is predicted or explained by the explanatory variable, with values that vary between 0 and 1. A value equal to 0 means that the response variable cannot be predicted from the explanatory variable, while a value equal to 1 means the response variable can be predicted without any errors. A value between 0 and 1 provides the percentage of successful prediction.

In regression, more than two explanatory variables can be used simultaneously for predicting the response variable, in which case it is called multiple linear regression.

The numerical prediction pattern can benefit from the application of the graphical summaries computation pattern by drawing a scatter plot to graphically validate if a linear relationship exists between the response and explanatory variables (Figure 4).

There are cases where a business problem involves predicting a category -- such as whether a customer will default on their loan or whether an image is a cat or a dog -- based on historical examples of defaulters and cats and dogs, respectively. In this case, the categories (default/not default and cat/dog) are known in advance. However, as the target class is categorical in nature, numerical predictive algorithms cannot be applied to train and predict a model for classification purposes (Figure 5).

Supervised machine learning techniques are applied by selecting a problem-specific machine learning algorithm and developing a classification model. This involves first using the known example data to train a model. The model is then fed new unseen data to find out the most appropriate category to which the new data instance belongs.

Different machine learning algorithms exist for developing classification models. For example, naive Bayes is probabilistic while K-nearest neighbors (KNN), support vector machine (SVM), logistic regression and decision trees are deterministic in nature. Generally, in the case of a binary problem -- cat or dog -- logistic regression is applied. If the feature space is n-dimensional (a large number of features) with complex interactions between the features, KNN is applied. Naive Bayes is applied when there is not enough training data or fast predictions are required, while decision trees are a good choice when the model needs to be explainable.

Logistic regression is based on linear regression and is also considered a class probability estimation technique, since its objective is to estimate the probability of an instance belonging to a particular class.

KNN, also known as lazy learning and instance-based learning, is a black-box classification technique where instances are classified based on their similarity, with a user-defined (K) number of examples (nearest neighbors). No model is explicitly generated. Instead, the examples are stored as-is and an instance is classified by first finding the closest K examples in terms of distance, then assigning the class based on the class of the majority of the closest examples (Figure 6).

Naive Bayes is a probability-based classification technique that predicts class membership based on the previously observed probability of all potential features. This technique is used when a combination of a number of features, called evidence, affects the determination of the target class. Due to this characteristic, naive Bayes can take into account features that may be insignificant when considered on their own but when considered accumulatively can significantly impact the probability of an instance belonging to a certain class.

All features are assumed to carry equal significance, and the value of one feature is not dependent on the value of any other feature. In other words, the features are independent. It serves as a baseline classifier for comparing more complex algorithms and can also be used for incremental learning, where the model is updated based on new example data without the need for regenerating the whole model from scratch.

A decision tree is a classification algorithm that represents a concept in the form of a hierarchical set of logical decisions with a tree-like structure that is used to determine the target value of an instance. [See discussion of decision trees in part 2 of this series.] Logical decisions are made by performing tests on the feature values of the instances in such a way that each test further filters the instance until its target value or class membership is known. A decision tree resembles a flowchart consisting of decision nodes, which perform a test on the feature value of an instance, and leaf nodes, also known as terminal nodes, where the target value of the instance is determined as a result of traversal through the decision nodes.

The category prediction pattern normally requires the application of a few other patterns. In the case of logistic regression and KNN, applying the feature encoding pattern ensures that all features are numerical as these two algorithms only work with numerical features. The application of the feature standardization pattern in the case of KNN ensures that none of the large magnitude features overshadow smaller magnitude features in the context of distance measurement. Naive Bayes requires the application of the feature discretization pattern as naive Bayes only works with nominal features. KNN can also benefit from the application of feature discretization pattern via a reduction in feature dimensionality, which contributes to faster execution and increased generalizability of the model.

The next article covers the category discovery and pattern discovery unsupervised learning patterns.

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One of the most significant challenges to the advancement of precision medicine has been the lack of an infrastructure to support translational bioinformatics, supporting organizations as they work to uncover unique datasets to find novel associations and signals.

By supporting greater interoperability and collaboration, data scientists, developers, clinicians and pharmaceutical partners have the opportunity to leverage machine learning to reduce the time it takes to move from insight to discovery, ultimately leading to the right patients receiving the right care, with the right therapeutic at the right time.

To get a better understanding of challenges surrounding precision medicine and its future, Healthcare IT News sat down with Dr. Taha Kass-Hout, director of machine learning at AWS.

Q: You've said that one of the most significant challenges to the advancement of precision medicine has been the lack of an infrastructure to support translational bioinformatics. Please explain this challenge in detail.

A: One of the challenges in developing and utilizing storage, analytics and interpretive methods is the sheer volume of biomedical data that needs to be transformed that often resides on multiple systems and in multiple formats. The future of healthcare is so vibrant and dynamic and there is an opportunity for cloud and big data to take on a larger role to help the industry address these areas.

For example, datasets used to perform tasks such as computational chemistry and molecular simulations that help de-risk, and advance molecules into development, contain millions of data points and require billions of calculations to produce an experimental output. In order to bring new therapeutics to market faster, scientists need to move targets through development faster and find more efficient ways to collaborate both inside and outside of their organizations.

Another challenge is that large volumes of data acquired by legacy research equipment, such as microscopes and spectrometers, is usually stored locally. This creates a barrier for securely archiving, processing and sharing with collaborating researchers globally. Improving access to data, securely and compliantly, while increasing usability is critical to maximizing the opportunities to leverage analytics and machine learning.

For instance, Dotmatics' cloud-based software provides simple, unified, real-time access to all research data in Dotmatics and third-party databases, coupled with integrated, scientifically aware informatics solutions for small molecule and biologics discovery that expedite laboratory workflows and capture experiments, entities, samples and test data so that in-house or multi-organizational research teams become more efficient.

Today we are seeing a rising wave of healthcare organizations moving to the cloud, which is enabling researchers to unite R&D data with information from across the value chain, while benefiting from compute and storage options that are more cost-effective than on-premises infrastructure.

For large datasets in the R&D phase, large-scale, cloud-based data transfer services can transfer hundreds of terabytes and millions of files at speeds up to 10 times faster than open-source tools. Storage gateways ensure experimental data is securely stored, archived and available to other permissioned collaborators. Uniting data in a data lake improves access and helps to eliminate silos.

Cloud-based hyperscale computing and machine learning enable organizations to collaborate across datasets, create and leverage global infrastructures to maintain data integrity, and more easily perform machine learning-based analyses to accelerate discoveries and de-risk candidates faster.

For example, six years agoModerna started building databases and information-based activities to support all of their programs. Today, they are fully cloud-based, and their scientists don't go to the lab to pipette their messenger RNA and proteins. They go to their web portal, the Drug Design Studio that runs on the cloud.

Through the portal, scientists can access public and private libraries that contain all the messenger RNA that exists and the thousands of proteins they can produce. Then, they only need to press a button and the sequence goes to a fully automated, central lab where data is collected at every step.

Over the years, data from the portal and lab has helped Moderna improve their sequence design and production processes and improve the way their scientists gather feedback. In terms of research, all of Moderna's algorithms rely on computational power from the cloud to further their science.

Q: You contend that by supporting greater interoperability and collaboration, data scientists, developers, clinicians and pharmaceutical partners have the opportunity to leverage machine learning to reduce the time it takes to move from insight to discovery. Please elaborate on machine learning's role here in precision medicine.

A: For the last decade, organizations have focused on digitizing healthcare. In the next decade, making sense of all this data will provide the biggest opportunity to transform care. However, this transformation will primarily depend on data flowing where it needs to, at the right time, and supporting this process in a way that is secure and protects patients' health data.

It comes down to interoperability. It may not be the most exciting topic, but it's by far one of the most important, and one the industry needs to prioritize. By focusing on interoperability of information and systems today, we can ensure that we end up in a better place in 10 years than where we are now. And so, everything around interoperability around security, around identity management, differential privacy is likely to be part of this future.

Machine learning models trained to support healthcare and life sciences organizations can help automatically normalize, index and structure data. This approach has the potential to bring data together in a way that creates a more complete view of a patient's medical history, making it easier for providers to understand relationships in the data and compare this to the rest of the population, drive increased operational efficiency, and have the ability to use data to support better patient health outcomes.

For example, AstraZeneca has been experimenting with machine learning across all stages of research and development, and most recently in pathology to speed up the review of tissue samples. Labeling the data is a time-consuming step, especially in this case, where it can take many thousands of tissue-sample images to train an accurate model.

AstraZeneca uses a machine learning-powered, human-in-the-loop data-labeling and annotation service to automate some of the most tedious portions of this work, resulting in at least 50% less time spent cataloging samples.

It also helps analysts spot trends and anomalies in the health data and derive actionable insights to improve the quality of patient care, make predictions for medical events such as stroke or congestive heart failure, modernize care infrastructure, increase operational efficiency and scale specialist expertise.

Numerate, a discovery-stage pharmaceutical, uses machine learning technologies to more quickly and cost-effectively identify novel molecules that are most likely to progress through the research pipeline and become good candidates for new drug development.

The company recently used its cloud-based platform to rapidly discover and optimize ryanodine receptor 2 (RYR2) modulators, which are being advanced as new drugs to treat life-threatening cardiovascular diseases.

Ryanodine 2 is a difficult protein to target, but the cloud made that process easier for the company. Traditional methods could not have attacked the problem, as the complexity of the biology makes the testing laborious and slow, independent of the industry's low 0.1% screening hit rate for much simpler biology.

In Numerate's case, using the cloud enabled the company to effectively decouple the trial-and-error process from the laboratory and discover and optimize candidate drugs five times faster than the industry average.

Machine learning also is helping power the entire clinical development process. Biopharma researchers use machine learning to design the most productive trial protocols, study locations, recruitmentand patient cohorts to enroll. Researchers not trained as programmers can use cloud-based machine learning services to build, train and deploy machine learning algorithms to help with pre-clinical studies, complex simulations and predictive workflow optimization.

Machine learning can also help accelerate the regulatory submission process, as the massive amounts of data generated during clinical trials can be captured and effectively shared to collaborate between investigators, contract research organizations (CROs) and sponsor organizations.

For example, the Intelligent Trial Planner (ITP) from Knowledgent, now part of Accenture, uses machine learning services to determine the feasibility of trial studies and forecast recruitment timelines. The ITP platform enables study design teams at pharma organizations to run prediction analysis in minutes, not weeks, allowing them to iterate faster and more frequently.

Powered by machine learning, real-time scenario planning helps to facilitate smarter trial planning by enabling researchers to determine the most optimal sites, countries and/or protocol combinations.

By eliminating poor performing sites, trial teams have the potential to reduce their trial cost by 20%. And by making data-driven decisions that are significantly more accurate, they can plan and execute clinical trials faster, leading to hundreds of thousands in cost savings for every month saved in a trial.

Additionally, purpose-built machine learning is supported by cost-effective cloud-based compute options. For example, high-performance computing (HPC) can quickly scale to accommodate large R&D datasets, orchestrating services and simplifying the use and management of HPC environments.

Data transformation tools can also help to simplify and accelerate data profiling, preparation and feature engineering, as well as enable reusable algorithms both for new model discovery and inference.

The healthcare and life sciences industry has come a long way in the last year. However, for progress and transformation to continue, interoperability needs to be prioritized.

Q: The ultimate goal of precision medicine is the right patients receiving the right care, with the right therapeutic, at the right time. What do healthcare provider organization CIOs and other health IT leaders need to be doing with machine learning and other technologies today to be moving toward this goal?

A: The first things IT leaders need to ask themselves is: 1) If they are not investing yet in machine learning, do they plan to this year? And 2) What are the largest blockers to machine learning in their teams?

Our philosophy is to make machine learning available to every data scientist and developer without the need to have a specific background in machine learning, and then have the ability to use machine learning at scale and with cost efficiencies.

Designing a personalized care pathway using therapeutics tuned for particular biomarkers relies on a combination of different data sources such as health records and genomics to deliver a more complete assessment of a patient's condition. By sequencing the genomes of entire populations, researchers can unlock answers to genetic diseases that historically haven't been possible in smaller studies and pave the way for a baseline understanding of wellness.

Population genomics can improve the prevention, diagnosis and treatment of a range of illnesses, including cancer and genetic diseases, and produce the information doctors and researchers need to arrive at a more complete picture of how an individual's genes influence their health.

Advanced analytics and machine learning capabilities can use an individual or entire population's medical history to better understand relationships in data and in turn deliver more personalized and curated treatment.

Second, healthcare and life sciences organizations need to be open to experimenting, learning about and embracing both cloud and technology and many organizations across the industry are already doing this.

Leaders in precision medicine research such as UK Biobank, DNAnexus, Genomics England, Lifebit, Munich Lukemia Lab, Illumina, Fabric Genomics, CoFactor Genomics and Emedgene all leverage cloud and technology to speed genomic interpretation.

Third, supporting open collaboration and data sharing needs to be a business priority. The COVID-19 Open Research Dataset (CORD-19) created last year by a coalition of research groups provided open access to the plenary of available global COVID-19 research and data.

This was one of the primary factors that enabled the discovery, clinical trial and delivery of the mRNA-based COVID-19 vaccines in an unprecedented timeframe. Additionally, our Open Data Programmakes more than 40 openly available genomics datasets accessible, providing the research community with a single documented source of truth.

Commercial solutions that have leveraged machine learning to enable large-scale genomic sequencing include organizations such as Munich Leukemia Lab, who has been able to use the Field Programmable Gate Array-based compute instances to greatly speed up the process of whole genome sequencing.

As a result, what used to take 20 hours of compute time can now be achieved in only three hours. Another example is Illumina, which is using cloud solutions to offer its customers a lower-cost, high-performance genomic analysis platform, which can help them speed their time to insights as well as discoveries.

Twitter:@SiwickiHealthITEmail the writer:bsiwicki@himss.orgHealthcare IT News is a HIMSS Media publication.

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