There are many ways synthetic data can be used to help grow, strengthen, and rejuvenate your organization and many processes it handles, but here are five key ways in which synthetic data will be able to directly help you and your company!
1) Synthetic Data has a wide variety of use cases to help you out with. Synthetic Data is artificially generated and thus can be manipulated for production testing and model fitting in a plethora of ways. It can be used for machine learning, mathematical model fitting, model testing, and more!
2) Synthetic Data adds an extra layer of security to your data; because synthetic data is artificially generated, if there is a data leak, hack, or if something ends up going wrong, there will be minimal security risk and harm as the exposed data will not put any individual's private information in danger of being exploited. This factor is huge within the cybersecurity world and adds as an extra precaution just in case there is a breach in the system.
3) Synthetic Data is cost-effective. Synthetic Data is less expensive to generate than it is to buy real data in terms of both time and money. Furthermore, because you may need different types of data for different types of test, you'll need several different types of data to test with; this begs the question, wouldn't it be easier to generate each type on the fly as needed rather than stat testing, realize you need to collect more samples and pause testing until you have collected enough to continue?
4) Synthetic Data is great when it comes to threat detection. Synthetic data can reflect authentic patterns and behaviors for insider threat detection and user behavior in the models it is used to create. Furthermore, it can be used during performance testing to cover a variety of different scenarios which can lead to increased threat detection and strengthen an application or model's defensive capabilities.
5) Synthetic Data strengthens performance more than authentic data can. Synthetic data can be used to test models with quickly and efficiently so that data can be analyzed right after the data is plugged in. Moreover, it can be used to train models in ways models can't be trained when using authentic data; it can be generated to fill in for any missing data or used to predict different types of behavior based on reasonable machine learning, rather than leaving data empty or assuming what 'would' have been answered.
Enterprise Implementation Best Practices: Behavioral Threat Detection for Sexual Harassment
We have discussed that with technology currently available, you can combine commercial network traffic and synthetic data generation technologies to provide rich content that mirrors real-world network traffic with configurable threat patterns contained within the traffic data.
Imagine if you were responsible for implementing a solution for detecting and preventing sexual harassment within your system’s network. Would it not make sense to procure this solution in a fashion where vendors could be quantifiably evaluated based on your actual network and sexual harassment criteria? And the awarded contract would include these same metrics as Service Level Agreement (SLA) criteria so that you would know the solution was implemented and operating over time correctly?
For those of you operating on the buy side of the Enterprise consider implementation best practices where you are not only trusting what the vendor is telling you the system is doing, but also verifying and holding the vendor to its commitments.
Learn more at www.exactdata.net
Enjoy the TAG Cyber interview below between ExactData's John Dawson and TAG Cyber's Ed Amoroso where John discusses the concept of Synthetic Data and its real-world application use cases!
The topics of supervised and unsupervised machine learning are up and coming in today's age, and both are essential to understand for those of us invested in the data analytics world. Below are two quick definitions for the differing types of machine learning.
Supervised Machine Learning is the process of learning the relationships between input data based on pre-existing knowledge, descriptors, and models to classify future unknown data in a more accurate way.
Unsupervised Machine Learning is the process of conceptualizing relationships and input data on the fly with the intent to understand, infer, and predict a balanced structure within a set of current or future data.
While both tactics for machine learning have their advantages and disadvantages, supervised machine learning tends to be utilized more frequently do to having an overall better comparative performance. Supervised machine learning is used throughout many fields of data analytics, a couple of examples being text analysis, sentiment analysis, clustering, risk analysis, and much more!
While supervised machine learning has many benefits, it has a few shortcomings as well, one of them being a reliance on labeled, network data for testing purposes. Fortunately, ExactData combines our synthetic data with Ixia's network traffic generator to counteract these shortcomings and test both frequently and rigorously to ensure the proper training of data models using supervised machine learning capabilities.
More on this subject can be found here in our Supervised Machine Learning white paper!
Healthcare is certainly at its limits right now due to the COVID-19 pandemic, but in more ways than you may think. Atlas VPN reports that an estimated 83% of healthcare providers in the United States are actually running on outdated software, meaning they're a lot more vulnerable to cyber attacks and malware. In fact, Palo Alto Networks reports that 56% of surveyed healthcare providers still use software that runs on the Windows 7 operating system, which Microsoft no longer offers customer support for, leaving them further at risk of attack.
However, concerns for healthcare data privacy is becoming more prevalent due to COVID-19 in multiple ways. For example, Congress has begun pushing for more healthcare data privacy amidst reports of the White House assembling technology and healthcare companies to develop a COVID-19 surveillance system. Subsequently, there is now a push from Congress to ensure more privacy when it comes to collecting and sharing healthcare data, including the data collected under the COVID-19 surveillance system. Globally, Europe also faces similar problems with emergency healthcare applications being used to track the COVID-19 virus and Petra Wilson, European Program Director of the Personal Connected Health Alliance, believes the COVID-19 pandemic pointed out flaws in their current usage and sharing of healthcare data and post-virus there will be a bigger emphasis on using health data for the public good and retaining security and privacy for peoples' personal data.
The COVID-19 is one of the most dangerous problems we as a society struggle with today, and to make matters worse the disease is highly contagious and spreading rapidly around the world. As there are many people who are unaware of their health situation and don't find it necessary to get tested, and furthermore aren't enough test kits readily available for every single person, it's essential we use our resources and historical data to track the virus so we can begin to stop it in its tracks.
By preparing travel, social, and contact networks, we may effectively be able to track to a certain degree where the virus is, isn't, and may potentially be. A travel network specifies a single, series, or pattern of travel activities by a node [individual] or group of nodes [group of individuals] by any mode to any location. A social network is defined as a network of known social interactions between family, friends, co-workers, and those you are relatively familiar with. Meanwhile, a contact network tracks the time and proximity one node may have to another at any given time, but isn't specifically limited to others known by the individual; contact networks include interactions with a cashier when buying a coffee or perhaps passing someone nearby on local transportation. By combining the three types of networks, we effectively can understand each node's travel, social, and contact patterns and compare them to COVID-19's own pattern of travel, something we can denote as contact tracing.
Using the data collected from the COVID-19 outbreak as well as by those who have been tested for exposure, we have the opportunity to track the precise whereabouts of the pandemic and fight it before the next wave of it or a future pandemic begins. The first of our two key assumptions for this methodology is we have enough readily available data to use for tracking where COVID-19 has been and currently is so we can also predict where it is likely to go. The second key assumption is that we find a way to track those we don't have data on, as the contact network isn't limited to interactions with known nodes, but unknown ones as well. Nevertheless, this is a rare opportunity we have to begin our fight back against COVID-19 and other future pandemics, and we should take any advantage we can to prepare for it.
The Next Step in the Evolutionary Cyber Security Ladder; Complex Dynamic
Payloads with High Fidelity Content and Relational Scenarios
Commercial network traffic generation technologies such as Ixia BreakingPoint or Spirent simulate real-world legitimate traffic, distributed denial of service (DDoS), exploits, malware, and fuzzing. These technologies help to test and validate an organization’s security infrastructure.
Today, advanced behavior-based threats are growing more sophisticated, harder to detect, and are accelerating rapidly. Current networks are becoming even more vulnerable to these rapidly growing
threats that cost more than $4B annually in the US alone. Detecting and mitigating Advanced Persistent Threats and Insider Threats demand far more advanced testing techniques, analytics, and sophisticated data sets for consistent detection, demonstration, measurement, and mitigation.
Today, you can combine commercial network traffic and synthetic data generation technologies to
provide rich content that mirrors real-world network traffic with configurable threat patterns contained within the traffic data. This end-to-end solution generates the behavioral network traffic test data as well as the system response files, enabling immediate scoring and correction of systems errors. This is a huge advancement in this critical and growing segment of sophisticated threat-based network testing.
At first, test data and training data may seem like the same thing, however there are several fundamental differences between the two that can make or break your data model.
Training Data is used to build or create a model in its earlier stages and can be implemented to help the model run. Training Data is often found in machine learning to ensure the data model can be "trained" to perform several actions, and ensuring the future development of the API and algorithms that a machine will continuously work with.
Test Data on the other hand are datasets which are used to validate existing data models to make sure algorithms produce correct results, machine learning is happening correctly, and output which mirrors intended findings are consistent. Alternatively, test data can be used to invalidate a data model and disprove it's efficiency, or find that a data model or AI may be producing sub-optimal results and therefore may need further tests or different kinds of training to reach maximum efficiency. Below, is a diagram depicting the data training, validation, and testing model and the different steps that may be encompassed throughout the different processes.
Training AI or any sort of machine learning is a rigorous process that requires a fair amount of training, validation, and testing, and it's very rare a data model is perfected on its first try. That's why it's important for training and test data to be differentiated; otherwise, results will appear too similar, leading observations and evaluations for both datasets to be inconclusive, ensuring more testing and validation would have to be done.
Banking, insurance, and financial applications are apart of the largest sector for consumer IT services and carry very confidential information pertaining to their day to day user base. Banking software must be free of error not only to ensure the best possible customer service, but because hackers can exploit potential bugs in the application and access private financial data, which may compromise the assets of many individuals using said banking, insurance or financial services.
Thus, it becomes essential that every iteration of banking applications are tested profusely before rolling out to the live servers. When used with test data, banking applications can simulate live consumer interactions with minimal risk to all parties involved. Test data can be used throughout each phase of the testing process including but not limited to database testing, integration testing, and security testing. For example, test data can be used for security verification and validation processes to ensure only those with the correct permissions can access their data.