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 Atlas VPN reports that an estimated 83% of healthcare providers in the United States are actually running on outdated software, meaning they're a lot more vulnerable to cyber attacks and malware.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.
60 percent of breaches are linked to a third party. Why are you giving them access to your data when you don't need too?
Third-party contractors are the biggest source of security incidents outside of a company’s employees:
Why are commercial companies and government agencies giving access to their private and confidential data to third parties when there exist viable technology alternatives to this practice and they don't need too?
With the rise of social media in our current day and age, it's only natural that test data is utilized to ensure platforms such as Facebook, Twitter, and Instagram are working as intended to their highest degree of efficiency. Test data is utilized both for desktop and mobile applications for the social media platforms and is generated for many different use cases. One particular use case for test data in social media applications is for encryption so that user data remains private and isn't accessible to those who shouldn't have it.
Furthermore, user data and test user data are used by platforms such as Facebook and LinkedIn to generated sponsored and tailored content for each specific individual. Each user's data is different to a degree and thus generated test data must be dynamic enough to be manipulated to represent the millions of user-types the social media world may have.
A thought exercise on the System perspective of dev and test, as enabled by ExactData Synthetic Data.
Let’s consider the development of an application that scours incoming data for fraudulent activity… How would that test and analysis look with production data, de-identified production data, hand crafted data, and ExD synthetic data?
Let’s also consider that the application will classify all transactions/events as either bad or good. The perfect application would classify every transaction correctly resulting in 100% Precision (everything classified as bad was actually bad), 100% capture rate (classified every actual bad as bad), 0% escape rate (no bads classified as good), and 0% False Positive rate (no goods classified as bad). The application needs to be developed, tested, and analyzed from a System perspective. For example, the application could classify every transaction as bad and achieve 100% capture rate, and 0% escape rate, but would also result in poor Precision and a huge False Positive rate – thus requiring significant labor support to adjudicate the classifications. On the other extreme, the application could classify everything good, be mostly right, and not catch any bads. Both of these boundary conditions are absurd but illustrate the point of the importance of System.
One method of System analysis is the Confusion Matrix, noted below.
With production data, you don’t know where the bads are, so you can’t complete the confusion matrix.
With de-identified production data, you don’t know where the bads are, so you can’t complete the confusion matrix.
With hand-crafted data, you might have the “truth” to enable completion of the confusion matrix, you would not have the complexity or volume to be truly testing to find the “needle” in the haystack of fraudulent behavior within mass of good behavior.
With ExD synthetic data, you know where every bad is (you have the ground truth), so you CAN complete all 4 quadrants of the confusion matrix, and can then only, conduct a system analysis, driving the application to the real goal of tuning and optimizing Precision (maximizing TP) and Capture rate (maximizing TP/TP+FN) , while at the same time minimizing Escapes (FN) and False Positive rate (FP/FP+TP). Within a particular setup of an application version, these are typically threshold trade-offs, but with next iteration development, there is the opportunity to improve on all scores.
Test data and test data management have become more prominent in today's cyber world. With trends such as data visualization, SCRUM and Agile methodologies, artificial intelligence, and big data connectivity, test data has become essential in how company's operate with their technology.
Test data management is the process of creating non-production data with the implication that it will be utilized for rigorous testing and development for future applications. Test data is safer to use than real data when performing said development tests and can significantly reduce the life-cycle time a test-product may need. Furthermore, test data can lower security risks an organization may have during the software development process, can help prevent rollbacks, and can help set forth precedents for any bug-fixes in one's code or application.
Not only are there many benefits for efficient test data management strategies within the cyber security industry, but the benefits may protrude into other departments such as R&D and marketing as well!