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 Atlas VPN reports that an estimated 83% of healthcare providers in the United States are actually running on outdated software, meaning they're a lot more vulnerable to cyber attacks and malware.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.