In order to protect sensitive information or equipment, cybersecurity systems rely on biometrics, behavior, and location data to identify authorized users and allow them access.
Importantly, the system also scans user behavior in real time. It scans downloads and uploads for malware, for instance, and logs users out of their session if it has gone idle for long enough, on the assumption that someone else could have gained access to their device.
Many systems also have IoT (internet of things) capability, which requires the coordination of security-related data across multiple devices and machines. When the number of network devices reaches the thousands or millions humans can no longer manage it alone.
Many machine learning programs also scan the internet for news about the latest threats and incorporate that information into its security framework.
Machine learning in cybersecurity protects software systems, responds to threats, and recovers damaged files in the event of a breach. It does this through cloud-based storage, encryption, blockchain technology, multi-factor authentication, firewalls, malware scanning, and more.
Machine learning programs are uniquely able to identify threats by learning users’ behavioral patterns. If they encounter any behavior that reads as unordinary, they respond automatically. These responses may be to neutralize the perceived threat or to alert IT or fraud departments.
NIST Cybersecurity Framework
Sometimes, governments provide authoritative standards for machine learning in cybersecurity. The US Department of Commerce’s National Institute of Standards and Technology is one such authority. The NIST publishes the CSF (Cybersecurity Framework) used by businesses and governments all over the world.
Amazon’s cloud-based system has been providing security services to companies of every possible size all over the world, in every industry, and they only continue to grow. For beginners, they also offer free resources to learn about machine learning in cybersecurity.
Fortinet leads the industry in firewall development as well as cloud services, threat detection, and other cybersecurity applications of machine learning. They also offer a free, comprehensive threat level assessment.
PipeCandy eCommerce Leads & Insights for Fulfillment tracks company data and company shipping details: shipping volume, which companies they use to ship products, whether they ship internationally or not, and so on. You can find company leads easily with this dataset with filter capabilities.
Wikiroutes Transit Data provides public transport information—routes, stop points, and more—via crowd-sourcing. The data is constantly updated and can be easily converted and integrated into your own software system.
Wikiroute’s Transit Data is used by individuals, private companies, and government agencies of all types and sizes.
TrackStar’s Predictive Credit Technology uses fifteen years of financial dispute data to create predictive models of future borrowing potential. With this data and AI technology, your bank or other lending company can mitigate the risk of fraud, improve existing customer relations, and reduce your operating costs.