Machine learning is used to create connections between various data pieces. Most models look for elements that can give the most context about malware risks while developing endpoint protection solutions. To put it another way, the models are taught to distinguish between good and poor software in order to stop the bad.

To emphasize the need for additional security, several modern solutions on the market try to discover a larger range of dangerous code than previous offerings. When models are trained with a bias toward detecting malware, they are more prone to mix up good and bad software, resulting in false positives.

Because capturing a representative sample of excellent software, particularly bespoke software, is difficult, this mismatch becomes much more severe. Many business applications are built for specific use at a certain firm, and new tools have made it easier and faster for firms to create or integrate more of their own applications. So, although getting tens of thousands of malware samples is simple and reflects concerns that affect all businesses, gathering a similar number of good software requires learning about well-known and packaged programs. As a result, training models distinguish between malware and commonly packaged software while ignoring the profile of bespoke or lesser-known apps that may be present.

  • The capacity of current machine learning to swiftly absorb insight from fresh data and adapt is a vital component.

Given how biases contribute to false positives, it’s evident that models will need to be tailored to each company’s unique software profile.

Machine learning may be a game-changing tool for detecting new threats, and it can also be used to train against a company’s most recent software. Within the organization, there is the finest body of useful software to train with. The models can give the greatest security with the highest accuracy — and the lowest false positive rate — by training against both the widest samples of malware and the most relevant examples of good software.

Reduce false positive rates

In the following methods, machine learning systems aid in the reduction of false-positive rates:

  • Data structuring: False-positive remediation necessitates the examination of large volumes of unstructured data gathered from external sources such as news outlets, social media platforms, and other public and private information. Machine learning algorithms may assist businesses in better organizing their data by learning to prioritize and categorize data based on its relevance to specific sorts of alerts.
  • Semantic and statistical analysis: Duplicate data, frequently comprising obsolete information or incorrectly matched names, causes many false positive alarms. In order to speed up warning cleanup, machine learning algorithms can be trained to spot duplicate data based on semantic context. Machine learning systems may also be configured to do statistical analysis on historical and real-time transaction data to determine the possibility of a false positive alert categorization.
  • Intuitive screening: As a consequence of misidentification of names or misunderstanding of data, false positives frequently occur during politically exposed individuals (PEP) and unfavorable media screenings, as well as checks of international sanctions lists. Machine learning algorithms may be used to augment consumer risk profiles by intuitively offering more identifying information or clarity regarding naming standards to assist compliance teams in spotting false positives.