Machine Learning and Cybersecurity
Cybersecurity threats are something that individuals, companies, and governments have been dealing with since the 1970s. As digital infrastructure continues to advance, cybersecurity measures must develop accordingly. Modern cyberattacks have become increasingly sophisticated, easily bypassing even some of the most robust cyber defenses. With this in mind, cybersecurity experts have applied a wide range of technologies to create better defenses than we have ever seen before.
Machine learning is typically denoted as a subset of artificial intelligence and refers to computer algorithms that use experience to continually improve themselves. The lack of need to explicitly program systems makes machine learning extremely efficient and invaluable in a wide range of applications. Allowing a computer to “learn for itself” can be instrumental in targeting cybersecurity risks before they become true threats.
Companies today are tasked with the responsibility of generating, collecting, and storing large quantities of data. When data is transferred over networks there is a massive vulnerability that exists for all parties involved. Financial data, for instance, is shared with the understanding that it will be protected. Without cybersecurity measures in place, organizations are targeted quickly and clandestinely, making it extremely difficult to react after a breach has occurred.
Introducing machine learning into the protection arsenal has cybersecurity professionals excited about the future of the industry as a whole.
Using Machine Learning to Protect Your Digital Assets
While still a relatively new concept, machine learning in cybersecurity has taken notable strides in the last 5 years. When COVID-19 hit, organizations across the globe were forced to shift to a much greater digital presence. With that, came a big increase in the number of cyberattacks worldwide. In fact, a recent report from IBM points out that the average data breach in the United States costs approximately $8.2 million.
In most cases, a cyberattack will target one (or more) of the following system components:
- Applications
- Endpoints (like mobile devices or laptops)
- Network
- Users
As such, machine learning aims to actively protect all four of these system components by means of predicting when a threat is present, detecting where it is coming from, preventing breaches, and regularly monitoring all aspects of the cybersecurity framework.
So, where does machine learning come in? Machine learning in cybersecurity is a means to identify digital fingerprints of threats that have been noted in the past. In doing so, the system is able to flag new threats as it continually utilizes data and patterns previously collected. Machine learning has three major benefits:
- Automated and efficient, thus impervious to human error
- Identifies new irregular patterns
- Dynamic in that it is constantly learning and improving
Machine learning is all about finding and analyzing patterns. When there is a change in behavior, the system recognizes the shift, allowing cybersecurity personnel to be proactive in their response time and thus preventing threats.
From data collection to data organization to data structuring, machine learning must be integrated into the infrastructure. As a result, companies will find themselves with a robust protection in place to identify and prevent cyberattacks both large and small.