Gartner has predicted that machine learning will be a standard part of cybersecurity by 2025. The need for machine learning has become more urgent with hackers constantly developing new tools and techniques for carrying out attacks, many of which are automated to overwhelm security defenses.
Machine learning is a form of artificial intelligence that uses advanced algorithms to identify patterns and anomalies and predict outcomes without being programmed to do so. The more data the application consumes, the more accurate it becomes, and the more complex problems it can solve.
Today’s threat climate makes it virtually impossible for human security analysts to process security data fast enough to identify and contain threats before serious damage is done. Many warning signs are buried in unstructured data and, therefore, extremely difficult for humans to detect. An ever-widening cybersecurity skills gap only makes it more challenging for organizations to keep up with modern hackers.
Machine learning can shoulder much of the burden of collecting, aggregating, correlating and analyzing massive amounts of data from multiple security tools and threat intelligence sources. The job of machine learning is to make this data more understandable and separate legitimate threats from false positives. This enables security teams to focus their investigations on the most serious threats and take action quickly.
Machine learning is particularly valuable in defending against malware attacks, which continue to skyrocket. In fact, there were 5.99 billion recorded malware attacksin the first half of 2018, twice the number from the same period in 2017. To make matters worse, more and more hackers are using machine learning to identify and exploit vulnerabilities and update their malware code so that it can bypass security software.
Anti-malware solutions that can only detect known threats based on existing signatures are useless against new, zero-day malware that can find and exploit vulnerabilities on the same day. Organizations can keep up with hackers by using machine learning to identify malware that has never been seen before. Machine learning can’t necessarily tell you that certain code is malware, but it can learn the behaviors and traits of files and websites that would indicate malicious intent so that security teams can investigate.
Anti-malware tools driven by machine learning can do much of the monotonous heavy lifting involved with detecting and classifying threats, and they can do it much more quickly and efficiently than humans. These tools can also help to pinpoint the origin of an attack to prevent a repeat occurrence.
IntelligentAV from WatchGuard uses a machine learning engine to not only identify new malware threats, but to predict threats long before they’re released. Predictive protection improves your defenses against evolving malware by automatically breaking down millions of files into their building blocks so that they can be analyzed at a granular level. No cloud connectivity, signatures or behavioral analysis are required. This makes it possible to identify indicators of malicious intent, classify files as malicious or safe, and block malware before it can execute, all in a fraction of a second.
Machine learning has become a critical tool in stopping malware attacks, especially for organizations with limited in-house security personnel and expertise. Let us show you how WatchGuard IntelligentAV uses predictive protection to classify any file and identify new, never-before-seen threats in real time.
