Is Machine Learning the Future of Cybersecurity?

There has been a lot of fuzz about the use of artificial intelligence in cybersecurity. In this article, we’ll detail how AI and machine learning, in particular, can help as well as talk about the faced limitations.

What Machine Learning Bring to the Cybersecurity Table

Without the help of artificial intelligence (AI) and machine learning (ML) in cybersecurity, systems will be hard-pressed to cope with the intensity and sophistication of today’s cyber attacks. Hackers and cybercriminals have access to some of the most current, cutting-edge technologies, and they do not hesitate to use these against their targets.

These are some of the ways ML strengthens cybersecurity systems:

  • ML helps spot and analyze patterns that human analysts can easily

overlook within mountains of data. Once spotted, the systems learn from these patterns and formulate the most appropriate responses to threats. But perhaps the most useful benefit from ML is that it helps cybersecurity systems adjust in real-time to changes in attack behavior. The ability to respond quickly and appropriately to threats minimizes the damage from attacks.

  • It empowers cybersecurity teams to hunt for threats before they take place. Preempting attacks also limits the damage that can be done, especially disruptions in services and operations. An ounce of prevention, as they say, is better than a pound of cure.
  • It helps make security activities more efficient. Cybersecurity teams can focus their efforts on dealing with threats discovered and confirmed by ML. As a result, the organization can use its resources more strategically.

But is it a walk in the park for machine language in cybersecurity?

A Few Difficult Lessons to Learn

As it turns out, there are a few factors that can limit ML’s effectiveness in cybersecurity.

  • ML needs data — lots and lots of it — so that it can learn from as many potential scenarios as possible. More data allows ML to process patterns using sophisticated algorithms, and thus produce more likely outcomes from given scenarios. When the data set is too small and does not represent the actual environment the machine learning model needs to train in, then you may face a condition called “sample bias”.
  • But quantity isn’t all that matters. The data must also be of high quality. It should be relevant, and would most likely require additional cleaning and organizing to be of value to the ML process. It must be rich and detailed, providing as much information as possible about machines, applications, protocols, sensors and all other components that make up the computing and information system.
  • ML systems must be able to adapt immediately, efficiently and effectively to situations when data changes. They must be able to respond to threat situations in real-time. Until they are able to do so, they cannot effectively cope with intense attacks.

Machine Learning Applications in Cybersecurity

Machine learning in cybersecurity is expected to grow as much as sevenfold from 2016 to 2020. Even now, we are seeing more and more applications of ML that can boost cybersecurity applications. Here are a few that ML can help protect against.

  • Spear Phishing: New and sophisticated phishing attacks are able to get past conventional cybersecurity. ML provides the speed and accuracy to reliably predict if links are malicious. It can detect the most subtle deviations from characteristic user behavior, and determine whether these constitute a threat.
  • Watering Hole: ML can help analyze the paths traversed by users within a site. It can then determine if users are being directed toward malicious sites.
  • Webshell: E-commerce platform backends are often the targets of webshell attacks, which insert code that accesses the system database. ML can be trained to spot questionable user behavior that may indicate the presence of webshell scripts.
  • Ransomware: ML can be trained to recognize ransomware attack patterns and trigger the appropriate countermeasures.
  • Remote Exploitation: ML is used to analyze network behavior and detect signs of remote exploits. Patterns that attempt to take advantage of network vulnerabilities may point to DDoS, DNS poisoning, port scanning, and other such threats.

Machine learning is undeniably proving itself indispensable as a cybersecurity tool. But there are some elements that need to be provided for ML to truly be effective.

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