10 Things to Know About Machine Learning

Machine learning (ML) tools can be an essential element of a dynamic and powerful security platform. ML can be used for a myriad of tasks within the cybersecurity space, including malware detection, network anomaly detection, user behavior categorization, vulnerability prioritization, and more. Ultimately, our goal for using ML is to improve model risk, streamline classification of threats, and accurately predict immediate and future attacks. Below are the top 10 things to keep in mind when considering implementing ML into your cyberspace.  

1. Cybercrime is evolving, and we must remain one step ahead
   As technology continues to rapidly develop, attackers are advancing their techniques for phishing, ransomware attacks, malicious campaigns, and more. It’s imperative that cybersecurity utilizes cutting edge technology that can be bolstered using ML.


2. Supervised and unsupervised learning are the two main components of ML
   Supervised learning involves using past data to help the algorithm identify good data from fraudulent data. It uses past learned behaviors to then predict outcomes that follow that same set of guidelines.
   
   Unsupervised learning uses clustering, which is grouping unlabeled examples together by identifying similarities between data. Within cybersecurity, unsupervised learning can pair fraudulent or anomalous data together. 
   
   Supervised learning is beneficial for classification. When given information about what potential threats look like, a machine can detect and pull out threats from data. Unsupervised learning, on the other hand, takes in data without a specific set of instructions and begins to put similar pieces of data together. This means it clusters and organizes data – like threats – in groups because of their similar qualities. 


3. Data is key
   In order for the ML algorithms to properly run and produce the desired outcome, a high volume of quality data must be imputed. When inputting large data sets, it’s important to keep in mind that your data needs to represent the threats that are expected to attack in order for the ML tool to properly do its job. You must also ensure that the data is up to date.


4. Data must speak the same language in order to effectively work with ML
   If data comes from different sources that don’t interact well with each other due to differences in data type or categorization, it can be difficult for a machine to sift through and determine what’s relevant. Data should come from one cohesive source to enable the algorithm to work at its best capacity. 


5. ML is predictive, not deterministic 
   ML deals with probabilities and likelihood of outcomes. This means it will take the data it’s given and use those previous outcomes to predict potential outcomes in the future. While these are not deterministic, they are typically very accurate and can be done at much higher speeds than could be done by a human. 


6. ML can provide security-specific techniques to overcome inefficient or impossible problems that traditional methods cannot solve 
   ML can aid in finding novel insights that slip past humans. It can also reduce the burden on workers by easily maintaining detection rules. Instead of manually ensuring that data follows the specific guidelines set out by a company, ML can automatically maintain security rules at faster speeds. It can be scaled, which allows it to grow the data it’s able to run, resulting in improved efficiency. 


7. Learning rules for regression, classification, clustering, and association is an important and common task
   Regression is similar to supervised learning in that it predicts the next output based on the previous outcomes. In cybersecurity, this can be used in fraud detection.
   Classification and clustering separate data into groupings or categories, and clustering specifically groups based on similarities presented in the data. Through ML, classification can make categories to distinguish spam from permitted and true data.
   Association rule learning uses past experiences with data to recommend a specific outcome at a much faster rate than a human could. If an incident occurs on a site, association rule learning can be implemented to recommend solutions to users automatically. 


8. While ML has become integrated in nearly every aspect of cybersecurity, it’s important to recognize its limitations
   ML algorithms are extremely efficient at recognizing patterns and making predictions. However, ML requires a lot of resources and still has a relatively high error susceptibility, because all data sets are limited in scope. ML can also be subject to exaggerations by the media, claiming it’s more powerful than it actually is.


9. The people implementing cybersecurity are just as important as the algorithm
   Maximizing the output of ML-based cybersecurity algorithms requires a cohesive effort between the person and the machine. While ML algorithms can carry out the brute data analysis, it’s critical that the team stays up-to-date with the latest breakthroughs in technology and possible threats.


10. ML for cybersecurity should integrate easily with existing software and architecture
    When implementing new ML techniques in your business, remember that it should simplify your experience and not cause tension. It’s beneficial to choose an ML solution that will integrate well with your current software and programming to get the most out of the implementation. 
    In summary, ML for cybersecurity should:
    • Be targeted towards specific goals and purposes
    • Minimize false predictions
    • Have a method for evaluating its effectiveness
    • Have a strong team working alongside it

How is Palo Alto Networks leveraging ML to protect enterprises from tomorrow’s threats? Our ML-Powered NGFWs use machine learning to prevent common file and web threats with a 95% success rate. Our firewalls detect three times more IoT devices and utilize ML to create a less than ten-second signature delivery, resulting in a 99.5% reduction in systems infected. ML is helping us create a safer, more secure environment for our partners and customers. 

Learn more about ML here at Palo Alto Networks in this Cyberpedia article: What is Machine Learning?

Palo Alto Networks, the global cybersecurity leader, is shaping the cloud-centric future with technology that is transforming the way people and organizations operate. Our mission is to be the cybersecurity partner of choice, protecting our digital way of life. For more information, visit www.paloaltonetworks.com