The Dos and Don'ts of Machine Learning in Computer Security

Machine learning might sound a little like a concept from a science fiction movie, but it’s actually quite similar to how humans learn and improve daily. Machine learning, or ML, is a computer’s way of improving its abilities by learning how to perform tasks more effectively.

Just like humans learn from mistakes when practicing math or soccer, computers use data to learn and make decisions, self-correcting over time to get smarter and smarter the more it gets used.

And just like there are dos and don’ts of any online tool, there are dos and don’ts of machine learning in computer security, too.

Machine learning in computer security helps quickly spot when something unusual is happening that might be a security threat. For instance, if an employee’s computer suddenly starts sending out several emails in the middle of the night, tools that have machine learning for computer security can help figure out if something’s not right before it’s too late.

However, just like any tool, machine learning isn’t perfect!

What is Machine Learning in Computer Security?: The Dos and Don’ts of Machine Learning in Computer Security

Let’s first get a little deeper into what machine learning for computer security is.

Machine learning is different from traditional computing where a computer just follows specific instructions from a programmer.

With machine learning, a computer looks at a lot of data, learns what’s normal and what’s not, and then makes decisions based on what it’s learned to educate it moving forward. In security, this might mean detecting when someone’s trying to break into a system or understanding when there’s a virus trying to spread.

Machine learning uses different types of data to learn about potential threats. This data can be anything from…

• Network traffic data
• User behavior data
• System logs
• Threat intelligence feeds
• Historical data on cybersecurity incidents

By understanding what’s normal, the machine learning system can alert humans when something unusual happens, helping prevent potential attacks before they cause harm.

The Dos of Machine Learning in Computer Security

When analyzing the dos and don’ts of machine learning in computer security, let’s start with the ‘do’s first so you know what you should be looking out for with proactive risk management:

1. Ensure Data Quality

Just like you can’t make a great cake with bad ingredients, you can’t expect a computer to learn properly with poor data! As they say, “garbage in, garbage out;” the quality of the data you feed into your ML model matters.

What does this mean? Cleanse and prepare your data carefully. This might look a little differently for everyone, but it could include:

• Removing errors
• Filling in missing values
• Getting rid of irrelevant information

This could take a lot of work manually, which is why there’s data scrubbing software to help do this for you.

2. Choose the Right Algorithms

There are lots of machine learning algorithms out there fit for different needs – so make sure you choose the right one.

Decision trees are great for clear, simple decisions.

Neural networks work well for more complex problems.

Clustering algorithms are also used a lot in security to find unusual patterns or group similar things together.

When you’re choosing an algorithm, think about what you need it to do. How complex is your data? How fast do you need the algorithm to learn? How much data do you actually have?

Sometimes, you might need to try a few different algorithms to see which one works best for your specific security challenges.

3. Continuously Update Models

The online world changes fast, which means new threats are popping up all the time.

Unfortunately, this means that even if your ML model was perfect a few months ago, it might not work perfectly now. Models can drift off track, especially when the data they learned from no longer represents its current environment.

To do this, you can use strategies like incremental learning, where the model learns from new data as it comes in without starting from scratch each time. This keeps your model up-to-date and ready to face new threats.

The Don’ts of Machine Learning in Computer Security

In our dos and don’ts of machine learning in computer security, we now know all the ‘do’s, and great ways to apply them. But what should you look out for? What pitfalls can you avoid to help ensure your ML initiatives are both effective and responsible?

1. Neglecting Privacy Concerns

One of the biggest mistakes you can make when using ML in security is overlooking privacy issues. With all the sensitive data involved, there’s a real risk of privacy breaches, which is why before anything, it’s crucial to comply with data protection regulations like GDPR in Europe or CCPA in California. These laws specifically help protect personal information, and following them isn’t just about avoiding fines—it’s about respecting user privacy.

To safeguard data, consider using anonymization techniques, which strip out personally identifiable information from the data sets you use for training your ML models.

You should also make sure your data is stored securely, using encrypted databases and secure access protocols. Not paying attention to these privacy aspects can damage your company’s reputation and lead to serious legal issues.

2. Over-Relying on Automation

ML can do a lot, but it can’t do everything. A big “don’t” in using ML for security is depending too much on automation. It’s tempting to set and forget an ML system, but without human oversight, you might miss out on nuances that only a human can catch.

Human intuition and decision-making are often necessary, especially in complex or ambiguous situations. For instance, ML might flag unusual but harmless behavior as a threat because it doesn’t fit typical patterns.

A human can look at the broader context and understand that it’s a false alarm – a machine can’t.

Remember, ML is here to assist, not replace, the security professionals. You’ll need to workshop and experiment what the right balance is for your organization between leveraging technology and maintaining human oversight. 

Good security practices often involve a partnership between both human and machine, where each play to their strengths. For instance, ML can handle routine monitoring at scale, while humans step in for decision-making and handling exceptions.

3. Ignoring Model Explainability

Model explainability is being able to understand and explain how your ML models make decisions.

The challenge with many ML models, especially the more complex ones, is that they can become “black boxes.” This means it’s not clear how they arrive at their conclusions.

This lack of transparency can be a big problem, particularly when you need to justify how they’re making decisions, like why they’re flagging certain activities as suspicious.

To improve the explainability of ML models, you can use techniques like feature importance scores, which help show which parts of the data were most influential in the model’s decisions. Visualization tools can also help by making it easier to see what the model is doing and why. Both of these methods help enhance transparency and make it easier for teams to work with these models effectively.

The Do’s: Explore Qohash!

Now that you know the dos and don’ts of machine learning in computer security, explore tools to help you streamline your security posture and data management so you always have a system for keeping your data safe.

Qohash offers two powerful tools: Qostodian and Qostodian Recon. Qostodian Recon helps you gain accurate data visibility in minutes, and Qostodian helps monitor your data 24/7, alerting you when non-compliant behavior occurs so you can proactively reduce your organizational risk.

Our products use a risk scoring function to help you prioritize your risk reduction engagement where the largest threat lies. Explore Qostodian today!