<img height="1" width="1" style="display:none;" alt="" src="https://px.ads.linkedin.com/collect/?pid=2815180&amp;fmt=gif">
Alienvault USM Anywhere Logo
Skip to content

Machine Learning Presents Unique Challenges to Information Security

Machine learning is a game-changing technology, but there are limits to what it can automate. 

As workflows become more complex, it gets harder to complete them in an accurate and timely manner. Many IT processes can no longer be feasibly completed manually. As increasingly complex use cases become common, ML-enabled technology demonstrates the value of automating those processes. 

Use cases like image recognition and natural language processing are already well-known and widely used. Information security technologies also make widespread use of machine learning, but in different ways. Successfully deploying machine learning in a security environment requires understanding the unique demands of that environment – and training the model to accommodate them. 

Machine Learning Enables Organizations to Scale Complex Workflows 

Scalability is one of the main reasons machine learning has become invaluable to the cybersecurity industry. The process of accurately interpreting large amounts of data gets harder as the dataset grows larger.  

Most organizations have long passed the point where manual human analysis is time-efficient or cost-effective. 

Machine learning technologies enable algorithms to take on some of this work. Organizations can move beyond traditional signature-based detection methods and catch advanced attacks. Importantly, they can do this at scale, matching their security capabilities to their real-world risk profile. 

Information Security Processes Face Key Challenges Implementing Machine Learning 

Consistently detecting advanced and zero-day attacks demands more than turning on an ML-powered detection algorithm. The security tasks that machine learning models take on are not at all like image recognition or natural language processing tasks. 

Here are three major challenges facing security leaders implementing machine learning: 

  • There is no room for error 

Most industries can handle large-scale machine learning deployments with a sizable margin of error. ML-powered natural language processors aren’t generally expected to output perfect human speech with unerring accuracy. Even obvious mistakes are admissible as long as they don’t hurt the user experience. 

This isn’t the case in cybersecurity. Enterprise CISOs don’t want to tell stakeholders they’ve reduced malware infections down to “acceptable levels”. They also don’t want to accidentally block legitimate network traffic, potentially doing as much damage as a cyberattack would. 

  • Sufficient training data is hard to find 

Image recognition databases tend to be large, highly organized, and well-labeled. This is not the case for malware sample databases. There simply isn’t enough publicly available attack data. Very few organizations are willing to share sensitive attack data that showcase their own vulnerabilities in real-time – and for good reason. 

This makes it hard for technology vendors to create machine-learning models that work in a wide variety of environments. Instead, they have to build models that can be adjusted to accommodate customer environments on a case-by-case basis. 

  • Mistakes can amplify quickly 

Sometimes suspicious activity is clearly malicious in nature. Sometimes it isn’t so obvious. There is no such thing as a comprehensive, all-inclusive malware database, so security teams don’t have a universally accepted point of reference. 

Security professionals must often make decisions based on the principle of risk management.  

That means there’s always a chance their actions backfire. When automated ML-powered detection algorithms backfire, they do it on a massive scale. Machine learning can automate failures just as well as it automates successes. 

How to Effectively Use Machine Learning in the Security Environment 

These challenges are not impossible to overcome. However, they do demand the attention of security leaders looking to implement solutions for meeting their needs at scale. The ideal security operations environment puts highly automated ML-enhanced tools in the hands of human experts who can use them effectively. 

For example, Exabeam primarily uses machine learning in three fundamental ways: 

  • Regression uses the relationships between different datasets to predict how they impact one another. In a security context, it can predict the next system call of an operating system process and generate an alert when the next call differs from that prediction. 
  • Classification takes data artifacts and classifies them according to pre-established training data. It’s useful for separating binary files into categories like adware, spyware, ransomware, and legitimate software. 
  • Clustering identifies common characteristics between objects, and groups them accordingly. It can identify DDoS attacks by identifying groups of traffic sessions that may originate from the same source. 

This approach makes machine learning an excellent tool for analyzing the behaviors of network users and entities, automating application security, and monitoring emails. By focusing on behavioral data, it expands antivirus capabilities beyond whatever threat signatures are currently available. That enables analysts to detect unknown threats and zero-day attacks in ways that traditional solutions can’t. 

10 Use Cases for Machine Learning in Behavioral Analytics 

User Entity and Behavioral Analytics (UEBA) technology uses machine learning to identify suspicious activity at the user level. Machine learning plays a vital role in picking out anomalous activities for analysts to investigate. This expands the number of security scenarios that security teams can effectively address: 

  1. Compromised user credentials 
  2. Privileged-user compromise 
  3. Executive assets monitoring 
  4. Compromised systems, hosts, and devices 
  5. Insider access abuse 
  6. Detecting lateral movement 
  7. Data exfiltration detection 
  8. Detecting repeated account lockouts 
  9. Identifying service account abuse 
  10. Investigating security alerts 

Maximize the Value of Machine Learning in Your Security Processes 

While undoubtedly popular and valuable, machine learning isn’t a cybersecurity cure-all. Like any tool, it can be misused, improperly implemented, or made redundant. To make the most of machine learning, security leaders need to deploy systems and policies that cater to the technology’s strong points and address its weaknesses. 

Castra’s SIEM expertise is a valuable asset for any organization looking to automate time-consuming security tasks using ML-powered technology. Discover how we can help you optimize your machine learning deployments by building custom training models that fit your unique security needs.

Schedule a demo with a Castra expert to learn more.