How Deep Learning Enhances Intrusion Detection Systems

Prevention is always better than a cure, but no proactive measure is foolproof. Cyberattacks are too frequent and the consequences too severe for organizations to assume nothing will ever breach their defenses. Teams must also recognize and respond to incidents quickly, necessitating reliable intrusion detection methods.

While automated intrusion detection is far from new, conventional approaches often fall short. Rules-based algorithms fail to catch many attacks and may flag legitimate actions as suspicious. Deep learning has emerged as a more effective alternative for several reasons.

Protection Against Novel Methods

The most prominent advantage of deep learning-based intrusion detection is that it goes beyond known attack indicators. While previously recorded signatures are useful, cybercrime evolves quickly. Threat actors used 23 new malware strains and researchers discovered 30,000 novel vulnerabilities in the second half of 2023 alone.

Deep learning can account for these as-of-yet-unseen attack methods because it considers a more nuanced range of factors. Instead of comparing activity to known indicators, it also looks for anything falling outside the baseline of normal behavior. Similarly, it can recognize general attack trends regardless of the specific steps or malware signatures.

Deep learning can identify zero-day attacks by considering known signs of suspicious behavior and normal baselines. This edge will become more important as cybercriminals increase their own AI adoption, spurring faster development of new strategies.

User and Entity Behavior Analytics

Deep learning can more accurately identify compromised insider accounts or devices. While multifactor authentication offers some protection, 54.8% of cloud compromises still come from weak passwords. Leaked credentials account for another 7.1%. Consequently, intrusion detection methods must be able to spot stolen or hacked accounts — something deep learning excels at.

This protection starts with user and entity behavior analytics (UEBA), which monitors normal behavior to spot outliers, suggesting a breach. While UEBA is possible without deep learning, it needs its nuance and ability to process large amounts of unstructured data to be reliable.

Deep learning can recognize when an account or device is not acting as it normally would almost instantaneously. These quick responses are becoming more important as ransomware demands have risen by 518% in recent years. Stopping a compromised account before it accesses sensitive data ensures businesses avoid significant losses.

Reduced False Positives

Across both these use cases, deep learning also produces few false positives. Rules-based methods or those using simpler AI algorithms struggle here because legitimate actions do not always follow strict parameters. While it is better to be safe than sorry, these false alarms lead to alert fatigue, which 76% of security workers say slows them down.

Unlike conventional methods, deep learning enables contextual analysis. Some actions may be suspicious for one user at one time of day but normal for another, and deep learning models can pick up on these nuances. By minimizing false positives, intrusion detection algorithms ensure strained IT resources go where they’re actually needed.

Deep learning models may still produce some false positives, but they’ll learn from these instances over time. Each correction provides a greater body of reference to judge activity against, leading to ongoing improvement.

Lightened Workloads for IT Staff

It’s also worth considering how deep learning can automate more intrusion detection and response steps than other methods. Because these models feature several layers of neural networks, they can handle multiple tasks in succession, each informing the other.

For example, they may detect suspicious activity, automatically contain the account in question and assign a risk score for more effective triage. This automation improves response speeds, but more importantly, it reduces the workload on security teams. As a result, IT staff can either handle more reports in a day or manage the same amount with fewer errors.

Reducing human workloads is crucial because 59% of cybersecurity leaders say their teams are understaffed, and 56% struggle to retain workers. Taking some of the load off will prevent burnout, improve retention and let a smaller team accomplish more work to compensate for shortages.

Reliability in Complex Network Environments

Deep learning also has the benefit of processing large volumes of unstructured data. This advantage is important for two reasons. First, it streamlines the model training phase, enabling faster deployment. Secondly, it means intrusion detection can cover a more complex environment and remain accurate.

Hybrid working environments and rapid SaaS adoption have led to increasingly complex networks. As a result, 49% of IT professionals say they only have visibility into half of their infrastructure. That lack of transparency makes it challenging to run effective anomaly detection, but deep learning can account for it.

Unlike simpler algorithms, deep learning models do not require much structure in their training or input data. Consequently, an environment that may be too complex for human experts to understand is not a challenge for these solutions. This ensures that IT expansion does not come at the cost of response times.

Deep Learning Is Imperfect But Provides Significant Improvements

Cybersecurity teams must recognize that deep learning introduces complications like bias and the threat of data poisoning attacks. These issues deserve attention but do not counteract this technology’s benefits. While deep learning still requires careful implementation to be reliable, it can revolutionize organizations’ intrusion detection.

Threat actors are not shying away from AI. It is time for security experts to follow suit and embrace deep learning’s speed, accuracy and adaptability.