An Overview of Artificial Intelligence in Cybersecurity

The evolution of AI in cybersecurity can be well described by the generally accepted three-wave progression of AI:

• First Wave: Rule-Based Systems
• Second Wave: Supervised Machine Learning
• Third Wave: Unsupervised Machine Learning

As the AI waves progress they don’t necessarily replace each other but rather add layers of complexity and capability to existing systems. Let’s take a closer look at the three-wave progression, and how it has taken place in cybersecurity.

1. Rule-Based Systems

The first wave of AI in cybersecurity emerged in the 1980s with simple, rules-based systems. By implementing conditional if-then statements – a rudimentary task for any modern coder – systems could automate security tasks. These rules-based tools automated security analysis and response using knowledge curated by human experts. Examples of these systems include signature-based virus detection, automated firewall policies, and network policy monitoring techniques.

The strength of these systems lies in their ability to reliably catch known threats. Once a particular form of malware or a specific attack pattern has been identified, a rule can be created to detect it, and the system will catch it every time. In the earlier days of cybersecurity, when the types of cyber attacks were relatively few and well-defined, rule-based systems were a powerful defense.

However, rule-based systems suffer from significant limitations. The obvious weakness is their inability to detect threats that haven’t been seen previously – known as zero-day attacks. They are also prone to generating false positives from improperly defined or outdated rules. Rule-based systems require constant updates as new threats are identified, and this process can be resource-intensive from both a computational and resourcing perspective.

As networks and attack surfaces continued to expand, rule-based systems became increasingly challenging to sustain, and less effective as a stand-alone security solution. These challenges underscored the necessity for the next evolution in cybersecurity, paving the way for the second wave: machine learning systems.

2. Supervised Machine Learning

The second wave of AI in cybersecurity allowed for significant progress in threat detection and response with the implementation of Machine Learning (ML). By implementing artificial intelligence algorithms that learned from and made decisions from data, machine learning allowed cybersecurity systems to make decisions without explicit programming. Supervised machine learning is still used in a wide range of modern cybersecurity tools.

Machine learning allows smarter rule-based systems to be deployed that can detect irregular behavior, even if it hasn’t seen that exact behavior before. ML systems understand what ‘normal’ behavior looks like based on training data and can identify deviations from the norm. Machine learning can analyze vast quantities of data with ease allowing these systems to scale to meet increasing demands. The implementation of ML systems has also seen a decrease in false positives that plagued rule-based systems in the past.

However, the integration of ML into cybersecurity has introduced its own set of challenges. ML systems require massive amounts of labeled data for training. Care must be taken to ensure the quality of data used for training ML models to prevent data poisoning – a type of adversarial machine learning attack. Another challenge is ensuring the data used for training ML models doesn’t quickly become outdated, in some cases reducing the effectiveness of the model before it is even implemented. Another issue is the “black box” problem, a characteristic of machine learning algorithms. These algorithms don’t readily offer explanations for decisions which makes it difficult to troubleshoot false positives and other decisions made by the system.

With the difficulties faced by supervised machine learning systems, the need for more adaptive and self-reliant systems has become apparent. This has led to the exploration and implementation of unsupervised machine learning.

3. Unsupervised Machine Learning

The third wave of AI is characterized by systems that learn from experience and can understand, reason, and adapt based on context. The focus is to move beyond supervised learning models that need extensive amounts of high-quality labeled data, and instead lean towards systems that can learn, and utilize data in real time. This approach can allow the discovery of patterns in new data without being given explicit instructions and can adapt their strategies based on the feedback they receive.

Unsupervised ML serves to improve many of the drawbacks seen with supervised ML. These smart algorithms have enhanced anomaly detection capabilities, and are less reliant on extensive labeled datasets. They can recognize previously unknown threats by detecting deviations from the norm, much like supervised algorithms, but without needing extensive prior knowledge of what ‘normal’ looks like. This ability to understand and learn from context significantly enhances the system’s ability to detect novel threats.

Despite these advancements, unsupervised ML faces its own unique set of challenges. The outputs from unsupervised learning are often less interpretable than those from supervised learning. This means that while these algorithms may be excellent at detecting anomalies, understanding why they flag certain activities can be challenging. In addition, adversarial machine learning attacks are becoming more commonplace. In these attacks, cybercriminals use sophisticated techniques to fool machine learning models into behaving in their favor and giving false outputs.