Adaptive Firewalls: ReinforcementLearning in Real-Time Network Defense

Firewalls are among the most important and widely used cybersecurity tools. They are especially useful today when almost every interested user produces content, trades online, and often generates income online. Over the years, firewalls have evolved and become a much more complex and secure tool.

In this article, we'll cover the concept of reinforcement learning and its application in making firewalls adaptive and, consequently, more secure. Firewalls like these will soon become the norm as users come to expect more complex tools.

The Evolution of Firewalls

Firewalls have long been an essential part of cybersecurity. Early firewalls worked based on simple rules that configured which packets could be received on a network and which were denied. The decision was made based on IP addresses, ports, or protocols.

As the threats facing users become more sophisticated, firewalls have too. New features included: deep packet inspection, user identity integration, and application-level awareness. After a while, it became clear that to remain protective, a firewall needed to learn, adapt, and act in real-time. The introduction of AI helped with this.

What Is Reinforcement Learning?

Reinforcement learning is a type of machine learning. With it, agents learn by interacting with the environment and adapting in real-time based on the changes that occur. This is also known as unsupervised learning, as the agents use a trial-and-error principle to change their behavior.

Such an approach to learning is best compared to training a dog. Once machines were able to learn in a similar fashion, security mechanisms could adapt based on changes in malware and viruses. Once a system is able to identify a threat independently, the approach to cybersecurity becomes more proactive.

Applying Reinforcement Learning to Firewalls

A firewall monitors network traffic and uses the information it receives in real-time to adjust its security posture. This is how the process works:

Input: The firewall collects data from the network. It examines packet metadata, payloads, session lengths, device behavior, and all other information that may be transmitted from the network.

Decision: The firewall then makes a decision on who to allow, who to block, and who to flag, as well as which activity to log and notify the users.

Feedback loop: The outcomes of these actions are then logged, allowing the system to track the consequences of its decisions.

Who Needs This Service?

A firewall is a service that anyone can use, and an adaptive firewall can be used in the same way. However, some users have a unique need for it. For instance, individuals who use the internet to mine and trade cryptocurrencies should invest additional time and effort into securing their systems.

Now, when almost everyone knows how to build a mining rig, a base of crypto miners is expanding. There are also tools available that enable miners to share mining resources in exchange for a subscription-based service. Experts such as those from CCN, have written about the expansion of cloud-based mining in recent years. This lowers the barrier to entry for new miners. With a wider market, there are also more security threats that investors should protect themselves against.

Adapting in Real Time

The core benefit of using an adaptive firewall is that it can adapt in real-time. Traditional firewalls had updates that were used to modify and improve the system. These updates occurred on a regular basis, and a good firewall team was the one that managed to provide updates as soon as new threats arose.

The benefits include:

Zero-Day Threat Detection: The system doesn't rely on known signatures; therefore, it can catch threats that were previously unknown.

Reduced False Positives: Firewalls can learn about ongoing traffic patterns, thereby reducing the likelihood of false positives. This allows them to distinguish between false positives and genuine threats. This means the users won't be sidetracked by threats that can't harm their data.

Automated policy tuning: The firewall no longer requires manual updates; it will adapt automatically.

Such an approach is especially useful when a user is subject to a polymorphic attack, meaning an attack originating from multiple sources and employing a range of methods to obtain the user's data and funds.

Challenges and Risks

Although the promises of using adaptive firewalls are great, and many are looking forward to trying out the new technology, there are also risks involved, as is the case with any innovation. Users, especially those working with cryptocurrency, should also be aware of these.

Training data bias

If the RL model is based on poor, skewed data, it may produce inadequate results. This may lead the firewall to overlook certain threats or teach the system to react poorly to these trends. For instance, if a firewall were only trained on corporate network traffic, it may not perform as well on public Wi-Fi.

Adversarial Attacks

Adversaries may use the training system to compromise the firewall. In these cases, actors deliberately introduce subtle, malicious traffic. It confuses the system, and sometimes it can even retrain the model, causing it to fail to react to further threats adequately. The most common way of doing so is to teach the system to trust malicious behavior and therefore ignore threats.

Explainability and Transparency

The AI used for learning and recognizing patterns has what's called a "black box problem." For instance, AI can block a specific IP, transaction, action, or behavior, and it's not easy to explain why it made that decision. This lack of transparency can be a problem for the user, as well as for regulatory compliance in the world of crypto transactions, since regulations require users to explain such behavior.

Performance Overhead

Utilizing AI to train firewalls requires substantial computing power. AI in general is very costly to use. Such problems can manifest in several ways: it may cause latency if the user lacks the necessary power, or it may require them to install additional infrastructure. If that's the case, the expenses are also transferred to the user.

To Sum Up

Adaptive firewalls refer to the more complex and secure firewalls that use AI to analyze network data and adapt their responses to threats. These firewalls are better suited for crypto miners and traders, as the loss of data also means the loss of money. As these fields grow in size and interest, such firewalls are becoming more of a necessity.

Firewalls use real-time data coming from the network and create a response based on that. At the same time, innovative technology also poses risks. For instance, it allows hackers to use the training process to make the firewall less effective.