Data privacy has always been a top priority and concern for every business. But as the pandemic has led us to spending more time online, businesses are having to be more diligent when it comes to protecting their networks.

Networks and systems require a lot of frontline protection, which is where intrusion detection systems come in. These systems can be based on rules, or by an algorithm. Below, we’ll talk about what intrusion detections systems are, and the role AI has played in improving them. 

What are intrusion detection systems? 

Intrusion detection systems are essentially a protective shield, and can be created on a few bases. The first is rule-based IDS, which subjects all traffic to a set of rules before allowing anything to pass through. Think of it almost like a security checkpoint during a lockdown situation. 

The second IDS is algorithm based, and uses machine learning to create new detection algorithms based on traffic. 

Whether an IDS is rule based or algorithm based, both are effective in analyzing and identifying traffic whether it’s good or bad. The reason many IDS systems are AI based today is because technology has become so readily accessible to network security teams and cyber attackers. However because threats are evolving at such a rapid pace, it’s becoming harder to develop sets of rules for the machines. But what if the machines could write the rules themselves? That’s where AI comes in.

How is AI improving intrusion detection systems? 

The pandemic has led us to see the highest rates in cyber attacks the world has ever seen. To combat the attacks, IT teams are looking towards AI to give their security efforts a leg up. However, implementing this is easier said than done. One of the challenges in making the switch is called adversarial AI. Modern day IDS does a good job at preventing regular intrusions, but isn’t strong enough against adversarial attacks. These attacks occur when cyber criminals inject malicious false positives and negatives into AI training data. It’s essentially fooling the system to allow malicious traffic to pass through.

To combat the adversarial AI, experts are looking to deception technology. Deception technology consists of a set of traps that are placed in specific areas around the network. These traps will get the attackers attention, and make it harder for them to locate where the real assets are. It creates better visibility on your end to see and meet attackers where they are.

The unfortunate thing about deception technology is that it doesn’t always learn from previous attacks, which makes it harder to improve. However, AI can learn and improve over time. 

If you’re looking to improve your security technology, now is the time. Organizations everywhere are at risk, especially during the pandemic. Thinline Technologies can help streamline your security tech stack and provide IT consulting services that will push your business to the next level. Visit our services page to learn more, and contact us today.