Neurosymbolic AI for Autonomous Cyber Defence

In the digital era, organizations are facing a huge cyberattack storm. The cybersecurity statistics state that there are around 4000 cyberattacks happening daily. Another report from CrowdStrike suggests that 79% of the attacks are now malware-free, which means that they don't use traditional malware anymore. On top of that, generative artificial intelligence (AI) has become a new tool for cybercriminals to deceive organizations. To tackle these rising concerns, organizations must evolve their defense. 

AI is also a great tool for cybersecurity experts to strengthen the walls. Over the last few years, AI systems have been extensively used to identify potential threats. As AI itself comes in various forms, like neural AI (deep learning) and symbolic AI (logic and rules), experts are now combining them to develop neurosymbolic AI for cyber defense. It is believed to bring a revolution in cybersecurity by making cyber defense smarter, safer, and more reliable.

What exactly is neurosymbolic AI?

It is the combination of two traditionally distinct technologies, which are neural and symbolic AI, for their benefits in one.

Neural AI: Also known as deep learning, they are extensively used to process a large amount of data in less time and are good at identifying patterns, malware, and network logs. For example, it can detect unusual login behavior, strange traffic across a network, and phishing emails. However, Neural AI fails to explain the reason behind its outcomes. Sometimes, it can also generate false alarms, which can waste valuable time. 

Symbolic AI: This AI is built on logical commands, rules, and structured knowledge. It gives a reason for the raised threats. For example, a command can be provided that if a single person logs in from two different places, then raise a threat alarm. It is highly explainable, and security teams can easily trace back a certain alert. But symbolic AI struggles to identify new or unknown threats that are outside the existing rules. 

Neurosymbolic AI merges these two approaches and can be used to not only learn from large databases but also reason through them using a structured logic. This hybrid model can identify threats from a large pool of data, explain the reason behind the threat, and take faster actions to protect the system. Another advantage is that it can learn from feedback, past data, and responses and adapt with time to tackle new challenges. 

How does it work? 

There are various steps involved in the process:

1. Data Scanning: The neural AI receives a large amount of data from which it learns and studies the normal and strange ongoing activities. It identifies patterns and unusual activities and attempts to infiltrate. 

2. Pattern Spotting: It processes unstructured data to identify potential threats and raise concerns over suspicious or unrecognized behaviors.

3. Symbolic Reasoning: The symbolic AI checks, reasons, and filters the flagged data. It only raises the concern after applying all the embedded logics and rules to the anomalies. 

4. Decision making: After combining the insights from both components, the system decides to take desired automated actions like stopping access to webpages, segregating the compromised device, or blocking logins. It further develops a readable explanation for the security team to preview the steps taken and track the record. 

5. Learning and adaptation: The system keeps improving its model and rules over time, based on the learning from past experiences, feedback, and actions. It keeps itself updated for any further attacks. 

Uses of Neurosymbolic AI

It holds various advantages and is used in various scenarios, like

1. Threat hunting: The AI can intensively search for hidden threats by analyzing millions of patterns in record time. Even without the prior knowledge of the attack, it can raise concerns in the meantime and stop further exploitation. Since it doesn't rely on 

2. Intrusion Detection: It can identify unauthorized access in real time, like the lateral movement of hackers in the network. 

3. Malware analysis: By combining the power of both AIs, it can break down malicious code structures and identify them more swiftly than traditional methods.

4. Automation: Neurosymbolic AI can do various repetitive tasks like log analysis, alert triggering, and basic incident response, reducing the workload of human analysts. 

5. Mitigate Zero-Day Attacks: Since it doesn't rely solely on records, it can identify new threats and vulnerabilities and help in predicting patching and isolation, even before it becomes a recognized threat. 

Read Also: The Cybersecurity Innovations That Will Define the Next Decade

Challenges

Bringing them together will be a difficult task for engineers, as it will require carefully curated structures with rule-based logic and advanced tools. It can further lead to computational overhead, as symbolic reasoning can slow down the performance. They can be both data-dependent and expensive.