There is no escaping the discussion about how machine learning (ML) and AI systems will revolutionize how people and industries work. Most of this discussion needs to be revised, as companies are still evaluating how AI systems (typically Large Language Model (LLM) systems like OpenAI ChatGPT, Google Gemini, Anthropic Claude and others) enhance worker productivity and deliver business benefits.
Cybersecurity is one sector where extensive use of AI-enhanced solutions is common. But what does machine learning and AI entail? And how do they relate to other techniques we use in cybersecurity defense?
Understanding Artificial Intelligence
AI is a broad field that focuses on creating intelligent systems that can perform tasks that typically require human intelligence. AI systems can learn from data, adapt to new information or inputs and solve complex problems, making them valuable tools for many complex tasks.
ML is a component of AI. At its core, ML helps a system learn and improve from experience without being explicitly programmed. Neural networks, inspired by brain anatomy, are a key component of many AI systems, allowing them to recognize patterns and make decisions. A common application of AI is in image recognition, where an ML model is trained on a large dataset of labeled images to identify patterns and features associated with each label, allowing it to classify new, unseen images accurately. This works for many classes of images, such as medical X-rays. It’s not just for pictures of cats!
The LLMs mentioned above that have taken the world by storm in the last 18 months use a combination of supervised and unsupervised ML techniques during their training. Most of the training time for an LLM model (like ChatGPT) is typically via self-supervised learning. In this, the model gets trained to predict the next word or token in a sequence based on the preceding words without having explicit labels. This allows the models to learn patterns and relationships in language from vast amounts of unlabeled text data such as books, the Web, newspaper archives and much more.
We’ll lightly skip over any copyright questions that arise when LLM vendors gather text to train their models. The courts will answer those questions, or they will be rendered moot via licensing deals for content, such as the recent deal between OpenAI and The Financial Times.
It’s important to point out that LLMs use ML methods, but LLMs are not the only way to create AI-based systems, even if they are in the spotlight in 2024 and occupy a large portion of the AI discussion landscape. Other techniques, also using ML, that fall under the AI umbrella have been in use for years, delivering benefits across many industries and sectors—especially in the cybersecurity space.
AI Techniques in Cybersecurity Tools
In cybersecurity, ML techniques enhance the capabilities of various tools, particularly Network Detection and Response (NDR) solutions.
ML algorithms can analyze enormous amounts of network data in real time, enhancing the ability to identify anomalies and potential threats before they can cause significant damage. Supervised learning techniques use labeled datasets of known cyberthreats to classify new and unknown threats. Alternatively, unsupervised learning techniques such as clustering and anomaly detection can identify uncommon patterns or behaviors without relying on labeled data.
Deep learning models, such as Convolutional Neural Networks and Recurrent Neural Networks, can learn complex patterns and relationships in network data. These models help detect sophisticated threats by analyzing network traffic data, including packet headers, payloads and flow information. ML models train using various approaches to recognize normal network behavior and then flag anomalies that could indicate potential threats such as unauthorized access, malware infections or data exfiltration. By continuously learning and adapting, the models in ML-based tools can detect cyberthreats at an early stage, often before they cause significant damage.
Progress Flowmon harnesses the power of ML to detect and mitigate cyberthreats by identifying anomalies in real time. It combines supervised and unsupervised learning techniques to detect known and unknown threats. Flowmon AI models get trained on vast amounts of historical network data, and they continuously update and learn as they function. When new threat patterns emerge, the AI can learn and adopt practices.
AI Techniques Enhance Other Detection Methods
The AI-based detection methods in Flowmon NDR enhance the other traditional techniques that still have a place in cybersecurity protection. These other non-AI-based techniques, such as heuristics and pattern matching, combine with the AI methods to deliver robust security capabilities. This combined approach means the AI models in Flowmon can identify suspicious network behavior, such as unusual data transfers or abnormal user activity, while its heuristics and pattern-matching techniques can detect known and unknown threat signatures and anomalies.
Heuristics and pattern-matching are fundamental techniques used in many cybersecurity tools, including NDR solutions. But, they still have a place in a modern defensive posture.
Heuristics are practical rules that use experience-based techniques to identify threats quickly. These rule-based methods allow quick decision-making based on characteristics commonly observed in malware or other attack types. When integrated with AI, heuristic approaches reduce false positives while prioritizing genuine threats and refine cybersecurity solutions’ detection capabilities.
Pattern matching involves searching for specific sequences in data. It is good at identifying the known threat signatures found in many malware variants and viruses. However, cyberthreats are increasingly sophisticated, often bypassing traditional pattern-matching techniques. This is where AI amplifies pattern matching, as it can learn and identify variations or entirely new patterns that indicate malicious activity, greatly enhancing the effectiveness and responsiveness of cybersecurity defenses.
AI-based detection methods in Flowmon integrate with traditional techniques to provide a multi-layered approach to threat detection. Here’s how AI enhances the other detection methods in Flowmon:
- Heuristics - AI models in Flowmon can help refine and optimize heuristic rules based on historical data and real-time threat intelligence. This reduces false positives and improves the accuracy of behavior-based heuristics.
- Pattern Matching - AI techniques used by Flowmon enhance pattern matching by learning to identify new threat patterns and variants that may evade traditional signature-based detection. ML models also learn and adapt continuously.
- Anomaly Detection - Learning algorithms in Flowmon work with statistical anomaly detection methods to identify unusual patterns or behaviors in network traffic that may indicate an attack or threat.
Conclusion
Flowmon solutions combine the power of AI and ML with other effective detection methods to create a thorough and flexible approach for identifying and mitigating cyberthreats in real time. By utilizing ML, heuristics, pattern matching and anomaly detection, they enable organizations to stay ahead of evolving threats and maintain a strong security posture.
As cyberthreats continue to grow in sophistication and frequency, adopting AI-driven cybersecurity solutions like Flowmon will help organizations stay ahead of the curve and maintain a robust security posture.
Try Flowmon for Yourself
Visit the Flowmon platform page for details of the current release and the Flowmon Security Operations page for more information on Flowmon NDR. If you’d like to speak with an expert about how Flowmon can help improve the security of your networks, don’t hesitate to contact us.
For a free trial of Flowmon to see how it can deliver actionable insights for your organization within minutes, visit our free trial page. Our support team can assist during your free trial testing. Use the contact page to start a conversation with the support team.