As information technology occupies an increasing place in everyday life, cybersecurity threats are also becoming more complex. Traditional security solutions – namely antiviruses, firewalls, and manual monitoring systems – are becoming vulnerable to the complexities of modern threats. Therefore, technologies based on artificial intelligence (AI) and, in particular. Based on neural networks, they are becoming an important tool in solving cybersecurity problems.
What is artificial intelligence and neural networks?
AI is the ability of computer systems to perform functions inherent in the human mind (for example, to analyze, make decisions, and learn).
Neural networks belong to the field of deep learning (deep learning) of AI. They analyze information in layers based on the principle of how biological neurons work. In particular, convolutional (CNN), recurrent (RNN) neural networks and transformers are widely used in cybersecurity.
How are AI and neural networks used in cybersecurity?
In automatic threat detection (DDoS attack), neural networks analyze network traffic or system activity to distinguish between normal and abnormal behavior. This gives a new level to the IDS (Intrusion Detection System) and IPS (Intrusion Prevention System) systems.
RNN models and transformers (for example, BERT) achieve high accuracy in analyzing e-mail and text messages and detecting fake (phishing) emails.
Using user behavior modeling (UEBA – User and entity behavior analytics), neural networks study user habits and identify cases of deviation from their usual behavior. This is especially useful when identifying "insider threats" (internal danger).
Botnet networks usually work according to certain algorithms. A botnet is a set of Internet–connected devices, each of which is controlled by two or more bots. AI algorithms, especially LSTM (Long short-term memory) models, analyze these patterns in network traffic to identify botnet activity.
Types of artificial intelligence models and their advantages in cybersecurity.
|
Model Type |
Advantage |
Application areas |
|
CNN (Convolutional Neural Network) |
Visual and structural analysis |
Malware identification. file structure analysis |
|
RNN / LSTM |
Efficiency in sequence analysis |
Log files, network traffic |
|
Autoencoder |
Anomaly detection |
Monitoring of abnormal activity |
|
GAN (Generative Adversarial Network) |
Creating artificial data |
Testing, model training |
|
Transformer (BERT, GPT) |
Advanced text analysis |
Detection of phishing emails and fake content |
For example, Google Chronicle and Microsoft Defender use AI-based real-time risk monitoring systems.
IBM Watson for Cybersecurity is a deep learning neural network platform that automatically analyzes thousands of threats.
The DARPA Active Cyber Defense project develops algorithms that "respond" to cyber attacks using generative models.
However, there are certain risks that you should pay attention to when using AI and neural networks.
The model is trained only based on the data provided to it. If the data provided does not match trends, is misleading, or is "malicious," the model will make the wrong decision.
Deceptive attacks can be carried out using specially created "secure" inputs to neural networks.
High computing power (GPU, TPU) is required to train neural networks and use them in real time.
The problem with Explainability is that in many cases the neural network cannot explain "why" such a decision was made. This reduces trust in security.
Strategies for the effective use of AI and neural networks.
1. Creation of a secure and high-quality data collection system.
2. A reasonable approach to choosing a model (CNN for malware, RNN for Log files).
3. Ensuring cooperation between cybersecurity specialists and AI specialists.
4. Using approaches that enhance the explanatory ability of the model (LIME, SHOP).
5. Constant updating of the trained models.
6. Integration of AI systems with traditional cybersecurity tools.
Artificial intelligence, especially neural networks based on deep learning, play an important role in detecting complex and volatile cybersecurity threats. They not only detect dangerous activities, but also predict, understand user behavior, and help strengthen systems. However, for the proper use of these technologies, factors such as strategic planning, ethics, interpretation, and security must be considered.
