In the past few years, internet dangers have become more composite, faster. Phishing, malicious software, DDoS, and data leaks result in serious consequences for companies or individuals. Traditional methods of protection do not always counter new dangers, as they often do not have time to respond to rapid changes in the behavior of burglars.
Neural networks counter these dangers. Their opportunity to find out from great quantities of data increases the precision of danger identification, abnormality identification, and protection against new types of attacks. In this article, we will consider what neural networks are, how they are used in cybersecurity, and what advantages they provide. We will also discuss the technologies and algorithms at the core of such systems, and share examples of real-life applications of neural networks in the field of security.
The purpose of this article is to introduce you to how neural networks solve cybersecurity problems and provide useful recommendations for those who want to master these technologies.
What is it
Neural networks are synthetic technique created by analogy with the person central nervous system. They comprise of interrelated elements – synthetic neurons that handle information. Each neuron executes a problem, transferring a signal further along the network, handling incoming data, training based on the outcomes. Encouraged by natural neural networks, such as the person’s brain, neural networks reveal samples and make judgments.
The discrepancy between neural networks and classic machine training mechanisms is that they self-train, accommodate, and operate with unstructured data. When training, the network learns the dependencies between input or output data, increasing its opportunity to forecast. The more data enters the framework, the more precise the neural network becomes.
In cybersecurity, neural networks provide enormous opportunities. Classic security activities rely on pre-defined rules, threat signatures. But given that cyber dangers are becoming fuller and more changeable, this attitude is limited. Neural networks, having the opportunity to self-train, determine newly, unknown dangers, accommodating to changing conditions.
Neural network education is divided into several stages. At the first stage, the system receives data (for example, network traffic or information about previous attacks). Next, the network learns from this data, fixing its domestic joining, which lets it to «figure out» them and produce valuable information from them. After training, the neural network disassembles new data, making predictions about whether a particular procedure is normal or potentially dangerous.
The principle of «learning from mistakes» is needed here. When a neural network makes a mistake, it corrects its connections, improving its work based on negative examples. As a result, the neural network not only classifies threats, but adapts, allowing the security system to be one step ahead of the attackers.
The peculiarity of neural networks is their opportunity to work with huge volumes of data. Unlike traditional methods that work only with a limited number of features (for example, lists of known malwares), neural networks disassemble huge volumes of information, revealing hidden samples. This protects both small and large mechanisms from threats.
Neural networks in cybersecurity use different models and architectures. One of the popular ones is deep neural networks, which use several layers of neurons for complex data processing. These mechanisms work with data in different formats, including text, images, sound, video.
Some types of neural networks used in cybersecurity:
- Convolutional neural networks (CNN) - are usually applied for image refining, but are also applied to disassemble data on network assaults, identify logs, and other information content.
- Recurrent neural networks (RNN) - are effective for disassembling sequential data, such as network traffic, event logs. They take into account time dependencies, forecast dangers that develop over time.
- Generative models –involve GAN (generative adversarial networks), which create new data, for example, to simulate attacks, check for vulnerabilities in security mechanisms.
Together, these mechanisms make a procuring of high precision in identifying dangers and adapting to shifting conditions. In cybersecurity, neural networks reveal known dangers and reveal new ones that have not yet been revealed by classic mechanisms. Neural networks in cybersecurity instantly react to alters in the network, reveal abnormalities, and prevent dangers before they cause damage. Neural networks are a top tool for protecting information in the face of continuously developing dangers.
Application of neural networks
Neural networks play a major role in cybersecurity, providing powerful tools for data analysis, threat detection, and protection against attacks. Their opportunity to self-learn and adapt to new conditions makes neural networks indispensable in the fight against rapidly evolving cyber dangers. Below we will consider the areas of application.
- Intrusion detection (IDS/IPS). One of the applications of neural networks is the emergence of intrusion detection systems (IDS), intrusion prevention systems (IPS). They analyze network traffic, identify anomalies indicating hacking attempts. Unlike traditional methods based on signatures of known attacks, neural networks work with unknown threats, analyzing unusual behavior on the network. For example, if an ordinary user starts performing unusual actions - too many requests, attempts to access closed resources, or the appearance of abnormal connections, the neural network will notice this and report a possible intrusion. Neurons increase detection accuracy by reducing the number of false positives. This is necessary when every second counts.
- Prevention of phishing. Phishing is one of the most popular social engineering techniques, when attackers obtain confidential information by forging emails, web pages. Neural networks are used to combat this type of attack. They analyze incoming emails, websites and identify signs of phishing. For example, the network identifies discrepancies in sender addresses, suspicious links, incorrect writing style or other factors typical of phishing messages. Neural networks-based systems are trained on data about known attacks, adapt to new fraudsters’ methods, increasing the accuracy of filtering phishing emails. In addition, neural networks are used to analyze web resource access logs to identify attempts to visit fake pages hiding under the addresses of well-known organizations. This prevents data leakage at the stage of a user’s attempt to go to a dangerous site.
- Detection, analysis of malware (Malware Detection). Recognizing malware such as viruses, trojans, spyware is a necessary task in the field of cybersecurity. Traditional security systems use a database of known threats, if a program or file matches one of these signatures, they are considered malicious. But, with the development of technology, attackers began to use methods to bypass them. Neural networks analyze not only program signatures, but also their behavior. For example, even if a program hides its true nature, has no obvious signs of a virus, the neural network will detect suspicious behavior, such as attempts to access confidential data or unauthorized operations with files.
- User and Entity Behavior Analytics (UEBA). It monitors the behavior of employees and other users on the network, identifying anomalies that indicate possible internal threats or data leaks. The use of neural networks in UEBA creates accurate user behavior profiles, based on which the system will detect deviations from the norm. For example, if an employee starts making unusual requests, downloading large amounts of data or performing actions outside of working hours, a neural network-based system signals a possible threat. What is needed here is the opportunity of the system to identify anomalies associated with compromised credentials or attempts to abuse confidential information. This allows organizations to protect their data and prevent threats emanating from their own employees.
- Protection from DDoS attacks. DDoS attacks aimed at denial of service are attempts to overload servers and network resources in order to disable them. In the context of such attacks, it is necessary not only to quickly identify the problem, but also to predict and prevent the attack at the preparation stage. Neural networks analyze incoming network traffic, identifying anomalies that indicate the launch of a DDoS attack. For example, if the system detects a sharp increase in the number of requests from one or more sources, the neural network will report a possible attack and take measures to block suspicious traffic.
- Vulner Opportunity prediction. The use of neural networks in vulnerability prediction is one of the promising areas in the field of cybersecurity. Neural networks analyze large amounts of data on network and software systems, identifying potential weaknesses that can be used by intruders. This allows developers and security specialists to take measures to protect themselves in advance, eliminating vulnerabilities before they are exploited. Neural networks are also used for testing and auditing systems for vulnerabilities, which will increase the level of protection at the development stage.
Technologies and algorithms in cybersecurity
Different technologies or algorithms have been developed for neural networks in cybersecurity. Let’s consider them:
- Deep learning: This is one of the most popular methods in machine learning, allowing neural networks to identify complex dependencies and patterns in data. They work with large amounts of data and learn from a variety of examples, which makes them indispensable for cybersecurity.
- Supervised learning: This is a learning method when a neural network is trained on pre-labeled data (examples of attacks or normal actions).
- Unsupervised learning: In this case, the neural network does not receive pre-labeled data and must independently identify patterns in the information flow. This is useful for detecting unknown threats that are not pre-encoded in databases.
- Recurrent neural networks (RNN): These networks analyze sequential data and are well suited for working with time series such as network traffic or event logs. This makes RNNs suitable for detecting new types of attacks that occur gradually over time.
- Convolutional Neural Networks (CNN): Typically used for image processing, but are applicable to analyzing data related to the visual aspects of cyber dangers, such as analyzing videos or images to detect hidden threats.
Each of these methods and technologies is applied to specific security-related problems and can be used in different combinations to create powerful, robust defenses.
Examples
In practice, neural networks have proven their effectiveness in protecting against cyber dangers. Some examples of how they are used:
- Phishing detection: PayPal uses neural networks to combat phishing. The system analyzes incoming emails, web pages, identifying signs of fake sites. Thus, many attempts to steal credentials have been prevented.
- Malware recognition, analysis: Crowdstrike uses neural networks to analyze program behavior. This system identifies malicious actions at an early stage, preventing infection, even if the virus has not yet been recorded in the databases.
- Prediction of attacks, vulnerabilities: Organizations use neural networks to predict possible threats. For example, the Cisco security system analyzes network behavior, based on this and predicts where or when an attack may occur.
These examples show how neural networks are used to improve security in various areas, from finance to large corporate systems.
Recommendations
If you need to implement neural networks to improve cybersecurity in your organization, here are some recommendations:
- Update and train neural networks regularly. Cyber threats are constantly changing, and your models must be relevant. Regularly updating data, learning on new examples will help adapt to new challenges.
- Use hybrid approaches. Combining neural networks with traditional methods such as antivirus software, intrusion prevention systems, improves security.
- Train employees. Employees working with neural networks in the field of security must be qualified. Courses, training programs on machine learning and cybersecurity will help your team become an expert in this field.
For those who want to study neural networks deeper, the chataibot.pro website offers access to powerful tools such as ChatGPT, useful for testing, developing machine learning algorithms. You can also find AI intelligence, cybersecurity on the website.
Results
Neural networks are a powerful tool for protecting against cyber dangers. They detect anomalies, threats with high accuracy, prevent attacks at early stages and adapt to changing conditions. Systems using neural networks significantly enhance traditional security methods, creating secure networks.
The introduction of neural networks in cybersecurity is a necessary step for companies and individuals. To start implementing these technologies, online courses, training programs of different academies available on chataibot.pro will help you. Visit chataibot.pro and find out more!
