Artificial Intelligence (AI) has quickly established itself as the most important technology of the future in many industries. This is also the case in IT and networking, where the use of AI models has been ubiquitous for some time and is becoming increasingly important, especially in network management.
The following points are highlighted in this article:
- What are the benefits of using artificial intelligence in network management?
- Areas of application for AI in network management
- What are the challenges of implementing AI in network management?
- What are the technical requirements for integrating AI into networks?
Benefits of AI in network management
AI-based systems offer a number of benefits in the IT and network environment. IT departments benefit from automated security functions, efficient resource allocation through AI, and capacity management. This frees up IT teams to focus on more strategic tasks.
AI automation and strategic relief
Network managers and administrators can use intelligent AI analytics to optimise network performance and security. For example, AI models can help proactively detect and resolve problems or unusual activity before serious disruptions occur. The Isolation Forest method has proven to be an efficient and powerful AI model that detects anomalies in a database to increase security. The Deep Q-Learning (DQN) AI model can be used to find optimal routing or resource allocation strategies to improve network performance.
Continuous monitoring and security management with AI
By using AI models in network management, organisations can reduce operating costs and improve service quality. In practice, AI models are used for automatic load balancing, fault detection and capacity management. By automating network management and increasing efficiency, organisations can become more competitive. AI also enables continuous monitoring and analysis of network activity, helping to detect and mitigate security threats in real time. This is particularly important for compliance with privacy and security regulations. Compliance and security officers also benefit from the use of AI-based systems.
AI as a driver of innovation and development
Research and development departments can use AI to develop new technologies and solutions for network management, for example, based on up-to-date and accurate data. Providers of cloud services and infrastructure solutions can offer AI-enabled products and solutions that provide greater scalability and flexibility in managing peak loads and integrating new types of devices. In this case, AI enables network planning and provisioning based on modelling and automation. This saves time by allowing thousands of access points, switches, routers and other components of the SD-WAN network to be configured simultaneously rather than manually one at a time.
The benefits
- Automation and efficiency: AI frees up IT teams with automated security functions and efficient resource allocation, allowing them to focus on strategic tasks.
- Improved performance: AI models such as Isolation Forest and Deep Q-Learning proactively improve network performance and security.
- Reduce costs: Applying AI to network management can significantly reduce operational costs.
- Security management: AI helps identify and defend against security threats in real time, increasing network security.
- Fostering innovation: AI helps R&D departments develop new technologies and solutions for network management.
Areas of application for AI in network management
Automated security and compliance
Automated security and compliance is one area where organisations can benefit from AI in network management. In this area, it is particularly important that AI systems can continuously analyse network traffic in real time to detect anomalies and potential security threats, and initiate appropriate defensive measures to protect the network in a timely manner. Because AI can analyse bandwidth requirements and allocate resources accordingly, network resources can be managed in real time. This dynamic resource allocation can prevent bottlenecks and optimise the overall efficiency of the network.
Intelligent capacity management
Another application area is intelligent capacity management. This means that AI can help to make efficient use of the network infrastructure by predicting capacity requirements. It does this by dynamically matching capacity to actual demand, optimising network performance and availability. AI models can also analyse network patterns and make optimisations to increase data transfer rates, reduce latency and improve overall network performance. AI network optimisation also includes optimising broadband usage and minimising downtime.
Proactive Maintenance, Traffic Management and Energy Efficiency
AI can also automatically detect and often fix network faults and problems before they cause major disruption. This enables proactive maintenance, resulting in greater network stability and availability.
AI can also be applied to traffic analysis and management. Here, AI models can analyse large amounts of network data in real time to identify usage trends and optimise traffic management on the network. AI network analysis can help ensure consistent quality of service (QoS) and improve customer satisfaction.
AI can also bring energy efficiency to network management. It can be used to intelligently control network components, turning them off or putting them in low-power mode when not in use. The resulting reduction in energy consumption can reduce operating costs.
Application areas
- Automated security and compliance: AI systems continuously analyse network traffic to detect anomalies and security threats and initiate defensive measures.
- Dynamic resource allocation: AI analyses bandwidth requirements and allocates network resources in real-time to avoid bottlenecks.
- Intelligent capacity management: AI predicts capacity requirements and dynamically adjusts network infrastructure to optimise performance and availability.
- Network Optimisation: AI models analyse network patterns to increase data transfer rates, reduce latency and improve overall performance.
- Proactive maintenance: AI automatically detects and repairs network faults to ensure greater network stability and availability.
- Traffic analysis and management: AI models analyse network data in real time to identify usage trends and optimise network traffic management.
- Energy efficiency: AI enables intelligent control of network components to reduce energy consumption and operating costs.
Challenges of implementing AI in network management
While there are many benefits, there are also challenges to implementation. Due to the complexity of the technology, many organisations need to build or acquire the necessary technical expertise. In addition, building a suitable AI infrastructure often requires large initial investments in hardware, such as high-performance servers with GPUs, as well as AI platforms and frameworks.
Data management is also a major challenge. As AI systems rely heavily on the quality and availability of data, organisations need to develop efficient data infrastructures to manage large volumes of data and ensure data integrity.
Data security and privacy requirements are another challenge for organisations. As network management systems have access to sensitive corporate data, robust AI-based security measures must be implemented that comply with current data protection regulations and enhance network security. For example, data must be encrypted in transit and at rest. In addition, strict access restrictions for AI systems and robust anonymisation procedures must be implemented. Data subjects should also be informed about the use of their data, for example in the case of network services that process personal data.
Compatibility issues often need to be addressed when integrating AI solutions into existing IT landscapes and network infrastructures.
Ongoing investment in technical staff and training is also required to ensure that AI systems can be regularly reviewed and adapted. This is the only way they can operate efficiently in the long term.
The challenges
- Developing or acquiring technical expertise: Companies need to build or acquire the technical expertise required for AI.
- High initial investment: Building a suitable AI infrastructure often requires significant investment in hardware, such as high-performance servers with GPUs, as well as AI platforms and frameworks.
- Data management: Effective data infrastructures need to be developed to manage large volumes of data and ensure data integrity.
- Security and privacy: Robust AI-based security measures that comply with data protection regulations must be implemented, including encryption and access restrictions.
- Compatibility issues: Integrating AI into existing IT landscapes may create compatibility issues that need to be addressed.
- Ongoing investment in people and training: In order to use AI systems effectively in the long term, regular checks and adjustments by technically trained personnel are required.
Network Infrastructure Components
There are a number of components that have been developed to build, manage and operate networks. These components ensure communication, data security, efficiency and stability in networks.
- Network switches: Essential for efficient data transfer
Network switches are essential in organisations with multiple departments, each using their own computers, printers and other network devices. They allow the individual devices to communicate, including the transfer of data packets, and ensure an efficient network connection. - Webcam covers and fingerprint sensors: protecting privacy
To protect privacy and prevent unauthorised access to the PC’s internal camera, there are practical webcam covers from the PC Security range. Fingerprint sensors, which capture and analyse a person’s fingerprint to uniquely identify or authenticate them, provide a fast, secure and easy-to-use method of identity verification. - NFC card readers: key technology for contactless payment
Contactless payment, access control and ticketing are typical applications for NFC card readers, which enable wireless data communication over short distances (typically less than 10 cm). - Security keys: protecting digital identities
When it comes to secure authentication to digital services, applications or systems, security keys are essential, for example for USB-A, USB-C and Lightning interfaces. - Network server systems: The backbone of IT infrastructure
Network server systems are an essential part of modern IT infrastructures. They enable the centralised provision, management and security of resources and services for other devices (clients). This can significantly increase the efficiency and security of networks. Hard disk drive models are available with capacities from 300 GB to 4 TB and form factors of 2.5″ and 3.5″. - Industrial PCs: Flexible solutions for a wide range of IT applications
Industrial PCs with different processors are the prerequisite for IT applications. Models are available with and without touch display in the display formats 16:9, 16:10, 4:3 and 5:4. - WLAN access points: Extending network coverage
Wi-Fi access points are available for larger buildings where the Wi-Fi signal does not reach all rooms. Once the access points have been installed, additional end devices can be supplied with fast Wi-Fi, enabling demanding applications such as home office or streaming even at locations far away from the router. - VDSL modems and WAN routers: Bridge builders in networks
VDSL modems as master, slave or master/slave variants, which can bridge distances of up to 2.5 km depending on the model, as well as WAN routers, VPN routers and firewall routers, but also VPN security and LTE routers are also components of high-performance networks.
Industrial 5G Router – TELTONIKA RUTX50
The RUTX50 is an industrial 5G router from Lithuanian manufacturer Teltonika Networks. Designed for professional use in demanding environments, it offers a range of features for reliable and secure data transmission.
- 5G connectivity: supports the latest 5G wireless standards for high speeds and low latency, backward compatible with 4G LTE and 3G networks
- Dual SIM: enables the use of two SIM cards for resilience and load balancing
- Gigabit Ethernet: multiple Gigabit Ethernet ports for fast wired connections
- WLAN: offers dual-band WLAN for wireless connectivity
- RutOS: runs RutOS, an OpenWRT-based operating system with advanced networking and security features
- RMS support: compatible with Teltonika’s Remote Management System (RMS) for remote management and monitoring
- GPS/GNSS: Provides GPS/GNSS functionality for navigation and positioning
The RUTX50 is suitable for a wide range of applications:
- Industrial automation
- Transportation
- Safety and security
- retail
and many more
In summary, the Teltonika RUTX50 is a powerful and versatile 5G router designed for use in demanding industrial and commercial environments.
The main components
- Network switches: Essential for networking and communication between different devices within an organisation.
- Security measures: Webcam covers and fingerprint sensors ensure privacy and secure user authentication.
- Authentication technologies: NFC card readers and security keys enable contactless transactions and secure access to digital services.
- Network server systems: Centralised servers provide storage, management and security for network resources and services.
- Enhanced connectivity: WLAN access points and a range of routers improve network coverage and performance.
- VPN Gateway and Gateways: Powerful platforms deliver high performance and advanced encryption for complex networks.
- Flexible network infrastructure: Integrated Services Gateways with multiple port options support an adaptable and extensible network architecture.
Conclusion
Integrating AI into networks can increase their performance and security, as well as improve efficiency and user experience. In addition, AI models can be used to reduce operational costs and prevent network outages. The integration of AI is technically feasible today and is facilitated by the availability of powerful hardware and software. Integrating AI into networks is an important step towards meeting the increasing demands placed on modern infrastructures. In addition, the use of AI in networks can significantly increase the competitiveness of companies.
Images: Adobe Stock
