A believe predicted by telecom leaders is that machine learning (ML) will be the key to derive and imply user behaviour and expectations – ML aims to make intelligent decisions based on knowledge extracted from data, increase the user’s quality of experience (QoE) and reduce customer churn. Under the AI umbrella (refers to any attempt of mimicking human behavior with computers), machine learning (ML) is used for network performance management, security and health management tools all use ML to power better analytics. ML-based tools are excellent in network testing by learning normal network behavior and highlighting relatively abnormal actions. So, now let us see The Role of Machine Learning in Network Testing along with Accurate Mobile Network Monitoring Tools, Mobile Network Drive Test Tools, Mobile Network Testing Tools and Accurate LTE RF drive test tools in telecom & Cellular RF drive test equipment in detail.
Machine learning is a subset of artificial intelligence (AI) used to building systems that can identify patterns and make logical decisions with little to no human intervention. ML is a data analysis method that automates to developing of analytical models through using data related to digital information (numbers, words, clicks and images). Machine Learning (ML) takes an unprecedented surge in applications that not only solve problems and enable automation in diverse domains but also enables a system to scrutinize data and deduce knowledge. ML applications acquire from the input data by using automated optimization methods to continuously improve the accuracy of outputs. ML technique is applied to fundamental problems in networking due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities – some of the network problems including traffic prediction, routing and classification, congestion control, resource, and fault management, QoS and QoE management, and network security. Machine learning (ML) even goes beyond simply extracting knowledge, to utilizing and improving knowledge over time and with experience.
Machine learning is beneficial in terms of:
- Making better decisions than humans and without getting tired like human operators.
- Not requiring any manual intervention.
- Providing instant alerts when something happens.
- Helping reduce costs by automating repetitive tasks.
- Improving efficiency and increasing productivity.
The various application of Machine Learning (ML) in computer networks and communication are as follows:
- Machine learning for multimedia networking
- Management of data centres and cloud infrastructures autonomously
- Modern applications of ML in the management of networks
- Cyber security such as anomaly detection, malware detection, etc.
- Autonomous sensors networks and self-organising systems
- Big Data analytics frameworks for networking data
- Resource allocation in networks using ML
- Deep learning in network control and management
- Applications of game theory in computer networks, evolutionary computing in network optimisation and applications of AI in network configuration tuning
- Modern approaches in cognitive computing, network monitoring and performance anomaly detection
When it comes to the role of ML in network testing, Machine Learning tools are playing a vital role in helping with moment-by-moment traffic management, longer-range capacity planning and management to send automated or manual direct management responses to correct the error if occurs any. Here are some steps ML follows while network testing.
Network analytics: Beyond management in the moment, ML tools can also predict traffic trends that help guide future decisions. Using of ML tool is beneficial to determine traffic flows, such as the data center shift from rack-to-rack to server-to-server within a rack.
ML-driven analytics can detect issues when a network component is in the initial stages of failure. Network equipment vendors are increasingly weaving analytics especially tools built around SaaS offerings.
Network security: ML has enormous value in network security; hence machine learning tools can analyse behaviour in any kind of network entity to improve behavioural threat analytics (such as email phishing, account compromise, Layer 7 attacks on web applications and OS-level network compromise) by reducing the occurrence of false positive reports.
As data volumes grow, computing power increases, and data scientists enhance their expertise, machine learning (ML) will only continue to drive deeper efficiency not only at work but also at home. Since the ever-increasing cyber threats that businesses are facing nowadays, ML is needed to secure valuable data and keep hackers out of internal networks. But network testing should be conducted continuously just to avoid such threats by checking network stability – RantCell is the appropriate tool to look after.