Machine Learning in Computer Networks: Techniques, Methods, and Performance Metrics

Machine learning is revolutionizing computer networks by enabling them to learn from experience and improve their performance over time. This is achieved through various techniques and methods that can be applied to different aspects of network management.

One key technique is anomaly detection, which involves identifying unusual patterns in network traffic that may indicate a security threat. This is particularly useful in detecting malware and other types of cyber attacks.

Network traffic classification is another important application of machine learning in computer networks. It involves categorizing network traffic into different types, such as HTTP, FTP, and VoIP, to optimize network performance and resource allocation.

The performance of machine learning algorithms in computer networks can be measured using various metrics, including accuracy, precision, and recall. These metrics help network administrators evaluate the effectiveness of different machine learning techniques and methods.

For another approach, see: Is Transfer Learning Different than Deep Learning

Machine learning (ML) is revolutionizing the way we manage and secure computer networks. ML tools can help with moment-by-moment traffic management, as well as longer-range capacity planning and management.

With ML tools, network professionals can evaluate situations where it could be beneficial to use a ML tool to determine traffic flows, such as when traffic is shifting from rack-to-rack to server-to-server within a rack, or when traffic is shifting from large numbers of small-packet flows to smaller numbers of large-packet flows.

ML has enormous value in network security. ML tools can analyze behavior in any kind of network entity and reduce the occurrence of false positive reports. This makes it easier to spot attacks that span several levels of enterprise activity, such as email phishing and Layer 7 attacks on web applications.

ML-powered tools are also useful in detection and remediation of DDoS attacks. By spotting anomalies in network behavior, network security teams can find everything from a compromised hardware node to a rogue employee on the company network.

Here are some key areas where ML is being applied in networking:

These areas are crucial for autonomic network operation and management. By applying ML techniques, network professionals can make data-driven decisions and improve network performance.

Take a look at this: Transfer Learning Enables Predictions in Network Biology

Machine learning (ML) tools are revolutionizing network management by enabling moment-by-moment traffic management and longer-range capacity planning. They can identify traffic spikes and failures, sending automated or manual responses to correct errors.

ML tools can predict traffic trends, helping guide future decisions. For instance, they can determine if traffic in the data center is shifting from rack-to-rack to server-to-server within a rack, or if it's shifting from small-packet flows to large-packet flows.

Network professionals can use ML tools to evaluate situations where it could be beneficial to use them to determine traffic flows. This can help IT determine what kinds of networks to design, such as leaf-spine, switch-based mesh or host-based mesh.

ML tools are especially helpful with root cause analysis, making it easier to diagnose and fix network issues. By combining ML-driven analytics with other AI tools, like natural language processing, network engineers can create virtual assistants to help network administrators.

Here are some examples of how ML-driven route analytics can be used:

These are just a few examples of how ML tools can be used in network management. By leveraging these tools, network professionals can create more efficient and effective networks that meet the needs of their users.

Network analytics can predict traffic trends, helping guide future decisions, but to do so, it needs a lot of data. This data can come from various segments of a network, and the more of it ML tools have access to, the more detailed their analysis and recommendations can be.

To establish the ground truth, network professionals need to give a formal description to the classes of interest, which requires hand-labeling by domain experts or aid from deep packet inspection (DPI) or unsupervised ML techniques.

Application signatures, built using features like average packet size, flow duration, and IP traffic packet payload, can be used to establish ground truth for application classes in the training dataset. Average packet size and flow duration have been shown to be good discriminators.

If this caught your attention, see: Labeling Data for Machine Learning

Network engineers can use ML-driven analytics to spot trends in traffic flows, such as shifting from rack-to-rack to server-to-server within a rack or from large numbers of small-packet flows to smaller numbers of large-packet flows.

To combat class imbalance in the training data, techniques like under-, over-, joint-, or ensemble-sampling can be applied. For example, uniform weighted threshold under-sampling creates smaller balanced training sets.

Here are some common methods for labeling datasets using the features of a class:

Establishing the ground truth drives the accuracy of ML models, and there's an inherent mutual dependency on the size of the training data of one class of interest on another, impacting model performance.

Traffic prediction plays a key role in network operations and management for today's complex and diverse networks.

The objective of traffic prediction is to forecast future traffic and traditionally has been addressed via time series forecasting (TSF).

Network traffic prediction has been traditionally addressed via time series forecasting (TSF), which involves constructing a regression model capable of drawing accurate correlation between future traffic volume and previously observed traffic volumes.

Statistical analysis models are typically built upon the generalized autoregressive integrated moving average (ARIMA) model, while majority of learning for traffic prediction is achieved via supervised NNs.

Traditional TSF models, such as the ARIMA model, are a popular approach for traffic prediction, where autoregressive (AR) and moving average (MA) models are applied in tandem to perform auto-regression on the differenced and “stationarized” data.

However, traditional TSF models are seemingly compromised with the rapid growth of networks and increasing complexity of network traffic, giving rise to more advanced ML models.

Efforts have been made to reduce overhead and, or improve accuracy in traffic prediction by employing features from flows, other than traffic volume.