Network Management, Network Security
Article | July 17, 2023
There was a time when network security meant having servers on-site. A firewall would protect company data whenever internet traffic entered and exited the network. But, what about today? Modern businesses do not strictly function on-premise.
With the COVID-19 pandemic, the number of people working off-site part-time or full-time increased enormously – and suddenly. This change compelled cybersecurity professionals to reconsider their security measures. Their online privacy solutions had to ensure that their most precious asset — their data — was secure regardless of where workers accessed it.
Even when restrictions are lifted, businesses continue to use remote teams. As a result, more and more of a company's critical data and services are being housed in the cloud. These two criteria indicate that the need to examine network security on a regular basis is here to stay.
The good news is that a VPN, or virtual private network, is one of the most simple and widely accessible network security solutions for remote worker internet access.
Do VPNs Provide Reliable Business Security?
A virtual private network is a kind of Internet security service that enables users to connect to the internet as if they were on a private network. VPNs utilize encryption to provide a secure connection across vulnerable Internet infrastructure.
VPNs are one method for protecting business data and controlling user access to that data. The VPNs safeguard data as users interact with applications and websites through the Internet, and they can conceal specific resources. They are typically used for access control, although alternative identity and access management (IAM) systems can also assist with user access management.
VPN Encryption Enhances Network Security
Data is encrypted so that only authorized parties can view it. Anyone who manages to intercept it, whether a hacker, a fraudster, or another bad actor, is out of luck.
Imagine an employee is working from a coffeehouse, shared workspace, hotel, or airport and has access to your company's business-grade VPN. (Please keep in mind that business-or enterprise-grade VPNs are not the same as free VPN services.) The employee can create an encrypted connection between both the user's device and your VPN by using a VPN client installed on their preferred device and a public Wi-Fi network. This device, as well as any others that connect to your VPN, will establish encryption keys on both sides of the network connection. These keys will then encrypt and decrypt the information being exchanged.
The data of the person working at the coffeehouse is secured by the VPN after they create an encrypted VPN connection by utilizing the coffeehouse's Wi-Fi as a hotspot with a VPN client. Even if cybercriminals get access to the network of that coffeehouse, your employees and their data are secure within the VPN tunnel.
Closing Lines
Network security requires a VPN service from a trustworthy VPN provider. Our next-generation VPN enables enterprises to fully protect their assets in a dynamic, cost-effective, and scalable manner. A VPN solution enables you to connect private networks, devices, and servers quickly and simply to create a secure, virtualized, modern internet.
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Data Center Networking
Article | July 5, 2023
The much-anticipated 5G Standalone has arrived. T-Mobile is the first to launch it in the USA, covering 250 million people across 7,500 cities and towns, including rural areas. China Mobile is the only other service provider to launch it in Hong Kong. Overall, 58 operators are currently investing (November 2020) in 5G SA, including those who have launched.
5G SA makes a break from 5G non-standalone by integrating the evolved packet core or the signaling brain of the 5G network, which controls the network's devices. It prepares the groundwork for new services unique to this generation of networks, such as network slicing to customize enterprise services across multiple networks.
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Enterprise Mobility, Mobile Infrastructure
Article | June 16, 2023
Applications of AI/ML
Modern businesses are adopting Artificial Intelligence (AI) that encompasses disciplines like machine learning (ML), natural language processing (NLP), evolutionary computation, etc., to increase their productivity and management capabilities.
Companies like Qualcomm are using AI and machine learning to improve their customer experience.
“Across many industries, we are currently experiencing the creation of intelligent machines that is using AI to simulate smart behavior.”
-Dr. Vinesh Sukumar, Senior Director- Head of AI/ML Product Management at Qualcomm, ( in an interview with Media7)
The application of machine learning in networking is swiftly taking shape. However, as the problems in modern computer networks are getting tedious to handle, AI tools are being introduced to hard-carry their smooth functioning.
Let’s take a look at how network complexity impacts businesses:
Difference in Network Parameters
Different client devices like laptops, smartphones, CCTV cameras, etc., are connected to a single network. However, their requirements and parameters are different. Therefore, the IT team of the business needs to meet them without compromising the functionality and security of the network.
Users Prefer Wireless Networks
Wireless networks are more complex than wired ones. They perform dynamically depending on the number of users, applications, and other variables.
Impact of Cloud Computing
Most applications are now cloud-based, and such a network has multiple data entry points and requires more support.
User Experience
Deciphering root cause analysis, finding correlation and solutions becomes tedious without an AI/ML model. Complex patterns remain unanalyzed, and this creates a vacuum between the customer and the business.
What Does ML Bring to the Table?
Machine learning applications in networking correlate to solving four types of network problems: clustering, extraction, regression, and classification.
For classification and regression, ML clusters similar data and creates a gap between data groups. It then successfully maps a new set of data to a pre-set continuously valued output. As for extraction, it easily establishes a statistical relationship between the data it analyzes.
Machine learning applications in networking encompass the following:
Automation and Cognitive Computing
ML enables automation in data processing by eliminating the human error factor and constantly improving with time. It analyzes data, improves the productivity, security, and health of the network. Cognitive computing allows processing diverse data sets, detecting and finding root causes and common traits within the system.
Network Monitoring & Security
Network monitoring is used to solve problems in a large dataset by deciphering the hidden pattern in the data. It then predicts the outcome for clustered data, malware attacks, or impending network failure. It recognizes impending threats in time and sends out warnings. ML uses anomaly-based intrusion, misuse-based intrusion, or hybrid intrusion to prevent misuse, modification, unauthorized access, or malfunction.
Traffic Prediction, Classification, and Routing
Network traffic prediction is important to handle any mishaps proactively. Network analysis in machine learning is done by using Time Series Forecasting (TSF). By using a regression model solution, TSF finds a correlation between the traffic volume in the future and the traffic previously observed.
Traffic classification ensures Quality of Service (QoS), planning ahead for capacity, security, performance analysis, etc. It helps with proper resource utilization by pinpointing unnecessary traffic in a critical application.
Factors like cost-effectiveness, link utilization, operational capabilities, and policies are also considered by the ML model.
Congestion Control
ML models control the number of packets that enter a network to ensure that the network is stable, fairly utilize resources, and follow queue management employed for congestion control.
Efficiently Managing Resources
ML efficiently manages network resources like the CPU, frequency, switches, memory, routers, etc., by using analytical decision-making.
ML Learning Curve
ML models learn in the following ways:
Pitfalls
Like any other technology, machine learning application in networking comes with pitfalls and limitations. Here are a few:
Data Quality
The efficiency of an ML model is based on the quality, quantity, and diversity of data it processes so it can deduce patterns or identify root causes. Most ML models use simplistic synthetic data for training, validation, and performance. The same cannot be said about practical settings because the data comes from different applications and services and is more complex.
Feasibility
There are scalability and feasibility issues because each network and application is different. Moreover, there are no set standards for uniformity for implementation which makes it hard to set benchmarks or best practices. Control over autonomic networks is distributed and remains limited based on the vendor’s specific devices.
Predictive Analysis and Its Cost
Network analysis and machine learning prediction require additional accurate and effective monitoring investments. Moreover, fault management may have some potholes as there may be a scarcity of normal fault data.
High FPR (False Positive Rates)
Anomaly detection by ML in networking has not created enough buzz in the industry because it generates high FPRs during operations. Also, no detailed anomaly report is generated, so no anomaly history log can be maintained.
Striking a Balance
ML requires time to learn and mitigate issues. It is difficult to identify, in advance, how complex the ML’s approach will be. Striking a balance between the performance and computational cost is difficult. Deciphering comprehensive evaluation metrics is also a tedious task.
No Theoretical Model
There is no theoretical model, in turn, a unified theory, for ML in networking, so each network may have to be learned separately. The current machine learning applications in networking are made keeping in mind certain applications. Over time, more research to tailor ML for certain networks needs to be done. Cross-domain experts who understand both ML and networking are also rare.
Solutions
Software Defined Networking (SDN)
CISCO helped PwC Italy set up a secure network at their new twenty-eight-floor tower with the help of their SD-Access product. PwC wanted a secure, robust network with increased Wi-Fi and wired connectivity for their 3000 employees by streamlining network operations.
“We needed a robust and highly reliable wireless network infrastructure that’s as advanced as the tower itself.”
-Simone Demaria,Network Architect and Infrastructure Manager at PwC Italy
By applying Software Defined Network (SDN), IT personnel can remotely govern network policies in real-time through open interfaces, so traffic engineering is easily possible. SDN also contributes to network virtualization.
SDN supports the upcoming 5G ecosystem. When combined with NFV and VNF, SDN can revolutionize networking.
Going Beyond Traffic Volume & Prediction
To tackle the limitations that TSF-based traffic prediction models have, leveraging features beyond traffic prediction and concentrating on traffic interpolation and sampling could be viable. Research is ongoing on this possibility.
Summing It Up
As the influx of data keeps on increasing, the complexity of networks will increase in tandem. For successfully implementing ML for streamlining networking, the ML approaches we are aware of today need to be upgraded to accommodate multi-layer networks and multi-tenancy so autonomic networking can be a reality.
FAQs
How Can ML Help in Making Networking Smarter?
ML can streamline the network by automation, threat detection, and improving its performance.
How Complex Is Integrating ML into Networking?
The complexity depends on the type of network you are integrating it into.
What to Keep in Mind Before Using Ml in Networking?
Consider investment costs, data availability, feasibility, and scalability.
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Article | August 17, 2021
Despite a global pandemic and the associated economic challenges, the world has moved to 5G four times faster than it did with 4G LTE, according to 5G Americas.
In the midst of this rollout, how can industries ensure that the benefits of 5G investment warrant the long-term investment costs? Or to put it another way, how can industries leverage 5G technology to build cost-efficient connected industry on a global scale?
The answer is the intelligent edge. The intelligent edge is the analysis of data and development of solutions at the site where the data is generated. By doing this, the intelligent edge reduces latency, costs and security risks, thus making the associated business more efficient. As 5G puts compute closer to the user (whether that be a human or a device) it enables a new paradigm of capabilities with AI, machine learning and a host of related use cases.
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