How Can Machine Learning Streamline Networking?

How Can Machine Learning Streamline Networking

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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The delivery of a quantum-secure Private 5G testbed, achieved where Athonet have combined the Athonet core with CableFree radios, underscores the commitment to innovation and reinforces the confidence enterprises can have in deploying networks that are both cutting-edge and secure from both present and future threats.” About Arqit Arqit Quantum Inc. (Nasdaq: ARQQ, ARQQW) (Arqit) supplies a unique encryption Platform as a Service which makes the communications links of any networked device, cloud machine or data at rest secure against both current and future forms of attack on encryption – even from a quantum computer. Compliant with NSA standards, Arqit’s Symmetric Key Agreement Platform delivers a lightweight software agent that allows devices to create encryption keys locally in partnership with any number of other devices. The keys are computationally secure and operate over zero trust networks. 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With today's announcement, Cato is expanding SASE into threat detection, incident response, and endpoint protection without compromising on the architectural elegance captured by the original SASE definition. "Cato SASE continues to be the antidote to security complexity," says Shlomo Kramer, CEO and co-founder of Cato Networks. "Today, we extend our one-of-a-kind SASE platform beyond threat prevention and into threat detection and response. Only Cato and our simple, automated, and elegant platform can streamline security this way." An early adopter of Cato XDR is Redner's Markets, an employee-owned supermarket chain headquartered in Reading, Pennsylvania, with 75 locations. Redner's Markets' vice president of IT and Infrastructure, Nick Hidalgo, said, "The Cato platform gave us better visibility, saved time on incident response, resolved application issues, and improved network performance ten-fold." (Read more about Redner's Markets and Cato in this blog. "The convergence of XDR and EPP into SASE is not just another product; it's a game-changer for the industry," said Art Nichols, CTO of Windstream Enterprise, a Cato partner. "The innovative integration of these capabilities brings together advanced threat detection, response capabilities, and endpoint security within a unified, cloud-native architecture—revolutionizing the way enterprises protect their networks and data against increasingly sophisticated cyber threats." (Read more about what Cato partners are saying about today's news in this blog.) Platform vs. Product: The Difference Matters Cato XDR takes full advantage of the enormous benefits of the Cato SASE Cloud platform, the first platform built from the ground up to enable enterprises to connect, secure, and manage sites, users, and cloud resources anywhere in the world. Unlike disjointed point solutions and security appliances, Cato capabilities are instantly on, always available at scale, and fully converged, giving IT teams a single, shared context worldwide to understand their networks, prevent threats, and resolve problems. As an autonomous platform, Cato SASE Cloud sustains its evolution, resiliency, optimal performance, and security posture, saving enterprises the operational overhead of maintaining enterprise infrastructure. Enterprises simply subscribe to Cato to meet their business needs. Cato's cloud-native model revolutionized security and networking operations when it was introduced in 2016, a fact validated three years later in 2019 when the Cato approach was formally recognized by the industry as SASE. Breach Times Still Too Long; Limitations of Legacy XDR Cato is again revolutionizing cybersecurity with the first SASE platform to expand into threat detection, empowering security teams to become smarter and remediate incidents faster. The flood of security alerts triggered by network sensors, such as firewalls and IPS, complicates threat identification. In 2023, enterprises required 204 days on average to identify breaches.1 XDR tools help security analysts close this gap by ingesting, correlating, and contextualizing threat intelligence information with the data from native and third-party sensors. However, legacy XDR tools suffer from numerous problems relating to data quality. Sensor deployment extends the time-to-value as IT must not only install the sensors but also develop a baseline of specific organizational activity for accurate assessments. Data quality is also compromised when importing and normalizing third-party sensor data, complicating threat identification and incident response. Security analysts waste time sorting through incident stories to identify the ones most critical for immediate remediation. Once determined, incident remediation is often hampered by missing information, requiring analysts to master and switch between disparate tools. No wonder in 2023, average breach containment required more than two months.1 Cato XDR and Cato EPP Expands the Meaning of SASE Cato XDR addresses legacy XDR's limitations. Instantly activated globally, Cato XDR provides enterprises with immediate insights into threats on their networks. Incident detection is accurate due to Cato's many native sensors – NGFW, advanced threat prevention (IPS, NGAM, and DNS Security), SWG, CASB, DLP, ZTNA, RBI, and now EPP/EDR. Powered by Bitdefender's world-leading malware prevention technology, Cato EPP protects endpoints from attack – in the Cato way. Endpoint threat and user data are stored in the same converged Cato data lake as the rest of the customer's network data, simplifying cross-domain event correlation. The result is incredibly high-quality data that improves the incident identification and remediation process. Cato AI uses the data to accurately identify and rank incidents, empowering analysts to focus critical resources on an organization's most important remediation cases. Cato AI is battle-tested and proven across years of threat hunting and remediation handling by Cato MDR service agents. Remediation times reduce as detected incident stories contain the relevant information for in-depth investigation. Cato's tools sit in the same console as the native engines, enabling security analysts to view everything in one place -- the current security policy and the reviewed story. Finally, incident reporting is simplified with generative AI. Purpose-built for investigations, this natural language engine provides human-readable explanations of incident stories. Analysts save time sharing incident information with other teams and reporting to their managers.

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Network Infrastructure

DISH Wireless Awarded $50 Million NTIA Grant for 5G Open RAN Integration and Deployment Center

PR Newswire | January 16, 2024

DISH Wireless, a subsidiary of EchoStar, was awarded a historic $50 million grant from the U.S. Department of Commerce's National Telecommunications and Information Administration (NTIA) to establish the Open RAN Center for Integration & Deployment (ORCID). ORCID will allow participants to test and validate their hardware and software solutions (RU, DU and CU) against a complete commercial-grade Open RAN network deployed by DISH. "The Open RAN Center for Integration and Deployment (ORCID) will serve a critical role in strengthening the global Open RAN ecosystem and building the next generation of wireless networks," said Charlie Ergen, co-founder and chairman, EchoStar. "By leveraging DISH's experience deploying the world's first standalone Open RAN 5G network, ORCID will be uniquely positioned to test and evaluate Open RAN interoperability, performance and security from domestic and international vendors. We appreciate NTIA's recognition of DISH and ORCID's role in driving Open RAN innovation and the Administration's ongoing commitment to U.S. leadership in wireless connectivity." To date, this grant represents NTIA's largest award under the Public Wireless Supply Chain Innovation Fund (Innovation Fund). ORCID will be housed in DISH's secure Cheyenne, Wyoming campus and will be supported by consortium partners Fujitsu, Mavenir and VMware by Broadcom and technology partners Analog Devices, ARM, Cisco, Dell Technologies, Intel, JMA Wireless, NVIDIA, Qualcomm and Samsung. NTIA Administrator Alan Davidson and Innovation Fund Director Amanda Toman will join EchoStar Co-Founder and Chairman Charlie Ergen, EchoStar CEO Hamid Akhavan, EVP and Chief Network Officer Marc Rouanne and other stakeholders to announce the grant and tour a DISH 5G Open RAN cell site later today in Las Vegas. During this event, DISH will outline ORCID's unique advantages, including that it will leverage DISH's experience as the only operator in the United States to commercially deploy a standalone Open RAN 5G network. DISH and its industry partners have validated Open RAN technology at scale across the country; today DISH's network covers over 246 million Americans nationwide. At ORCID, participants will be able to test and evaluate individual or multiple network elements to ensure Open RAN interoperability, performance and security, and contribute to the development, deployment and adoption of open and interoperable standards-based radio access networks. ORCID's "living laboratory" will drive the Open RAN ecosystem — from lab testing to commercial deployment. Below are highlights of ORCID: ORCID will combine both lab and field testing and evaluation activities. ORCID will be able to test elements brought by any qualified vendor against DISH's live, complete and commercial-grade Open RAN stack. ORCID will use DISH's spectrum holdings, a combination of low-, mid- and high-band frequencies, enabling field testing and evaluation. ORCID will evaluate Open RAN elements through mixing and matching with those of other vendors, rather than validating a single vendor's stack. DISH's experience in a multi-vendor environment will give ORCID unique insights about the integration of Open RAN into brownfield networks. ORCID's multi-tenant lab and field testing will occur in DISH's secure Cheyenne, Wyoming facility, which is already compliant with stringent security protocols in light of its satellite functions. About DISH Wireless DISH Wireless, a subsidiary of EchoStar Corporation (NASDAQ: SATS), is changing the way the world communicates with the Boost Wireless Network. In 2020, the company became a nationwide U.S. wireless carrier through the acquisition of Boost Mobile. The company continues to innovate in wireless, building the nation's first virtualized, Open RAN 5G broadband network, and is inclusive of the Boost Infinite, Boost Mobile and Gen Mobile wireless brands.

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Events