C3 Client Case Study (SD-WAN): Revere Plastics Systems

Unified Communications, Network Security

Article | July 10, 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? 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Data Center Networking

Article | July 5, 2023

The Verizon 5G Business Internet rollout that started in parts of Chicago, Houston and Los Angeles continues this month in 21 new markets with more on the way, the company announced Thursday. Verizon Business is marketing fixed-wireless connectivity as an alternative to cable for enterprise and small to midsize customers. In a press release, Tami Erwin, CEO of Verizon Business, said, "As 5G Business Internet scales into new cities, businesses of all sizes can gain access to the superfast speeds, low latency and next-gen applications enabled by 5G Ultra Wideband, with no throttling or data limits."

Enterprise Mobility, Mobile Infrastructure

Article | June 16, 2023

Digital liberation has opened up several avenues for businesses. The current scenario is a bright example of how a remote or hybrid work model seamlessly became a norm, establishing digital workspaces, including laptops and PCs. But this has also led companies to deal with a lot of challenges in managing their enterprise mobility. Whether it is the security or Bring Your Own Device (BYOD) to the user experience or migration, Mobile Device Management (MDM) plays a significant role in digital transformation. PROTECTION VS. PRIVACY: THE PROBLEM WITH (MDM) – INTRODUCTION Mobile device management pertains to software solutions and reliable practices that enable companies to easily manage and obtain wide-ranging mobile devices in compliance with corporate guidelines. In addition, the MDM functionality addresses the security of devices and data, management of devices, and configurations. Essentially, MDM as security is an element of an enterprise mobility management solution that integrates a clustered set of tools to secure and manage mobile apps, BYOD devices, content data and access, configurations, risk management, software updates, and application management. MDM allows a single-interface control over all connecting devices, enabling each device registered for corporate use through the MDM software to be easily monitored, managed, and controlled as per organizational policies. “It’s clear that our network is better protected. We have solved our BYOD issues and can rely on great support via e-mail, phone, or Skype.” - Raymond Bernaert, IT Administrator at ROC Kop van Noord, the Netherlands However, when it comes to an understanding, this technology is of utmost importance to consider the key challenges that companies face regarding protection vs. privacy of mobile device management. Key Challenges MDM solutions are built to improve visibility and secure better control into an end user’s mobile device activity. However, unrestrained tracking of the device’s activities poses a huge threat to the end user’s privacy. For instance, the mobile device management solution may track real-time location and browsing detail. The information exposes employees’ data and usage habits beyond the employer’s device management and security needs. Moreover, as the mobile device market expands, employees choose devices from various brands and platforms, which companies extend support and manage; nevertheless, unanticipated security issues with a specific platform and software version could emerge at any point. Thus, executing the entire process without compromising the end-user convenience. Now, let’s check out some of the most common mobile device management challenges. Security Using numerous devices and endpoints could potentially increase the risk of hacking. This is because, for hackers, it would be a lot easier to exploit the endpoints. And, no wonder mobile device security is one of the fastest-growing concepts in the cybersecurity landscape today. Incorporating mobile devices under the umbrella of mobile device management would be helpful to bridge the vulnerable gaps and prove to firmly manage the entire digital fleet, including mobile phones and PCs. In addition, this will increase up-time significantly and containerize the personal data from corporate data. The Privacy Issue Though MDM solution helps organizations obviate data breaches, they also open up doubt and questions regarding employee privacy. This is because various MDM tools allow employers to monitor the entire device’s activities, including personal phone calls and web activity, at any point. Subsequently, this empowers the IT team to command control in corporate security, whereby they perform many such remote actions, which harm the employees’ privacy. Organizations over the years have used mobile device management solutions with the intent to put BYOD in place. When an enterprise enables BYOD, employees use their devices to access data to help achieve the tasks. With the intent to secure the endpoints, companies choose MDM as their key solution and take control over the entire mobile device, but with that comes the potential for abuse. So, naturally, there is an unwillingness among employees to get MDM installed on their devices. Network Access Control (NAC) The sudden surge in digital workspace culture has also brought in additional complications with varied mobile devices. It is crucial to ensure the team has access to all the apps and corporate data they need. However, it is also important to note that there should be a check on direct access to the data center. One of the core elements for enterprise mobility is network access control (NAC). NAC scrutinizes devices wanting to access your network and it carefully enables and disables native device capabilities distinctly. With designated devices getting connected to the network as per resource, role, and location, it is relatively easy for NAC to ascertain their access level based upon the pre-configured concepts. User Experience It is essential to consider the end-user experience while managing mobility as it often becomes a big challenge. Therefore, a successful mobile device management structure lies mainly in creating a satisfying user experience. A company that uses various devices and has extensive BYOD users may find VMware Workspace ONE or MobileIron effective. However, if the enterprise is all Apple iPhones, the ideal enterprise mobility management would be Jamf Pro, an Apple-only EMM. A single sign would be a perfect method to get into the virtual desktop to ensure efficiency for the remote workers. Moreover, it won’t ask you to sign into different applications separately. Sturdy enterprise mobile device management is an absolute necessity to have a hassle-free experience. Let’s cite the example of this case study, where ‘The Department of Homeland Security (DHS) Science and Technology Directorate’ (S&T) initiated the Next Generation First Responder (NGFR) Apex program to assist tomorrow’s first responder in becoming protected, connected and aware. DHS S&T held a series of NGFR Integration Demonstrations to incrementally test and assess interoperable technologies presently at the development stage. These demonstrations have changed from tabletop integration to field exercises with partner public safety agencies incorporating increasingly complex technology. The NGFR- Harris County OpEx included 23 varied DHS and industry-provided technologies involving six Internet of Things (IoT) sensors, five situational awareness applications and platforms and live-stream video feeds. Additionally, Opex technologies also integrated body-worn cameras and real-time data aggregation and access across numerous agencies. In a nutshell, this case study identifies and explains the mobile device management (MDM) solutions that provided an application-level cybersecurity evaluation and remote device management. The Opex addresses how nationwide public safety agencies could utilize MDM to enhance the operational deployment of new devices and applications. Final Words There are surely both pros and cons involved in mobile device management. Over the years, the BYOD program has turned out to become a norm in corporate culture. In addition, the use of personal devices has significantly surged due to the gradual increase in remote and hybrid work models. Thus, many believe that the MDM solution is naturally aligned with BYOD. However, the fact is, a perfectly planned BYOD policy is the only way to ensure clarity. Having no policy in place will expose a firm to the so-called ‘Shadow IT’ as users will circumvent the IT infrastructure working from their mobile devices. Though the breach of privacy is likely, the policy can be tailored based on the company’s needs. The IT security is adequately maintained and protected and strikes a balance between protections vs. privacy in mobile device management. Frequently Asked Questions What can mobile device management do? Mobile device management keeps business data safe and protected and secures control over confidential information. MDM also exercises its power to lock and remove all data. This is the capability that sustains the device’s security. What are different mobile management tools? With the introduction of Bring Your Own Device (BYOD), several enterprise mobility management tools have also been inducted into MDM. To name a few, some of the prominent MDM tools are Miradore, Citrix Endpoint Management, and SOTI Mobicontrol. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What can mobile device management do?", "acceptedAnswer": { "@type": "Answer", "text": "Mobile device management keeps business data safe and protected and secures control over confidential information. MDM also exercises its power to lock and remove all data. This is the capability that sustains the device’s security." } },{ "@type": "Question", "name": "What are different mobile management tools?", "acceptedAnswer": { "@type": "Answer", "text": "With the introduction of Bring Your Own Device (BYOD), several enterprise mobility management tools have also been inducted into MDM. To name a few, some of the prominent MDM tools are Miradore, Citrix Endpoint Management, and SOTI Mobicontrol." } }] }

Article | April 16, 2020

At about 9.30pm on Easter Monday, in the small Dutch town of Almere near Amsterdam, the fire brigade was called to put out a blaze at a large telecoms mast—the second fire of its kind that night in the area. Though neither of the Almere towers were equipped with any of the latest 5G telecoms equipment—in fact one was designed only for use by the emergency services—authorities soon concluded that the fires were perpetrated by vandals acting in the name of an unusual theory: that 5G networks have contributed to the coronavirus pandemic.