5G
Article | November 25, 2021
Introduction
5G is predicted to have a significant impact on how cloud computing operates in the future. It will undoubtedly revolutionize the communication and networking industry. In addition, it will have a significant impact on all other industries. Transmission speeds will be 10 times faster in 5G than in 4G.
Apart from networking and communication industries, it will also revolutionize other healthcare, automotive, and many more. Commercial 5G smartphones are already in the market. A study report by Ericsson Mobility predicts that there will be 5G subscriptions by 2023. It will account for about 20% of the mobile data traffic.
5G, with its features such as low latency performance and high speed, has all the capabilities of developing cloud computing and take to the next level. As a result, there will be an unpredictable positive impact of 5G on cloud computing, especially in the industries and sectors that use the latest technologies such as the Internet of Things, machine learning, and AI.
It has already started redefining business networks. It is also expected to make tremendous, unpredictable progress and changes in how cloud computing and networks perform in storing, moving, and accessing data. It will be possible as 5G brings more technological applications to make digital transformations faster and more efficient for businesses.
5G Network- Advantages
As said in the introduction, there are many advantages to 5G network. Some of them can be:
Greater transmission speed
Lower latency
Higher capacity
Compared to 4G, 5G has increased bandwidth.
These features will change the way people work, live, and play in the future once the 5G network comes into play widely.
How Will 5G Impact Cloud Computing?
Undoubtedly 5G has the potential to redefine the future of cloud computing. It will transform edge computing. Let us look in detail at what impact 5G will bring in cloud computing.
Mobile Cloud Applications to Become More Efficient and Widely UsedHow Will 5G Impact Cloud Computing?
Undoubtedly 5G has the potential to redefine the future of cloud computing. It will transform edge computing. Let us look in detail at what impact 5G will bring in cloud computing.
Mobile Cloud Applications to Become More Efficient and Widely Used
Organizations today widely use cloud-enabled applications for customer services as well as for their different operations. Once the widespread use of the 5G network starts, the mobile application will become more efficient and widely used. It will be reflected more in the industries such as healthcare and banking.
Enabling Cloud Service Providers to Reach Customers Reliably and Easily
5G will make a machine to machine communication and larger computing possible. This will make accessing virtual machines via phones a common practice. Mobile users will get more features and options from cloud computing enterprises. Remote workers will access cloud services as hotspots will become faster and uninterrupted.
Complete Transformation of Edge Computing
The emergence of edge computing has solved the issues of unnecessary traffic on the cloud and latency. The need for edge computing became strong when internet penetration and IoT came into existence. Now, G5 will make edge computing grow, making it an essential thing.
G5 will transform edge computing entirely and increase the demand for it. As a result, edge and 5G are becoming mutually reinforcing phenomena. 5G will work on edge computing to provide quick real-time data. This is because edge computing has the potential to provide low latency and higher bandwidth.
Faster Streaming
5G network will surprise companies and entities with its fast data transfer capabilities. It will be ten times faster than the 4G network. It will facilitate storage and faster real-time streaming and thus productivity at its best.
“If everything you touch has to go to a data center and back before you see the animation, you're going to notice. Working at the 5G Lab in a mobile edge cloud, all of a sudden, what we thought would be impossible can happen because the 5G network is so fast.”
- Ian McLoughlin, LiquidSky Software founder, and CEO
Work from any Location
As 5G is ten times faster than 4G in transferring data due to its better connectivity, employees can work from any location. It will make remote work possible anywhere.
Better Security Systems
As technology is ever-evolving, hackers and online frauds come with advanced techniques to steal data. They hold the sensitive information of organizations and do unimaginable damage.
Once 5G is rolled out widely, administrators will recognize such frauds in advance and prepare to mitigate such cyber-attacks.
Summing up
Cloud computing is undoubtedly going to have an impact on cloud computing. The connectivity of 5G is ten times faster than 4G. IT will help people connect to their workplaces from any location. Remote work will make it easier.
Cloud mobile applications will become more efficient and reliable. The service providers will have a good relationship with customers as they will be providing prompt and reliable service in terms of connectivity. Joining together with other technologies such as edge computing, AI, ML, technology is expected to get into another level with the wide roll-out of 5G.
Frequently Asked Questions
How will 5G affect cloud computing?
Cloud computing will have a complete transformation and improvement when 5G is rolled out widely. The 5G connectivity has the features such as cloud virtualization, Ultra-reliable low-latency communication, better latency, increased bandwidth, and more flexible cloud-based management.
How is 5G going to impact lives?
5G is capable of controlling services remotely. In addition, 5G will enhance autonomous driving, personal communication, IoT, AI, and augmented reality. It will also change the way companies store, access, share and protect data.
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NETWORK MANAGEMENT
Article | November 22, 2021
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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NETWORK SECURITY
Article | October 27, 2021
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.
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NETWORK SECURITY
Article | October 26, 2021
Cybercrimes have increasingly become a matter of concern for companies worldwide. Over the past few years, the rise of big-ticket ransomware attacks and exposure of perilous software supply chain infections has awakened organizations to various digital dangers.
So, the big question is, how to combat the security threats that are on meteoric growth?
The best solution is to adopt a security vulnerability assessment.
What is Security Vulnerability Assessment?
A vulnerability assessment involves a systematic review of security hazards, which helps identify IT infrastructure’s weaknesses, risks, and vulnerabilities.
When it comes to mitigating vulnerabilities and resolving issues, the collective imperative is to analyze the problem areas before getting them fixed.
A security assessment is critically important to combat the complexities and with an effective vulnerability assessment program, organizations use the tools required to comprehend the probable security weaknesses and enable the protection of systems and data from intruders and unauthorized breaches.
For most organizations, ensuring the safety of devices, networks, applications, and digital assets are part of a broader vulnerability management strategy. It includes an extensive assessment, in-depth processes, and mitigation methods to explore the entire threat spectrum.
Typically, it is conducted regularly. Vulnerability assessment offers a firm assurance in the security of data, especially when some alterations have been implemented or a new service has been added, or, for that matter, and installation of new equipment has taken place.
Each assessment provides a perspective about the risk in its periphery and suggests solutions to control the risk factors and the evolving threats.
Why Security Vulnerability Assessment Is Necessary?
The perpetual threat of cybercrime has necessitated the demand for vulnerability assessments significantly. They make organizations realize their security defects and contribute towards mitigating them.
Hackers are forever ready to make phishing attacks. As per reports, hackers are at work every 39 seconds. Thus, it is extremely important to be vigilant or complacent to activate hackers’ and cybercriminals’ machinations. Over the period, cybercrimes are fluctuating and thus need ongoing attention.
The ideal solution is to undertake consistent vulnerability assessments to safeguard confidential data, systems, and networks. Furthermore, it helps organizations understand the risk and enables smart decision-making.
To ensure security, companies ought to conduct both external and internal scans of their networks.
According to Gartner (paywall), “Large organizations with thousands of employees, tens of thousands of servers and many operating systems receive hundreds of requests per year to patch thousands of vulnerabilities that cannot be remediated in less than 15 days.”
One of the best reasons security vulnerability assessments are important is because it confirms an enterprise’s management processes and whether it has covered every critical patch through outlined existing remediation.
Why Do Companies Need Vulnerability Assessment?
A vulnerability assessment provides companies insightful details on all types of security discrepancies in their environment.
It paves ways to evaluate the risks associated with the flaws.
This helps organizations have a better knowledge of their security scare, overall weaknesses, and assets.
Moreover, the first thing that strikes us on hearing about a cyber-attack is the security of data. With the right and adequate implementation of security assessments, the safety and security of important data could be easily protected. A security assessment would be helpful to reduce irrelevant expenses and make space and increase the IT budget to invest in other key aspects.
Undoubtedly, data breach causes substantial loss to an organization, which leads to legal hassles and financial hazards. In fact, most of the time companies fail to recover the loss.
Thus, it doesn’t harm to place solid policies and methods to strengthen the entire security position of the organization and this can only be possible with a strategic security vulnerability assessment.
In a nutshell, this would keep the companies aware and, in all likelihood, keep the cyber-criminals at bay.
A CASE STUDY ANALYSIS
To cite an example, let’s take how Zensar conducted a three-pronged vulnerability assessment with port scan and penetration scanning. It determined the security of its offerings to meet customer requests for Brainshark, a leading provider of on-demand presentation solutions, helping customers deliver business interaction across 600+ ranking companies in the market.
While Brainshark knew their systems were secure and could also establish it through their documentation, they still undertook a third-party security vulnerability assessment.
Zensar’s vulnerability assessment procedures were based on the industry’s best practices that included tests for SQL injection, cookie manipulation, access control weakness, session state, and cross-site scripting.
The focus of the test was to identify the host and application security concerns. Upon completing the tests and assessments, Brainshark expressed satisfaction and was confident enough in their ability and solution. They knew their security posture was highly protected and secure.
Types of Vulnerability Assessments:
Vulnerability assessments unearth a variety of system and network vulnerabilities. This indicates the reliability of the assessment process, which is implemented with different tools, scanners, and methods that helps discover the vulnerabilities, risks, and threats.
Network-based assessment scanning: It is used to determine the presumptive network security attacks. This kind of scanning can also detect the vulnerable systems on wired as well as wireless networks.
Host-based scans: It is easy to locate the vulnerabilities in servers or other network hosts with host-based scanning. This type of scanning provides visibility into the configuration settings and legacy systems.
Database scans: Database scans ascertain the weak points in a database to preclude malefic attacks.
Application Scans: It examines websites to identify and recognize software vulnerabilities and inaccurate configurations in network or web applications.
Summing Up
Organizations need to be watchful every minute and ensure the security posture is rigorous, which is only possible with security vulnerability assessments. Based on this criterion, understanding company risks gets simplified in turn preventing intrusions and threats.
FREQUENTLY ASKED QUESTIONS
What Are the Advantages of Security Vulnerability Assessment?
There are several advantages attached to security vulnerability assessments. To put it precisely, it can help identify the vulnerabilities before cybercriminals do and determine the level of risk.
Undoubtedly, opting for vulnerability assessment would save a lot of time and money and mitigate the risk and prevent the irrelevant expenditure that follows after the cyber-attacks.
What Are the Disadvantages of Security Vulnerability Assessment?
While vulnerability assessments are highly advisable, it has its share of drawbacks which cannot be ignored. One of the primary limitations of vulnerability assessment is that it does not hint at every vulnerability that exists. Moreover, it sometimes signals false positives too.
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