Artificial Intelligence & Machine Learning in Network Management
Artificial Intelligence & Machine Learning in Network Management
Since its inception in 1955, Artificial Intelligence has remained at the forefront of academic research. However, it wasn’t until decades later that AI emerged from academia and began being applied to everyday life.
Whenever AI captures the attention of media writers, their creative potential for it is seemingly boundless. Attention to this topic shifts in cycles, typically occurring about once every decade. Sadly, the media has painted an unrealistic picture of travel with far too ambitious expectations. AI has frequently disappointed, with the media quickly turning their attention to something else. However, this cycle repeats itself about every ten years or so. The potential of AI has drawn intense media attention and sizable investments into the field; however, reports of its shortcomings have had a deterrent effect on investment.
As Artificial Intelligence continues to evolve, it has become a viable tool for journalists with the capacity to generate impressive copy. In only 65 years, AI technology has made remarkable progress and is beginning to impact everyday life. This time around, the Artificial Intelligence Revolution will entice public attention for a more extended period and enable investments that facilitate growth in this field. As such, it can create products with commercial applications.
Artificial Intelligence is revolutionizing how networks are managed, enabling rapid and efficient data processing. Organizations can expect significant performance improvements by utilizing AI in IT network Support management.
What is AI?
For academics, AI has developed a well-defined meaning over the years. Early on, the definition of artificial intelligence (AI) was as simple as whether a machine could convince another human that they were engaging in conversation with another person. This is known as the Turing Test.
The industry and academic research about AI are focused on using heuristics and probability to execute programming code. By changing the perception of AI from something out of this world to something more commonplace, those within the field have been able to dissipate and lessen many of the sensationalized expectations often generated by journalists. By removing the risk of others overestimating, Artificial Intelligence experts are more likely to attain realistic goals.
“Artificial Intelligence is the optimal solution to today’s biggest IT issue: quickly sorting through massive amounts of data.”
It’s only possible to identify and address issues if you make a point of noting their origin. Unfortunately, it can be hard to predict future mishaps. Subsequently, IT systems are incredibly effective at recording and analyzing data.
To effectively manage all system event records, the critical first step is to acquire a log management tool. The sheer volume of data generated by this strategy surpasses traditional programming tools’ ability to uncover meaningful insights in a timely fashion that is suitable for taking action. AI is the perfect choice for this application, combining the efficiency of computing with human-like decision-making skills.
Computers have a significant benefit – they are organized and calm. On the other hand, humans may grow impatient or overlook details; computers never fall short in either of these aspects. Human intelligence can quickly eliminate unlikely options, leading us directly to the probable solution.
After a car mechanic starts the engine and hears a click, they will often diagnose it as the starter motor. A car analysis computer program would systematically examine every system, part, and feature of the car until it pinpoints any existing issues. The concept of “probability” embedded in a mechanic’s initial diagnosis is emblematic of artificial intelligence; AI integrates probability into its code, unlocking robust solutions to complex problems. If the car won’t initiate, there’s no point for the mechanic to spend time checking out those bolts on its wheels. Artificial Intelligence (AI for network management) has revolutionized how programs can solve problems, allowing them to skip over superfluous steps and arrive at solutions more expeditiously.
What is Machine Learning?
ML lies within the field of Artificial Intelligence and has revolutionized automation processes. When you inquire why a mechanic knows there is an issue with the starter motor and it’s unnecessary to investigate the wheel bolts, he will likely tell you that it was an easy observation. Nevertheless, this instantaneous understanding of an issue would be less evident to a two-year-old. Machine learning supplies the bedrock for recognizing what is feasible.
AI is only suitable for a few applications. However, ML is becoming a helpful aid in cutting out time-wasting fault diagnosis, so it speeds up the delivery of results.
Once a difficulty has been pinpointed, AI initiates an approach known as “triage” to tackle the issue. Allocating resources to this area helps us enhance our impact. For instance, if the system recognizes a user account exhibiting suspicious activity, it is advantageous to deploy more in-depth monitoring procedures for that account. It would be a waste of resources to apply the same degree of scrutiny to all accounts.
AI and ML in network management
In network management, time is of the essence for addressing any issues. AI is revolutionizing the way we manage networks, providing solutions to problems that require urgent attention. Four key areas benefit from this groundbreaking technology. They are:
- Traffic management
- Performance monitoring
- Capacity planning
- Security monitoring
Across the board, Machine Learning is indispensable in addressing all AI-related issues.
Traffic management
Traffic management and performance monitoring go hand in hand, and their correlation is undeniable. Traffic management services analyze the traffic of packets through network devices, and performance systems rely on status updates from those same gadgets for efficient operations.
Network managers can maximize physical performance with the use of traffic-shaping techniques. For IT departments with a limited budget, planning capacity at less than peak throughput is possible as long as interactive applications like VoIP, surveillance cameras, and video conferencing systems have priority access through the system switches. Establishing a specific VLAN for voice traffic is an established and reliable technique. This technology enables voice signals of the office telephone network to be transmitted over the same wires used for data.
AI technology has yet to be adopted by most switch manufacturers for onboard traffic management functions. D-Link has taken the lead on its competitors in this area with its Auto Surveillance VLAN (ASV) and Auto Voice VLAN (AVV) systems, conveniently built into their intelligent switches.
ASV is designed to provide an undisturbed channel for security camera video feeds, while AVV channels exclusively manage voice traffic. With the help of AI technologies, the switch can recognize and prioritize different traffic types without a network manager’s manual intervention. This means no more having to manually tag VLAN packets or define how the switch should treat each package.
Cisco Systems is the undisputed leader in worldwide network switch production. The company has AI products but doesn’t implement artificial intelligence on board its switches. As an alternative, it shifts the data into network monitoring and management software that can be hosted on servers.
Network Monitoring
By merging hardware and software products, Cisco has developed an AI-driven solution called “Cisco DNA Assurance.” This comprehensive package offers increased efficiency when compared to traditional methods. This sophisticated package highlights the synergy between AI and ML when working alongside non-AI components to create a forward-thinking solution. The physical aspect of this series is the revolutionary Catalyst 9000 line of switches. The switch is an instrumental data contributor to the AI service but does not possess any artificial intelligence features.
The AI-powered addition to the bundle is dubbed Cisco AI Network Analytics – delivering unprecedented analytics capabilities. This state-of-the-art system leverages data from multiple sources, including switch metrics sampling, to craft innovative solutions for detecting and resolving network performance issues.
AI services have no direct influence on the collection of network monitoring data. Rather than remaining a passive observer, this system examines linked data from multiple sources to diagnose the root cause of issues. By leveraging the power of Artificial Intelligence, businesses can now streamline their services and increase productivity like never before. This cutting-edge technology is revolutionizing how we do business today!
Traditional network performance monitoring
Network performance monitors have consistently monitored the switches’ status and conveyed this data in clear, insightful visuals on the network management console. Network monitors commonly depend on Simple Network Management Protocol (SNMP) services for successful operation. This effective system relies on regular status reports created by the network’s routers and switches. The network management console ascertains the frequency of updates; however, in times of urgency, it can grant a device privileged access to transmit an immediate report.
SNMP-based monitoring is an all-encompassing process and does not need to be substituted by AI procedures. Nevertheless, we can refine root cause analysis for even more efficient results. This is why Cisco wisely opted for an AI approach that puts novel techniques in a scrutinized module.
Network performance vs. traffic analysis
Investigating the root cause of a problem can be laborious and requires human intelligence to identify it. When analyzing the source of performance difficulties, traffic throughput calculations must be executed; however, if only partially addressed, the issue can migrate from one network node to another.
Traversing a network produces much data to process, which is one of the main components for conducting practical Network Traffic Analysis. Acquiring packets and saving them yields vast amounts of data, necessitating abundant storage space. Sifting through all the retained packages to detect valuable data can be tedious, and deciding which facts are pertinent to optimizing performance requires expert discernment.
AI and machine learning can significantly assist traffic analysis in network management. An ML process can continuously run, collecting total results and tracking real-time statistics. By utilizing this strategy, it is now possible to make minute adjustments to routes and adjust traffic flow with the help of techniques such as queuing and application prioritization.
Autonomous nodes vs. coordinated traffic management
In contrast to Cisco, which carries out its dynamic AI-based traffic management processes externally, D-Link performs these tasks on the switch itself. When compared, the D-Link strategy is a quicker and more straightforward solution than Cisco’s method. Its immediacy provides instant results that are easier to achieve.
Autonomous decision-making by network devices is perfect for successfully routing on the Internet. By implementing AI services for traffic management, all the devices on your network can run the same protocols and operate with maximum efficiency. Therefore, the D-Link strategy is sensible if every switch belongs to the same AI-enhanced series from D-Link.
When presented with capacity limits, the efficacy of AI traffic management systems on private networks is drastically reduced due to their finite allocation of resources. By observing all switches from a centralized monitor, Cisco has developed the most comprehensive approach to resolving traffic issues and optimizing capacity planning.
Cisco is one of many hardware providers to offer a sophisticated and unified AI-supported network management suite that accommodates traffic monitoring, managing, and capacity planning. Moreover, Juniper Networks includes an AI-based feature of natural language processing on top of all these functions.
Juniper Networks’ Virtual Network Assistant of Marvis is a revolutionary voice-activated chatbot that allows for swift network monitoring and provides immediate solutions to any issues you may encounter. Below, we will further explore how AI can be used for chatbots. With the innovative Mavis system, you can access a comprehensive solutions database with only your voice! Its revolutionary voice command interface makes accessing data easier and faster than ever. This concept is comparable to Google’s Voice Search, which utilizes machine learning to identify and provide the most suitable answer for your query.
By continuously gathering and analyzing data on previously encountered issues, the Marvis search engine can zero in on related problems with each successive inquiry, resulting in more accurate resolutions. Traffic analysis and capacity planning may be of little concern, but they are nevertheless essential tasks. However, due to the time it takes and considering all possible traffic scenarios, preparing for this can be quite a challenge.
Draw on the power of Juniper Networks’ Mist AI system to supercharge your Mavis platform. In addition, this module provides analytics that can sift through the most recent logs to collect data and pinpoint any potential issues. Not only does Mist AI activate embedded reporting tools present on switches to gain valuable insights regarding performance issues, but it can also help pinpoint the root cause of these problems.
Neither Cisco Systems nor Juniper Networks can monopolize the reporting components found in their network devices. Third-party network monitoring tools leverage these sources of data for maximum potential. With its powerful cloud-based system, LogicMonitor unites the advantages of SNMP-based network performance monitoring and traffic analysis based on packet sampling. Just like the network monitoring system of Cisco Systems and Juniper Networks, LogicMonitor employs AI to identify root causes.
With the aid of ML processes, LogicMonitor’s system intelligently adjusts alert thresholds to limit the number of unnecessary alerts and precisely grade events for greater accuracy.
Capacity planning
Capacity planning needs much more data input than on-the-spot device health monitoring. AI and Machine Learning (ML) processes are astonishingly beneficial to network management, particularly in this area. A retrospective data study can be conducted slower to forecast traffic growth trends. However, rapid re-routing is necessary when equipment unexpectedly fails, and traffic management measures need to be implemented immediately.
Network staff often feel the strain from juggling long-term capacity planning and needing to act quickly for immediate failure re-planning. To reduce such overwhelming pressure and stress, automated analysis tools are necessary. Artificial Intelligence and Machine Learning can expedite the process of capacity planning and traffic shaping, making these tasks more efficient. Cisco Systems and Juniper Networks have incorporated extra capabilities into their AI products so that you can get more out of them.
Enterprise Intelligence’s Service Delivery Intelligence (SDI) leverages the power of AI to accelerate performance examination data searches, allowing for quick problem identification and providing invaluable solutions. This data analysis solution is the perfect tool for AI-assisted capacity planning, providing maximum efficiency and accuracy.
Capacity planning necessitates more than just a forecast of total network traffic. When designing a network, it is essential to keep track of the load on each switch. If you increase capacity in one area, more pressure may be placed on other controls that are connected. Accordingly, capacity planning necessitates traffic flow simulations and repetitive processes or switch-by-switch performance expectations. Artificial Intelligence-powered capacity planning tools have made this process incredibly swift and efficient.
Security monitoring
AI-driven triage in data examination, combined with the exceptional performance metrics of Machine Learning, are both critical components for advanced security monitoring. Artificial Intelligence and Machine Learning can take security systems to another level, providing two distinct forms of protection. To ensure organizations remain secure, (SIEM) & (UEBA) are two powerful tools. Both offer unparalleled protection to businesses of any size.
SIEM tools
The SIEM software category comprises two distinct components, which work together to create a comprehensive security solution. These are two concepts in security management: SIM (Security Information Management) and SEM (Security Event Management). Network traffic analysis lies at the cornerstone of Security Event Management. AI systems empower the Security Information Management segment of SIEM, providing immense advantages.
Security Information and Event Management (SIEM) can detect any suspicious activity by carefully analyzing log files. The SIEM system collects all of the log messages from every device or application used by your company, giving you a comprehensive overview. These messages are combined and standardized so they can be searched collectively as one single database. This process typically involves in-depth computation. However, AI techniques can drastically decrease the time needed to detect malicious activities.
Speed is of utmost importance in security monitoring, as it’s pointless to identify a hacker if the damage has already been inflicted. Consequently, the AI approach of recognizing and analyzing activity patterns allows vast amounts of information to be searched quickly enough to prevent any harm from being done by malicious actions.
Not only are AI-driven SIEM systems fast, but they can also function as powerful network performance monitors. Ultimately, SNMP reports and network monitors are both forms of logging. Dynatrace is a powerful network monitoring solution that utilizes Artificial Intelligence (AI) methods to monitor performance and serves as a Security Information and Event Management (SIEM) system.
User and Entity Behavior Analytics
UEBA is a way to keep tabs on user and endpoint activity with precision. UEBA collects log data from every user account and IP address, tracking all system access events and utilizing files and resources. Furthermore, entity tracking can be extended to monitoring the activities conducted by external IP addresses.
We can analyze and interpret meaningful insights by feeding records into a machine-learning algorithm. This advanced technology can quickly identify any sudden changes in behavior, immediately alerting the account or IP address. This is the initial step of a triage procedure. Therefore, complete user surveillance measures are only necessary for some users. These granular tracking protocols take effect only after a UEBA alert is triggered for a designated account.
UEBA is an increasingly essential part of many cybersecurity systems and has become a staple in Intrusion Detection Systems (IDS) and Next-generation Antivirus Systems (NGAV). As organizations strive for enhanced security, UEBA’s recognition capabilities are invaluable.
Intrusion Detection Systems
The relevance of UEBA became paramount for Intrusion Prevention Systems (IPSs), Identity Detection Systems integrated with automated mitigation measures. Formerly, intrusion prevention systems would peruse activity patterns to instantly discontinue a user’s access if their actions did not follow the standard. Unfortunately, these systems were too strict and blocked staff from performing their duties, prohibiting huge demographic groups from gaining access to the website.
Those who devised the regulations for “normal behavior” had never experienced the daily operations of every business enterprise. Consequently, the software designers’ study of what was thought to be suspicious activity in one business could be standard practice for other companies. This is called “false-positive reporting,” which negatively affects IPSs’ desirability.
To establish a baseline, IPS developers included UEBA routines rather than defining “normal activity” in the software package. UEBA eliminates the issue of false-positive reporting. This innovation has facilitated the use of IPSs, thus fueling more advancements in this domain.
Next-generation antivirus systems
The phrase “next-generation” when it comes to products like firewalls and antivirus systems implies the utilization of UEBA for baselining. Due to the sheer number of new viruses being created, the AV industry nearly collapsed from their antiquated method of issuing a list of malware to look for.
One of the most significant drawbacks of traditional AV programs is that at least one customer must be infected with a virus before the supplier becomes aware that it exists. While researchers attempt to identify the virus and develop a solution, many other customers of the antivirus system become infected. Thus, it is essential to be proactive in preventing cyber threats before they occur – time is of the essence!
Nobody enjoys taking a gamble and possibly becoming the first infected by a malicious virus that their purchased antivirus software fails to detect. Consequently, the entire endpoint protection industry was in peril due to its lacking services. UEBA offers a highly effective solution to our problem.
UEBA allows antivirus services to detect a virus the minute it attempts infiltration onto protected networks, preventing potential damage. The “entity” component of User and Entity Behavior Analytics assesses all active processes to detect abnormal behaviors on an endpoint.
AI for network management
Artificial Intelligence is the perfect tool for completing essential tasks in network monitoring. For this industry, customers should prioritize finding competent AI for network management tools. As time progresses, many top-rated network monitoring systems have been leveraging AI and ML processes which have proven invaluable for their success. Soon enough, these innovative technologies will become a staple in all networks, ensuring that users can reap maximum benefits from their application.
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