Posts Tagged ‘Network Monitoring’

5 Ways Flow Based Network Monitoring Solutions Need to Scale

Partial Truth Only Results in Assumptions

A common gripe for Network Engineers is that their current network monitoring solution doesn’t provide the depth of information needed to quickly ascertain the true cause of a network issue. Imagine reading a book that is missing 4 out of every 6 words, understanding the context will be hopeless and the book has near to no value. Many already have over-complicated their monitoring systems and methodologies by continuously extending their capabilities with a plethora of add-ons or relying on disparate systems that often don’t interface very well with each other. There is also an often-mistaken belief that the network monitoring solutions that they have invested in will suddenly give them the depth they need to have the required visibility to manage complex networks.

A best-value approach to network monitoring is to use a flow-based analytics methodology such as NetFlow, sFlow or IPFIX.

The Misconception & What Really Matters

In this market, it’s common for the industry to express a flow software’s scaling capability in flows-per-second. Using Flows-per-second as a guide to scalability is misleading as it is often used to hide a flow collector’s inability to archive flow data by overstating its collection capability and enables them to present a larger number considering they use seconds instead of minutes. It’s important to look not only at flows-per-second but to understand the picture created once all the elements are used together. Much like a painting of a detailed landscape, the finer the brush and the more colors used will ultimately provide the complete and truly detailed picture of what was being looked at when drawing the landscape.

Granularity is the prime factor to start focusing on, specifically referring to granularity retained per minute (flow retention rate). Naturally, speed impediment is a significant and critical factor to be aware of as well. The speed and flexibility of alerting, reporting, forensic depth, and diagnostics all play a strategic role but will be hampered when confronted with scalability limitations. Observing the behavior when impacted by high-flow-variance or sudden-bursts and considering the number of devices and interfaces can enable you to appreciate the absolute significance of scalability in producing actionable insights and analytics.  Not to mention the ability to retain short-term and historical collections, which provide vital trackback information, would be nonexistent. To provide the necessary visibility to accomplish the ever-growing number of tasks analysts and engineers must deal with daily along with resolving issues to completion, a Network Monitoring System (NMS) must have the ability to scale in all its levels of consumption and retention.

How Should Monitoring Solutions Scale?

A Flow-Based network monitoring software needs to scale in its collection of data in five ways:

Ingestion Capability – Also referred to as Collection, means the number of flows that can be consumed by a single collector. This is a feat that most monitoring solutions are able to accomplish, unfortunately, it is also the one they pride themselves on. It is an important ability but is only the first step of several crucial capabilities that will determine the quality of insights and intelligence of a monitoring system. Ingestion is only the ability to take in data, it does not mean “retention”, and therefore could do very little on its own.

Digestion Capability – Also referred to as Retention, means the number of flow records that can be retained by a single collector. The most overlooked and difficult step in the network monitoring world. Digestion / Flow retention rates are particularly critical to quantify as they dictate the level of granularity that allows a flow-based NMS to deliver the visibility required to achieve quality Predictive AI Baselining, Anomaly Detection, Network Forensics, Root Cause Analysis, Billing Substantiation, Peering Analysis, and Data Retention compliance. Without retaining data, you cannot inspect it beyond the surface level, losing the value of network or cloud visibility.

Multitasking Processes– Pertains to the multitasking strength of a solution and its ability to scale and spread a load of collection processes across multiple CPUs on a single server.  This seems like an obvious approach to the collection but many systems have taken a linear serial approach to handle and ingest multiple streams of flow data that don’t allow their technologies to scale when new flow generating devices, interfaces, or endpoints are added forcing you to deploy multiple instances of a solution which becomes ineffective and expensive.

Clustered Collection – Refers to the ability of a flow-based solution to run a single data warehouse that takes its input from a cluster of collectors as a single unit as a means to load balance. In a large environment, you mostly have very large equipment that sends massive amounts of data to collectors. In order to handle all that data, you must distribute the load amongst a number of collectors in a cluster to multiple machines that make sense of it instead of a single machine that will be overloaded. This ability enables organizations to scale up in data use instead of dropping it as they attempt to collect it.

Hierarchical Correlation – The purpose of Hierarchical correlation is to take information from multiple databases and aggregate them into a single Super SIEM. With the need to consume and retain huge amounts of data, comes the need to manage and oversee that data in an intelligent way. Hierarchical correlation is designed to enable parallel analytics across distributed data warehouses to aggregate their results. In the field of network monitoring, getting overwhelmed with data to the point where you cannot find what you need is a as useful as being given all the books in the world and asked a single question that is answered in only one.

Network traffic visibility is considerably improved by reducing network blindspots and providing qualified sources and reasons of communications that impair business continuity.The capacity to capture flow at a finer level allows for new Predictive AI Baselining and Machine Learning application analysis and risk mitigation.

There are so many critical abilities that a network monitoring solution must enable its user, all are affected by whether or not the solution can scale.

Visibility is a range and not binary, you do not have or don’t have visibility, its whether you have enough to achieve your goals and keep your organization productive and safe.

How to Use a Network Behavior Analysis Tool to Your Advantage

How to Use a Network Behavior Analysis Tool to Your Advantage

Cybersecurity threats can come in many forms. They can easily slip through your network’s defenses if you let your guard down, even for a second. Protect your business by leveraging network behavior analysis (NBA). Implementing behavioral analysis tools helps organizations detect and stop suspicious activities within their networks before they happen and limit the damage if they do happen.

According to Accenture, improving network security is the top priority for most companies this 2021. In fact, the majority of them have increased their spending on network security by more than 25% in the past months. 

With that, here are some ways to use network behavior anomaly detection tools to your advantage.

1.     Leverage artificial intelligence

Nowadays, you can easily leverage artificial intelligence (AI) and machine learning (ML) in your network monitoring. In fact, various software systems utilize  AI diagnostics to enhance the detection of any anomalies within your network. Through its dynamic machine learning, it can quickly learn how to differentiate between normal and suspicious activities.

AI-powered NBA software can continuously adapt to new threats and discover outliers without much interference from you. This way, it can provide early warning on potential cyberattacks before they can get serious. This can include DDoS, Advanced Persistent Threats, and Anomalous traffic.

Hence, you should consider having AI diagnostics as one of your network behavior analysis magic quadrants.

2.           Take advantage of its automation

One of the biggest benefits of a network anomaly detection program is helping you save time and labor in detecting and resolving network issues. It is constantly watching your network, collecting data, and analyzing activities within it. It will then notify you and your network administrators of any threats or anomalies within your network.

Moreover, it can automatically mitigate some security threats from rogue applications to prevent sudden downtimes. It can also eliminate blind spots within your network security, fortifying your defenses and visibility. As a result, you or your administrators can qualify and detect network traffic passively.

3.           Utilize NBA data and analytics

As more businesses become data-driven, big data gains momentum. It can aid your marketing teams in designing better campaigns or your sales team in increasing your business’ revenues. And through network behavior analysis, you can deep-mine large volumes of data from day-to-day operations.

For security engineers, big data analytics has become an effective defense against network attacks and vulnerabilities. It can give them deeper visibility into increasingly complex and larger network systems. 

Today’s advanced analytics platforms are designed to handle and process larger volumes of data. Furthermore, these platforms can learn and evolve from such data, resulting in stronger network behavior analytics and local threat detection.

4.           Optimize network anomaly detection

A common issue with network monitoring solutions is their tendency to overburden network and security managers with false-positive readings. This is due to the lack of in-depth information to confirm the actual cause of a network issue. Hence, it is important to consistently optimize your network behavior analysis tool.

One way to do this is to use a flow-based analytics methodology for your network monitoring. You can do so with software like CySight, which uses artificial intelligence to analyze, segment, and learn from granular telemetry from your network infrastructure flows in real-time. It also enables you to configure and fine-tune your network behavior analysis for more accurate and in-depth monitoring.

5.           Integrate with other security solutions

Enhance your experience with your network behavior analytics tool by integrating it with your existing security solutions, such as prevention technology (IPS) systems, firewalls, and more. 

Through integrations, you can cross-analyze data between security tools for better visibility and more in-depth insights on your network safety. Having several security systems working together at once means one can detect or mitigate certain behaviors that are undetectable for the other. This also ensures you cover all the bases and leave no room for vulnerabilities in your network.

Improving network security

As your business strives towards total digital transformation, you need to start investing in your network security. Threats can come in many forms. And once it slips past your guard, it might just be too late.

Network behavior analysis can help fortify your network security. It constantly monitors your network and traffic and notifies you of any suspicious activities or changes. This way, you can immediately mitigate any potential issues before they can get out of hand. Check out CySight to know more about the benefits of network behavior analysis.

But, of course, a tool can only be as good as the people using it. Hence, you must make sure that you hire the right people for your network security team. Consider recruiting someone with an online software engineering masters to help you strengthen your network.

Ref: Accenture Report