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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

Big Data – A Global Approach To Local Threat Detection

From helping prevent loss of life in the event of a natural disaster, to aiding marketing teams in designing more targeted strategies to reach new customers, big data seems to be the chief talking point amongst a broad and diverse circle of professionals.

For Security Engineers, big data analytcs is proving to be an effective defense against evolving network intrusions thanks to the delivery of near real-time insights based on high volumes of diverse network data. This is largely thanks to technological advances that have resulted in the capacity to transmit, capture, store and analyze swathes of data through high-powered and relatively low-cost computing systems.

In this blog, we’ll take a look at how big data is bringing deeper visibility to security teams as environments increase in complexity and our reliance on pervading network systems intensifies.

Big data analysis is providing answers to the data deluge dilemma

Large environments generate gigabytes of raw user, application and device metrics by the minute, leaving security teams stranded in a deluge of data. Placing them further on the back foot is the need to sift through this data, which involves considerable resources that at best only provide a retrospective view on security breaches.

Big data offers a solution to the issue of “too much data too fast” through the rapid analysis of swathes of disparate metrics through advanced and evolving analytical platforms. The result is actionable security intelligence, based on comprehensive datasets, presented in an easy-to-consume format that not only provides historic views of network events, but enables security teams to better anticipate threats as they evolve.

In addition, big data’s ability to facilitate more accurate predictions on future events is a strong motivating factor for the adoption of the discipline within the context of information security.

Leveraging big data to build the secure networks of tomorrow

As new technologies arrive on the scene, they introduce businesses to new opportunities – and vulnerabilities. However, the application of Predictive AI Baselining analytics to network security in the context of the evolving network is helping to build the secure, stable and predictable networks of tomorrow. Detecting modern, more advanced threats requires big data capabilities from incumbent intrusion prevention and detection (IDS\IPS) solutions to distinguish normal traffic from potential threats.

By contextualizing diverse sets of data, Security Engineers can more effectively detect stealthily designed threats that traditional monitoring methodologies often fail to pick up. For example, Advanced Persistent Threats (APT) are notorious for their ability to go undetected by masking themselves as day-to-day network traffic. These low visibility attacks can occur over long periods of time and on separate devices, making them difficult to detect since no discernible patterns arise from their activities through the lens of traditional monitoring systems.

Big data Predictive AI Baselining analytics lifts the veil on threats that operate under the radar of traditional signature and log-based security solutions by contextualizing traffic and giving NOCs a deeper understanding of the data that traverses the wire.

Gartner states that, “Big data Predictive AI Baselining analytics enables enterprises to combine and correlate external and internal information to see a bigger picture of threats against their enterprises.”  It also eliminates the siloed approach to security monitoring by converging network traffic and organizing it in a central data repository for analysis; resulting in much needed granularity for effective intrusion detection, prevention and security forensics.

In addition, Predictive AI Baselining analytics eliminates barriers to internal collaborations between Network, Security and Performance Engineers by further contextualizing network data that traditionally acted as separate pieces of a very large puzzle.

So is big data Predictive AI Baselining analytics the future of network monitoring?

In a way, NOC teams have been using big data long before the discipline went mainstream. Large networks have always produced high volumes of data at high speeds – only now, that influx has intensified exponentially.

Thankfully, with the rapid evolution of computing power at relatively low cost, the possibilities of what our data can tell us about our networks are becoming more apparent.

The timing couldn’t have been more appropriate since traditional perimeter-based IDS\IPS no longer meet the demands of modern networks that span vast geographical areas with multiple entry points.

In the age of cloud, mobility, ubiquitous Internet and the ever-expanding enterprise environment, big data capabilities will and should become an intrinsic part of virtually every security apparatus.

8 Keys to Understanding NetFlow for Network Security, Performance & Overall IT Health

Balancing Granularity Against Network Security Forensics

With the pace at which the social, mobile, analytics and cloud (SMAC) stack is evolving, IT departments must quickly adopt their security monitoring and prevention strategies to match the ever-changing networking landscape. By the same token, network monitoring solutions (NMS) developers must balance a tightrope of their own in terms of providing the detail and visibility their users need, without a cost to network performance. But much of security forensics depends on the ability to drill down into both live and historic data to identify how intrusions and attacks occur. This leads to the question: what is the right balance between collecting enough data to gain the front foot in network security management, and ensuring performance isn’t compromised in the process?

Effectively identifying trends will largely depend on the data you collect

Trend and pattern data tell Security Operations Center (SOC) staff much about their environments by allowing them to connect the dots in terms of how systems may have become compromised. However, collecting large portions of historic data requires the capacity to house it – something that can quickly become problematic for IT Departments. Netflow data analysis acts as a powerful counterweight to the problem of processing and storing chunks of data, since it collects compressed header information that is far less resource-intensive than entire packets or investigating entire device log files, for example. Also, log files are often hackers’ first victims by way of deletion or corruption as a means to disguise attacks or intrusions. With NetFlow Auditor’s ability to collect vast quantities of uncompromised transaction data without exhausting device resources, SOCs are able to perform detailed analyses on flow information that could reveal security issues such as data leaks that occur over time. Taking into account that Netflow security monitoring can easily be configured on most devices, and pervasive security monitoring becomes relatively easy to configure in large environments.

Netflow security monitoring can give SOCs real-time security metrics

Netflow, when retained at high granularity, can facilitate seamless detection of traffic anomalies as they occur and when coupled with smart network behavior anomaly detection (NBAD), can alert engineers when data traverses the wire in an abnormal way – allowing for both quick detection and containment of compromised devices or entire segments. Network intrusions are typically detected when data traverses the environment in an unusual way and compromised devices experience spikes in multiple network telemetry metrics. As malicious software attempts to siphon information from systems, the resultant increase in out-of-the-norm activity will trigger warnings that can bring SOC teams in the loop of what is happening. IdeaData’s NetFlow Auditor employs machine learning that continuously compares multi-metric baselines against current network activity and quickly picks up on anomalies overlooked by other flow solutions, even before they constitute a system-wide threat. This type of behavioral analysis of network traffic places security teams on the front foot in the ongoing battle against malicious attacks on their systems.

Network metrics are being generated on a big data scale

Few things can undermine a network’s performance and risk more than a monitoring solution that strains to provide anticipated visibility. However, considering the increasing complexity of distributed connected assets and the ways and speed in which people and IoT devices are being plugged into networks today, pervasive and detailed monitoring is absolutely crucial. Take the bring your own device (BYOD) phenomenon and the shift to the cloud, for example. Networking and security teams need visibility into where, when, and how mobile phones, tablets, smart watches, and IoT devices are going on and offline and how to better manage the flow of data to and from user devices. Mobile devices increasingly run their own versions of business applications and with BYOD cultures somewhat undermining IT’s ability to dictate the type of software allowed to run on personal devices, the need to monitor traffic flow from such devices – from both a security and a performance perspective – becomes clear.

General Netflow performance analytics tools are capable of informing NOC teams about how large IP traffic flows between devices, with basic usage statistics on a device or segment level. However, when network metrics are generated on a big data scale, traffic anomalies that require SOC investigation get lost in leaky bucket sorting algorithms of basic tools. Detecting the real underlying reasons for traffic degradation or identifying risky communications such as Ransomware, DDoS, slowDoS, peer-to-peer (p2p), the dark web (ToR), and having complete historical visibility to trackback undesirable applications become absolutely critical, but far less difficult, with NetFlow Auditor’s ability to easily provide information on all of the traffic that traverses the environment.

NetFlow security monitoring evolves alongside technology organically

Thanks to Netflow and the unique design and multi-metric approach that IdeaData has implemented, as systems evolve at an increasing rate, it doesn’t mean you need to re-invent your security apparatus every six months or so. NetFlow Auditor’s ubiquity, reliability, and flexibility give NOC and SOC teams deep visibility minus the administrative overheads in getting it up and running along with collecting and benefiting from big flow data’s deep insights. You can even fine-tune your monitoring to give you the right granularity you need to keep your systems safe, secure, and predictable. This results in fewer network blind spots that often act as the Achilles Heel of the modern security and network experts.

On the other end of the scale, Netflow analyzers – in their varying feature sets – give NOCs some basic ability to collect, analyze, and detect from within-the-top bandwidth metrics which some engineers may still believe is the most pertinent to their needs. Once you’ve decided on the data you need today whilst keeping an eye on what you need tomorrow, it’s now time to choose the collector that does the job best.

8 Keys to Understanding NetFlow for Network Security, Performance & Overall IT Health

Deploying NetFlow as a Countermeasure to Threats like CNB

Few would debate legendary martial artist Chuck Norris’ ability to take out any opponent with a quick combination of lightning-fast punches and kicks. Norris, after all, is legendary for his showdowns with the best of fighters and being the last man standing in some of the most brutal and memorable fight scenes. It’s no surprise, then, that hackers named one of their most dubious botnet attacks after “tough guy” Norris, which wreaked havoc on internet routers worldwide. The “Chuck Norris” botnet, or CNB, was strategically designed to target poorly configured Linux MIPS systems, network devices such as routers, CCTV cameras, switches, Wifi modems, etc. In a study on CNB, the University of Masaryk in the Czech Republic, examined the attack’s inner workings and demonstrated how it employed Netflow as a countermeasure to actively detect and incapacitate the threat.

Lets look at what gave CNB its ability to infiltrate key networking assets and how, through flow-based monitoring, proactive detection made it possible to thwart the threat and others like it.

What made the Chuck Norris attack so potentially devastating?

What made the CNB attack so menacing was its ability to access all network traffic by infiltrating routers, switches and other networking hardware. This allowed it to go undetected for long periods, whereby it was capable of spreading through networks fairly quickly. As Botnet attacks “settle in”, they start issuing commands and take control of compromised devices, known as “bots”, that act as launch pads for Denial of Service (DoS) attacks, illegal SMTP relays, theft of information, etc.

Deploying Netflow as a countermeasure to threats like CNB

In the case of the CNB attack, Netflow collection data revealed how it infiltrated devices on TELNET and SSH ports, DNS Spoofs and web browser vulnerabilities, enabling Security teams to track its distribution on servers to avoid further propagation. Netflow’s deep visibility into network traffic gave Security teams the forensics they needed to effectively detect and incapacitate CNB.

Analysts are better positioned to mitigate risk to the network and its data through flow-based security forensics applied at the granular level coupled with dynamic behavioral and reputation feeds. Only with sufficient granularity and historic visibility can the risk of an anomaly be better diagnosed and mitigated. Doing so helps staff identify breaches that occur in real-time, as well as data leaks that take place over a prolonged period.

Flow-based monitoring solutions can collect vast amounts of security, performance and other data directly from networking infrastructure, giving Network Operations Centers (NOCs) a more comprehensive view of the environment and events as they occur. In addition, certain flow collectors are themselves resilient against cyber attacks such as DDoS. NetFlow technology isn’t only lightweight in terms of resource demands on switches and routers, but also highly fault-tolerant and limits exposure to flow floods including collection tuning, self-maintaining collection tuning rules and other self-healing capabilities.

As a trusted source of deep network insights built on big data analysis capabilities, Netflow provides NOCs with an end-to-end security and performance monitoring and management solution. For more information on Netflow as a performance and security solution for large-scale environments, download our free Guide to Understanding Netflow.

Cutting-edge and innovative technologies like CySight delivers the deep end-to-end network visibility and security context required assisting in speedily impeding harmful attacks.

Performance Monitoring & Security Forensics: The 1-2 Punch for Network and IT Infrastructure Visibility

3 Ways Anomaly Detection Enhances Network Monitoring

With the increasing abstraction of IT services beyond the traditional server room computing environments have evolved to be more efficient and also far more complex. Virtualization, mobile device technology, hosted infrastructure, Internet ubiquity and a host of other technologies are redefining the IT landscape.

From a cybersecurity standpoint, the question is how to best to manage the growing complexity of environments and changes in network behavior with every introduction of new technology.

In this blog, we’ll take a look at how anomaly detection-based systems are adding an invaluable weapon to Security Analysts’ arsenal in the battle against known – and unknown – security risks that threaten the stability of today’s complex enterprise environments.

Put your network traffic behavior into perspective

By continually analyzing traffic patterns at various intersections and time frames, performance and security baselines can be established, against which potential malicious activity is monitored and managed. But with large swathes of data traversing the average enterprise environment at any given moment, detecting abnormal network behavior can be difficult.

Through filtering techniques and algorithms based on live and historical data analysis, anomaly detection systems are capable of detecting even the most subtly crafted malicious software that may pose as normal network behavior. Also, anomaly-based systems employ machine-learning capabilities to learn about new traffic as it is introduced and provide greater context to how data traverses the wire, thus increasing its ability to identify security threats as they are introduced.

Netflow is a popular tool used in the collection of network traffic for building accurate performance and cybersecurity baselines with which to establish normal network activity patterns from potentially alarming network behavior.

Anomaly detection places Security Analysts on the front foot

An anomaly is defined as an action or event that is outside of the norm. But when a definition of what is normal is absent, loopholes can easily be exploited. This is often the case with signature-based detection systems that rely on a database of pre-determined virus signatures that are based on known threats. In the event of a new and yet unknown security threat, signature-based systems are only as effective as their ability to respond to, analyze and neutralize such new threats.

Since signatures do work well against known attacks, they are by no means paralyzed against defending your network. Signature-based systems lack the flexibility of anomaly-based systems in the sense that they are incapable of detecting new threats. This is one of the reasons signature-based systems are typically complemented by some iteration of a flow based anomaly detection system.

Anomaly based systems are designed to grow alongside your network

The chief strength behind anomaly detection systems is that they allow Network Operation Centers (NOCs) to adapt their security apparatus according to the demands of the day. With threats growing in number and sophistication, detection systems that can discover, learn about and provide preventative methodologies  are the ideal tools with which to combat the cybersecurity threats of tomorrow. NetFlow Anomaly detection with automated diagnostics does exactly this by employing machine learning techniques to network threat detection and in so doing, automating much of the detection aspect of security management while allowing Security Analysts to focus on the prevention aspect in their ongoing endeavors to secure their information and technological investments.

8 Keys to Understanding NetFlow for Network Security, Performance & Overall IT Health