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How to Use a Network Behavior Analysis Tool to Your Advantage

August 22, 2021 Tomare Curran / / / / / / / / / / / / / /

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

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Big Data – A Global Approach To Local Threat Detection

July 27, 2021 Rafi Sabel / / / / / / / / / / / / / / / / / / / / / / / /

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.

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