How P J Networks Is Using Behavioral Analytics in Firewall Monitoring

Behavioral Analytics

Cybersecurity is probably more important than ever. So despite organizations having a plethora of firewalls to monitor, and reams of firewall logs generated each day, traditional methods are often inadequate in identifying advanced or targeted cyber attacks. Behavioral analytics comes in that scenario. So, in a way we are able to detect threats based on behaviour via network traffic. Behavioral analytics is the process of assembling a benchmark environment to learn normal network behavior and detect abnormal deviations.

How It Complements Firewall Monitoring

The signature-based detection systems are mostly used for the monitoring of traditional firewalls. These tools compare network traffic with known threat signatures, and alert on potential intrusions. This technique, though effective against known threats, lacks the competency to combat new and evolving threats. It looks for suspicious patterns in network traffic (like socks doing the triple-double while running a marathon) opposed to be sent to timeout when something doesn’t seem quite right according Identification. If you know what a network is supposed to act like normally, it makes for easier detection of something weird!

Behavioral Analytics in Practice

Example

Consider a case where an enterprise server, which usually serves 500 requests per hour, receives about 5,000 requests within that same amount of time. Even if the requests do not meet any known signatures, traditional firewalls may still fail to flag this as a threat. But behavioral analytics would identify this unusual surge and notify about the potential threat for follow-on investigation.

P J Networks’ Implementation

To strengthen the offering, P J Networks has introduced behavioral analytics into its firewall monitoring services. Here’s how we do it:

1. Establishing Baselines

The first step in our process is to determine what constitutes ‘normal’ for each network of the client. Using historical data, we can learn about normal traffic load and use-cases as well where users are connecting to the network.

2. Continuous Monitoring

We observe network activities in real-time after we have a baseline for normal behavior. We have a mechanism that keeps tabs on all the traffic coming in and going out from your devices to match it with overall established behavior.

3. Anomaly Detection

Violations or variations from the baseline are marked as possible hazards. These oddities can be anything from an uptick in data transfers, strange access times or something else.

4. Immediate Response

When our team detects an anomaly, it is notified instantly. We perform a detailed investigation to identify the threat and respond accordingly in order to save from risk.

5. Regular Updates

Cyber threat landscape is ever-changing. P J Networks updates its behavioral analytics model to address emerging threats as well as changes in network behavior.

Case Studies

Case Study 1: Insider Threat Detection

For example, a financial institution client we worked with saw their security incidents plummet after deployment. In a traditional sense, while it was outside regular business hours the employee accessed this data and had an insider threat on their hands. Our system detected this falsification helping the client to react and stop a potential data loss.

Case Study 2: Discovering a Piece of Malware

For another client, a healthcare provider, traditional firewalls were failing to catch widespread malware repeatedly attacking the organization. When they finally switched to P J Networks, we were blindsided by a one-in-a-million network pattern that only our behavioral analytics could have picked out as malware communication with command-and-control. The client was able to detect this early on and isolate it, removing the malware before any considerable damage could be done.

Case Study 3: Stopping Data Exfiltration

A manufacturing enterprise saw loss prevention-type value in what we do; it blocked data exfiltration. Unexplained network performance issues had been plaguing the team. We found that a significant amount of data was being downloaded to an external IP address by our system, which traditional firewalls missed. The company could then stop the illegal transfers (and hopefully save its sensitive data).

Conclusion

Firewall monitoring in particular has been totally revolutionized by behavioral analytics, which adds another level of security that many traditional methods cannot measure up to. Through observing the behaviors of network traffic, rather than restricting detection to only known signatures (or examples) of threats we can be more efficient in detecting and responding.

We take pride in providing next-level behavioral analytics through our firewall monitoring services here at P J Networks. Our solutions are designed to update and fasten your network with its full system protection, whether you want to rent firewalls or servers. You can trust the stability of even skilled cyber threats with continuous monitoring, anomaly detection, and automatic response capabilities.

Using behavioral analytics in your cybersecurity is more than just the advancement of technology and a tale to tell, it is proactively protecting that network from things you cannot see coming. Get in touch with P J Networks and leverage our next-generation security mechanisms that are customized to fit your business requirements. Please reach out to us and we will give you a free consultation on services that can be used to secure your network so all you have to do is run your business.