Network Intelligence Archives

How to Improve Cyber Security with Advanced Netflow Network Forensics

Most organizations today deploy network security tools that are built to perform limited prevention – traditionally “blocking and tackling” at the edge of a network using a firewall or by installing security software on every system.

This is only one third of a security solution, and has become the least effective measure.

The growing complexity of the IT infrastructure is the major challenge faced by existing network security tools. The major forces impacting current network security tools are the rising level of sophistication of cybercrimes, growing compliance and regulatory mandates, expanding virtualization of servers and the constant need for visibility compounded by ever-increasing data volumes. Larger networks involve enormous amounts of data, into which the incident teams must have a high degree of visibility for analysis and reporting purposes.

An organization’s network and security teams are faced with increasing complexities, including network convergence, increased data and flow volumes, intensifying security threats, government compliance issues, rising costs and network performance demands.

With network visibility and traceability also top priorities, companies must look to security network forensics to gain insight and uncover issues. The speed with which an organization can identify, diagnose, analyze, and respond to an incident will limit the damage and lower the cost of recovery.

Analysts are better positioned to mitigate risk to the network and its data through security focused network forensics applied at the granular level. Only with sufficient granularity and historic visibility and tools that are able to machine learn from the network Big Data can the risk of an anomaly be properly diagnosed and mitigated.

Doing so helps staff identify breaches that occur in real-time, as well as Insider threats and data leaks that take place over a prolonged period. Insider threats are one of the most difficult to detect and are missed by most security tools.

Many network and security professionals assume that they can simply analyze data captured using their standard security devices like firewalls and intrusion detection systems, however they quickly discover limitations as these devices are not designed for and cannot record and report on every transaction due to lack of deep visibility, scalability and historic data retention making old fashioned network forensic reporting expensive and impractical.

NetFlow is an analytics software technology that enables IT departments to accurately audit network data and host-level activity. It enhances network security and performance making it easy to identify suspicious user behaviors to protect your entire infrastructure.

A well-designed NetFlow forensic tool should include powerful features that can allow for:

Micro-level data recording to assist in identification of real-time breaches and data leaks;
Event notifications and alerts for network administrators when irregular traffic movements are detected;
Tools that highlight trends and baselines, so IT staff can provision services accordingly;
Tools that learn normal behavior, so Network Security staff can quickly detect and mitigate threats;
Capture highly granular traffic over time to enable deep visibility across the entire network infrastructure;
24-7 automation, flexible reporting processes to deliver usable business intelligence and security forensics specifically for those analytics that can take a long time to produce.

Forensic analysts require both high-level and detailed visibility through aggregating, division and drilldown algorithms such as:

Deviation / Outlier analysis
Bi-directional analysis
Cross section analysis
Top X/Y analysis
Dissemination analysis
Custom Group analysis
Baselining analysis
Percentile analysis
QoS analysis
Packet Size analysis
Count analysis
Latency and RTT analysis

Further when integrated with a visual analytics process it will enable additional insights to the forensic professional when analyzing subsets of the flow data surrounding an event.

In some ways it needs to act as a log analyzer, security information and event management (SIEM) and a network behavior anomaly and threat detector all rolled into one.

The ultimate goal is to deploy a multi-faceted flow-analytics solution that can compliment your business by providing extreme visibility and eliminating network blindspots, both in your physical infrastructure and in the cloud, automatically detecting and diagnosing your entire network for anomalous traffic and improving your mean time to detect and repair.

5 Perks of Network Performance Management

July 4, 2021 Rafi Sabel NetFlow / Network Intelligence / Network Monitoring / Peering Analytics / Performance Analytics / Traffic Accounting NetFlow / Network Forensics

Network performance management is something that virtually every business needs, but not something that every business is actively doing, or even aware of. And why should they?

While understanding the technical side of things is best left to the IT department, understanding the benefits of a properly managed network is something that will help get the business managers on board, especially when good performance management solutions might be a cost that hadn’t been considered. So what are the benefits?

1. Avoiding downtime – Downtime across an entire network is going to be rare, but downtime in small areas of the network are possible if it gets overloaded. Downtime of any kind is just not something that business can tolerate, for a few reasons:

it leaves that area of the network unmonitored, which is a serious security issue
shared files won’t be accessible, nor will they be updating as users save the files. This will lead to multiple versions of the same file, and quite a few headaches when the network is accessible again
downtime that affects customers is even worse, and can result in lost revenue or negative customer experiences

2. Network speed – This is one of the most important and easily quantified aspects of managing netflow. It will affect every user on the network constantly, and anything that slows down users means either more work hours or delays. Obviously, neither of these is a good problem to have. Whether it’s uploading a file, sending a file to a coworker, or sending a file to a client; speed is of paramount importance.

3. Scalability – Almost every business wants to grow, and nowhere is that more true than the tech sector. As the business grows, the network will have to grow with it to support more employees and clients. By managing the performance of the network, it is very easy to see when or where it is being stretched too thin or overwhelmed. As performance degrades, it’s very easy to set thresholds that show when the network need upgraded or enlarged.

4. Security – Arguably the most important aspect of network management, even though it might not be thought of as a performance aspect. An unsecured network is worse than a useless network, and data breaches can ruin a company. So how does this play into performance management?

By monitoring netflow performance, it’s easy to see where the most resources are being used. Many security attacks drain resources, so if there are resource spikes in unusual areas it can point to a security flaw. With proper software, these issues can be not only monitored, but also recorded and corrected.

5. Usability – Unfortunately, not all employees have a working knowledge of how networks operate. In fact, as many in IT support will attest, most employees aren’t tech savvy. However, most employees will need to use the network as part of their daily work. This conflict is why usability is so important. The easiest way to minimize training costs with any network management program is to ensure that it is as user-friendly as possible.

The fanciest, most impressive network performance management system isn’t worth anything if no one knows how to use and optimize it properly. Even if the IT department has no issues with it, the reports and general information should be as easy to decipher as possible.

Is your network as optimized as it could be? Are you able to monitor the network’s performance and flow, or do network forensics to determine where issues are? Don’t try to tackle all of this on your own, contact us and let us help you support your business with the best network monitoring for your specific needs.

5 Benefits of NetFlow Performance Monitoring

June 28, 2021 Rafi Sabel NetFlow / Network Intelligence / Network Monitoring / Performance Analytics / Traffic Accounting NetFlow / Network Performance Management

NetFlow can provide the visibility that reduces the risks to business continuity associated with poor performance, downtime and security threats. As organizations continue to rely more and more on computing power to run their business, transparency of business applications across the network and interactions with external systems and users is critical and NetFlow monitoring simplifies detection, diagnosis, and resolution of network issues.

Traditional SNMP tools are too limited in their collection capability and the data extracted is very coarse and does not provide sufficient visibility. Packet capture tools can extract high granularity from network traffic but are dedicated to a port mirror or a per segment capture and are built for specialized short term captures rather than long historical analyses as well as packet based Predictive AI Baselining analytics tools traditionally do not provide the ability to perform forensic analysis of various sub-sections or aggregated views and therefore also suffer from a lack of visibility and context.

By analyzing NetFlow data, a network engineer can discover the root cause of congestion and find out the users, applications and protocols that are causing interfaces to exceed utilization. They can determine the Type or Class of Service (QoS) and group with application mapping such as Network Based Application Recognition (NBAR) to identify the type of traffic such as when peer-to-peer traffic tries to hide itself by using web services and so on.

NetFlow allows granular and accurate traffic measurements as well as high-level aggregated traffic collection that can assist in identifying excessive bandwidth utilization or unexpected application traffic. NetFlow enables networks to perform IP traffic flow analyses without deploying external probes, making traffic Predictive AI Baselining analytics cheap to deploy even on large network environments.

Understanding the benefits of a properly managed network might be a cost that hasn’t been considered by business managers. So what are the benefits of NetFlow performance monitoring?

Avoiding Downtime

Service outages can cause simple frustrations such as issues when saving opened files to a suddenly inaccessible server or cause delays to business process can impact revenue. Downtime that affects customers can result in negative customer experiences and lost revenue.

Network Speed

Slow network links cause severe user frustration that impacts productivity and causes delays.

Scalability

As the business grows, the network needs to grow with it to support more computing processes, employees and clients. As performance degrades, it can be easy to set thresholds that show when, where and why the network is exceeding it’s capability. By having historical performance of the network, it is very easy to see when or where it is being stretched too thin or overwhelmed and to easily substantiate upgrades.

Security

Security Predictive AI Baselining analytics is arguably the most important aspect of network management, even though it might not be thought of as a performance aspect. By monitoring NetFlow performance, it’s easy to see where the most resources are being used. Many security attacks drain resources, so if there are resource spikes in unusual areas it can point to a security flaw. With advanced NetFlow diagnostics software, these issues can be not only monitored, but also recorded and corrected.

Peering

NetFlow Predictive AI Baselining analytics allows organizations to embrace progressive IT trends to analyze peering paths, or virtualized paths such as Multiprotocol Label Switching (MPLS), Virtual Local Area Networks (VLAN), make use of IPv6, Next Hops, BGP, MAC Addresses or link internal IP’s to Network Address Translated (NAT) IP addresses.

MPLS creates new challenges when it comes to network performance monitoring and security with MPLS as communication can occur directly without passing through an Intrusion Detection System (IDS). Organizations have two options to address the issue: Implement a sensor or probe at each site, which is costly and fraught with management hassles; or turn on NetFlow at each MPLS site and send MPLS flows to a NetFlow collector that becomes a very cost-effective option because it can make use of existing network infrastructure.

With NetFlow, network performance issues are easily resolved because engineers are given in-depth visibility that enables troubleshooting – all the way down the network to a specific user. The solution starts by presenting the interfaces being used.

An IT engineer can click on the interfaces to determine the root cause of a usage spike, as well as the actual conversation and host pairs that are dominating bandwidth and the associated user information. Then, all it takes is a quick phone call to the end-user, instructing them to shut down resource-hogging activities, as they’re impairing network performance.

IT departments are tasked with responding to end-user complaints, which often relate to network sluggishness. However, many times an end-user’s experience has more to do with an application or server failing to respond within the expected time period that can point to a Latency, Quality of Service (QoS) or server issue. Traffic Latency can be analyzed if supported in the NetFlow version. Statistics such as Round Trip Time (RTT) and Server Response Time (SRT) can be diagnosed to identify whether a network problem is due to an application or service experiencing slow response times.

Baselines of RTT and SRT can be learned to determine whether response time is consistent or is spiking over a time period. By observing the Latency over time alarms can be generated when Latency increases above the normal threshold.

TCP Congestion flags can also serve as an early warning system where Latency is unavailable.

NetFlow application performance monitoring provides the context around an event or anomaly within a host to quickly identify the root cause of issues and improve Mean Time To Know (MTTK) and Mean Time To Repair (MTTR).

NetFlow for Advanced Threat Detection

June 24, 2021 Rafi Sabel Anomaly Detection / Big Data / BYOD / Cyber Security / Data Retention Compliance / DDoS / Internet of Things / IoT / NetFlow / Network Forensics / Network Intelligence / Network Security / Predictive AI Baselining / Predictive Analytics / Ransomware / Threat Detection / Threat Intelligence / ToR NetFlow / Threat Detection / Threat Intelligence

These networks are vital assets to the business and require absolute protection against unauthorized access, malicious programs, and degradation of performance of the network. It is no longer enough to only use Anti-Virus applications.

By the time malware is detected and those signatures added to the antiviral definitions, access is obtained and havoc wreaked or the malware is buried itself inside the network and is obtaining data and passwords for later exploitation.

An article by Drew Robb in eSecurity Planet on September 3, 2015 (https://www.esecurityplanet.com/network-security/advanced-threat-detection-buying-guide-1.html) cited the Verizon 2015 Data Breach Investigations Report where 70 respondents reported over 80,000 security incidents which led to more than 2000 serious breaches in one year.

The report noted that phishing is commonly used to gain access and the malware then accumulates passwords and account numbers and learns the security defenses before launching an attack. A telling remark was made, “It is abundantly clear that traditional security solutions are increasingly ineffectual and that vendor assurances are often empty promises,” said Charles King, an analyst at Pund-IT. “Passive security practices like setting and maintaining defensive security perimeters simply don’t work against highly aggressive and adaptable threat sources, including criminal organizations and rogue states.”

So what can businesses do to protect themselves? How can they be proactive in addition to the passive perimeter defenses?

The very first line of defense is better education of users. In one test, an e-mail message was sent to the users, purportedly from the IT department, asking for their passwords in order to “upgrade security.” While 52 people asked the IT department if this was a real request, 110 mailed their passwords right back. In their attempts to be productive, over half of the recipients of phishing e-mails responded within an hour!

Another method of advanced threat protection is NetFlow Monitoring.

IT department and Managed service providers (MSP’s), can use monitoring capabilities to detect, prevent, and report adverse effects on the network.

Traffic monitoring, for example, watches the flow of information and data traversing critical nodes and network links. Without using intrusive probes, this information helps decipher how applications are using the network and which ones are becoming bandwidth hogs. These are then investigated further to determine what is causing the problem and how best to manage the issue. Just adding more bandwidth is not the answer!

IT departments review this data to investigate which personnel are the power users of which applications, when the peak traffic times are and why, and similar information in addition to flagging and diving in-depth to review anomalies that indicate a potential problem.

If there are critical applications or services that the clients rely on for key account revenue streams, IT can provide real-time monitoring and display of the health of the networks supporting those applications and services. It is this ability to observe, analyze, and report on the network health and patterns of usage that provides the ability to make better decisions at the speed of business that CIO’s crave.

CySight excels at network Predictive AI Baselining analytics solutions. It scales to collect, analyze, and report on Netflow datastreams of over one million flows/second. Their team of specialists have prepped, installed, and deployed over 1000 CySight performance monitoring solutions, including over 50 Fortune 1000 companies and some of the largest ISP/Telco’s in the world. A global leader and recognized by winning awards for Security and Business Intelligence at the World Congress of IT, CySight is also welcomed by Cisco as a Technology Development Partner.

Balancing Granularity Against Network Security Forensics

June 20, 2021 Tomare Curran Anomaly Detection / Big Data / BYOD / Cyber Security / Data Retention Compliance / DDoS / Internet of Things / IoT / NetFlow / Network Forensics / Network Intelligence / Network Security / Ransomware / Threat Detection / Threat Intelligence / ToR Anomaly Detection / Cyber Security / NetFlow / Network Forensics / Network Security / Threat Detection / Threat Intelligence

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 CySight’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. CySight 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 CySight’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 CySight 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. CySight’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.

What is NetFlow & How Can Organizations Leverage It?

June 16, 2021 Rafi Sabel Anomaly Detection / Big Data / BYOD / Cyber Security / Data Retention Compliance / DDoS / Internet of Things / IoT / NetFlow / Network Forensics / Network Intelligence / Network Monitoring / Network Security / Network Usage Billing / Peering Analytics / Performance Analytics / Ransomware / Threat Detection / Threat Intelligence / ToR / Traffic Accounting NetFlow

NetFlow is a feature originally introduced on Cisco devices (but now generally available on many vendor devices) which provides the ability for an organization to monitor and collect IP network traffic entering or exiting an interface.

Through analysis of the data provided by NetFlow, a network administrator is able to detect things such as the source and destination of traffic, class of service, and the causes of congestion on the network.

NetFlow is designed to be utilized either from the software built into a router/switch or from external probes.

The purpose of NetFlow is to provide an organization with information about network traffic flow, both into and out of the device, by analyzing the first packet of a flow and using that packet as the standard for the rest of the flow. It has two variants which are designed to allow for more flexibility when it comes to implementing NetFlow on a network.

NetFlow was originally developed by Cisco around 1990 as a packet switching technology for Cisco routers and implemented in IOS 11.x.

The concept was that instead of having to inspect each packet in a “flow”, the device need only to inspect the first packet and create a “NetFlow switching record” or alternatively named “route cache record”.

After that that record was created, further packets in the same flow would not need to be inspected; they could just be forwarded based on the determination from the first packet. While this idea was forward thinking, it had many drawbacks which made it unsuitable for larger internet backbone routers.

In the end, Cisco abandoned that form of traffic routing in favor of “Cisco Express Forwarding”.

However, Cisco (and others) realized that by collecting and storing / forwarding that “flow data” they could offer insight into the traffic that was traversing the device interfaces.

At the time, the only way to see any information about what IP addresses or application ports were “inside” the traffic was to deploy packet sniffing systems which would sit inline (or connected to SPAN/Mirror) ports and “sniff” the traffic. This can be an expensive and sometimes difficult solution to deploy.

Instead, by exporting the NetFlow data to an application which could store / process / display the information, network managers could now see many of the key meta-data aspects of traffic without having to deploy the “sniffer” probes.

Routers and switches which are NetFlow-capable are able to collect the IP traffic statistics at all interfaces on which NetFlow is enabled. This information is then exported as NetFlow records to a NetFlow collector, which is typically a server doing the traffic analysis.

There are two main NetFlow variants: Security Event Logging and Standalone Probe-Based Monitoring.

Security Event Logging was introduced on the Cisco ASA 5580 products and utilizes NetFlow v9 fields and templates. It delivers security telemetry in high performance environments and offers the same level of detail in logged events as syslog.

Standalone Probe-Based Monitoring is an alternative to flow collection from routers and switches and uses NetFlow probes, allowing NetFlow to overcome some of the limitations of router-based monitoring. Dedicated probes allow for easier implementation of NetFlow monitoring, but probes must be placed at each link to be observed and probes will not report separate input and output as a router will.

An organization or company may implement NetFlow by utilizing a NetFlow-capable device. However, they may wish to use one of the variants for a more flexible experience.

By using NetFlow, an organization will have insight into the traffic on its network, which may be used to find sources of congestion and improve network traffic flow so that the network is utilized to its full capability.

Seven Reasons To Analyze Network Traffic With NetFlow

June 6, 2021 Rafi Sabel Anomaly Detection / BYOD / Cyber Security / Data Retention Compliance / DDoS / Internet of Things / IoT / NetFlow / Network Forensics / Network Intelligence / Network Monitoring / Network Security / Network Usage Billing / Peering Analytics / Performance Analytics / Ransomware / Threat Detection / Threat Intelligence / ToR / Traffic Accounting NetFlow / Network Traffic Analysis / Traffic Accounting

NetFlow allows you to keep an eye on traffic and transactions that occur on your network. NetFlow can detect unusual traffic, a request for a malicious destination or a download of a larger file. NetFlow analysis helps you see what users are doing, gives you an idea of how your bandwidth is used and can help you improve your network besides protecting you from a number of attacks.

There are many reasons to analyze network traffic with NetFlow, including making your system more efficient as well as keeping it safe. Here are some of the reasons behind many organizations adoption of NetFlow analysis:

Analyze all your network NetFlow allows you to keep track of all the connections occurring on your network, including the ones hidden by a rootkit. You can review all the ports and external hosts an IP address connected to within a specific period of time. You can also collect data to get an overview of how your network is used.

Track bandwidth use. You can use NetFlow to track bandwidth use and see reports on the average use of This can help you determine when spikes are likely to occur so that you can plan accordingly. Tracking bandwidth allows you to better understand traffic patterns and this information can be used to identify any unusual traffic patterns. You can also easily identify unusual surges caused by a user downloading a large file or by a DDoS attack.

Keep your network safe from DDoS attacks. These attacks target your network by overloading your servers with more traffic than they can handle. NetFlow can detect this type of unusual surge in traffic as well as identify the botnet that is controlling the attack and the infected computers following the botnet’s order and sending traffic to your network. You can easily block the botnet and the network of infected computers to prevent future attacks besides stopping the attack in progress.

Protect your network from malware. Even the safest network can still be exposed to malware via users connecting from home or via people bringing their mobile device to work. A bot present on a home computer or on a Smartphone could access your network but NetFlow will detect this type of abnormal traffic and with auto-mitigation tools automatically block it.

Optimize your cloud. By tracking bandwidth use, NetFlow can show you which applications slow down your cloud and give you an overview of how your cloud is used. You can also track performances to optimize your cloud and make sure your cloud service provider is offering a cloud solution that corresponds to what they advertised.

Monitor users. Everyone brings their own Smartphone to work nowadays and might use it for purposes other than work. Company data may be accessible by insiders who have legitimate access but have an inappropriate agenda downloading and sharing sensitive data with outside sources. You can keep track of how much bandwidth is used for data leakage or personal activities, such as using Facebook during work hours.

Data Retention Compliance. NetFlow can fill in the gaps where other technologies cannot deliver. A well-architected NetFlow solution can help business and service providers to achieve and maintain data retention compliance for a wide range of government and industry regulations.

NetFlow is an easy way to monitor your network and provides you with several advantages, including making your network safer and collecting the data you need to optimize it. Having access to a comprehensive overview of your network from a single pane of glass makes monitoring your network easy and enables you to check what is going on with your network with a simple glance.

CySight solutions takes the extra step to make life far easier for the network and security professional with smart alerts, actionable network intelligence, scalability and automated diagnostics and mitigation for a complete technology package.

CySight can provide you with the right tools to analyze traffic, monitor your network, protect it and optimize it. Contact us to learn more about NetFlow and how you can get the most out of this amazing tool.

Deploying NetFlow as a Countermeasure to Threats like CNB

June 2, 2021 Rafi Sabel Anomaly Detection / Cyber Security / DDoS / NetFlow / Network Forensics / Network Intelligence / Network Security / Ransomware / Threat Detection / Threat Intelligence Anomaly Detection / Cyber Security / NetFlow / Network Security

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.

Why NetFlow is Perfect for Forensics and Compliance

May 25, 2021 Rafi Sabel Big Data / Cyber Security / Data Retention Compliance / NetFlow / Network Forensics / Network Intelligence / Network Security Data Retention Compliance / NetFlow / Network Forensics / Network Traffic Analysis

Netflow forensic investigations can produce the report evidence that can be used in court as it describes the movement of the traffic data even without necessarily describing its contents.

It’s therefore crucial that the Netflow solution deployed can scale in archival to allow full context of all the flow data and not just the top of the data or the data relating to one tools idea of a security event.

The issue with Forensics and flow data is that in order to achieve full compliance its necessary to retain a data warehouse that can eventuate in a huge amount of flow records.

These records, retained in the data warehouse may not seem important at the time of collection but become critical to uncover behavior that may have been occurring over a long period and to ascertain the damage of the traffic behavior. I am talking broadly here as there are so many different instances where the data suddenly becomes critically important and it’s hard to do it justice by explaining one or two case studies. Remember you don’t know what you don’t know but when you discover what you didn’t know you need to have the ability to quantify the loss or the risk of loss.

How much flow data is enough to retain to satisfy compliance?

From our experience it is usually between 3-24 months depending on the size of the environment and the legal compliance relating to data protection or data retention. For most corporates we would recommend 12 months as a best practice. Data retention in ISP land in some countries requires the ability to analyze traffic for up to 2 years. Fortunately disk today is cheap and flow is cost effective to deploy across the organization. There is more information about this in our Performance and Security eBook.

Once a security issue has been identified the flow database can be available to quantify exactly what IP’s accessed a system, the times the system was accessed as well as quantifying the impact on dependent systems that the host conversed with directly or indirectly on the network before and after the issue.

Trawling through huge collection of flow-data can be a lengthy task and its necessary to have the ability to run automated Predictive AI Baselining analytics and parallel Predictive AI Baselining analytics to gauge damage from a long term inside threat that could have been dribbling out your intellectual property slowly over a few months.

3 Ways Anomaly Detection Enhances Network Monitoring

May 20, 2021 Rafi Sabel Anomaly Detection / BYOD / Cyber Security / DDoS / Internet of Things / IoT / NetFlow / Network Forensics / Network Intelligence / Network Monitoring / Network Security / Performance Analytics / Predictive AI Baselining / Predictive Analytics / Traffic Accounting Anomaly Detection / Cyber Security / NetFlow / Network Forensics / Network Security / Threat Detection / Threat Intelligence

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.

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Effectively identifying trends will largely depend on the data you collect

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Network metrics are being generated on a big data scale

NetFlow security monitoring evolves alongside technology organically

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