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Cyberwar Defense using Predictive AI Baselining

The world is bracing for a worldwide cyberwar as a result of the current political events. Cyberattacks can be carried out by governments and hackers in an effort to destabilize economies and undermine democracy. Rather than launching cyberattacks, state-funded cyber warfare teams have been studying vulnerabilities for years.

An important transition has occurred, and it is the emergence of bad actors from unfriendly countries that must be taken seriously. The most heinous criminals in this new cyberwarfare campaign are no longer hiding. Experts now believe that a country could conduct more sophisticated cyberattacks on national and commercial networks. Many countries are capable of conducting cyberattacks against other countries, and all parties appear to be prepared for cyber clashes.

So, how would cyberwarfare play out, and how can organizations defend against them?

The first step is to presume that your network has been penetrated or will be compromised soon, and that several attack routes will be employed to disrupt business continuity or vital infrastructure.

Denial-of-service (DoS/DDoS) attacks are capable of spreading widespread panic by overloading network infrastructures and network assets, rendering them inoperable, whether they are servers, communication lines, or other critical technologies in a region.

In 2021, ransomware became the most popular criminal tactic, but country cyber warfare teams in 2022 are now keen to use it for first strike, propaganda and military fundraising. It is only a matter of time before it escalates. Ransomware tactics are used in politically motivated attacks to encrypt computers and render them inoperable. Despite using publicly accessible ransomware code, this is now considered weaponized malware because there is little to no possibility that a key to decode will be released. Ransomware assaults by financially motivated criminals have a different objective, which must be identified before causing financial and social damage, as detailed in a recent RANSOMWARE PAPER

To win the cyberwar on either cyber extortion or cyberwarfare attacks, you must first have complete 360-degree view into your network and deep transparency and intelligent context to detect dangers within your data.

Given what we already know and the fact that more is continually being discovered, it makes sense to evaluate our one-of-a-kind integrated Predictive AI Baselining and Cyber Detection solution.

YOU DON’T KNOW WHAT YOU DON’T KNOW!

AND IT’S WHAT WE DON’T SEE THAT POSES THE BIGGEST THREATS AND INVISIBLE DANGERS!

You may be surprised to learn that most tools lack the REAL Visibility that could have prevented attacks on a network and its local and cloud-connected assets. There are some serious shortcomings in the base designs of other flow solutions that result in their inability to scale in retention.

This is why smart analysts are realizing that Threat Intelligence and Flow Analytics today are all about having access to long-term granular intelligence. From a forensics perspective, you would appreciate that you can only analyze the data you retain, and with large and growing network and cloud data flows most tools (regardless of their marketing claims) actually cannot scale in retention and choose to drop records in lieu of what they believe is salient data.

Imputed outcome data leads to misleading results and missing data causes high risk and loss!

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So how exactly do you go about defending your organizations network and connected assets?

Our approach with CySight focuses on solving Cyber and Network Visibility using granular Collection and Retention with machine learning and A.I.

CySight was designed from the ground up with specialized metadata collection and retention techniques thereby solving the issues of archiving huge flow feeds in the smallest footprint and the highest granularity available in the marketplace.

Network issues are broad and diverse and can occur from many points of entry, both external and internal. The network may be used to download or host illicit materials and leak intellectual property.

Additionally, ransomware and other cyber-attacks continue to impact businesses. So you need both machine learning and End-Point threats to provide a complete view of risk.

The Idea of flow-based analytics is simple yet potentially the most powerful tool to find ransomware and other network and cloud issues. All the footprints of all communications are sent in the flow data and given the right tools you could retain all the evidence of an attack or infiltration or exfiltration.

However, not all flow analytic solutions are created equal and due to the inability to scale in retention the Netflow Ideal becomes unattainable. For a recently discovered Ransomware or Trojan, such as “Wannacry”, it is helpful to see if it’s been active in the past and when it started.

Another important aspect is having the context to be able to analyze all the related traffic to identify concurrent exfiltration of an organization’s Intellectual Property and to quantify and mediate the risk. Threat hunting for RANSOMWARE requires multi-focal analysis at a granular level that simply cannot be attained by sampling methods. It does little good to be alerted to a possible threat without having the detail to understand context and impact. The Hacker who has control of your system will likely install multiple back-doors on various interrelated systems so they can return when you are off guard.

CySight Turbocharges Flow and Cloud analytics for SecOps and NetOps

As with all CySight Predictive AI Baselining analytics and detection, you don’t have to do any heavy lifting. We do it all for you!

There is no need to create or maintain special groups with Ransomware or other endpoints of ill-repute. Every CySight instance is built to keep itself aware of new threats that are automatically downloaded in a secure pipe from our Threat Intelligence qualification engine that collects, collates, and categorizes threats from around the globe or from partner threat feeds.

CySight Identifies your systems conversing with Bad Actors and allows you to backtrack through historical data to see how long it’s been going on.

Summary

IdeaData’s CySight software is capable of the highest level of granularity, scalability, and flexibility available in the network and cloud flow metadata market and supports the broadest range of flow-capable vendors and flow logs.

CySight’s Predictive AI Baselining, Intelligent Visibility, Dropless Collection, automation, and machine intelligence reduce the heavy lifting in alerting, auditing, and discovering your network making threat intelligence, anomaly detection, forensics, compliance, performance analytics and IP accounting a breeze!

Let us help you today. Please schedule a time to meet https://calendly.com/cysight/

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 NetFlow Solves for Mandatory Data Retention Compliance

Compliance in IT is not new and laws regulating how organizations should manage their customer data exist such as: HIPPA, PCI, SCADA and Network transaction logging has begun to be required of business. Insurance companies are gearing up to qualify businesses by the information they retain to protect their services and customer information. Government and industry regulations and enforcement are becoming increasingly stringent.

Most recently many countries have begun to implement Mandatory Data Retention laws for telecom service providers.

Governments require a mandatory data retention scheme because more and more crime is moving online from the physical world and ISP‘s are keeping less data and retaining it for a shorter time. This negatively impacts the investigative capabilities of law enforcement and security agencies that need timely information to help save lives by early spotting lone-wolf terrorists or protect vulnerable members of society from abuse by sexual deviants, ransomware or other crimes online.

Although there is no doubt as to the value of mandatory data retention schemes they are not without justifiable privacy, human rights and expense concerns to implement.

It takes a lot of cash, time and skills that many ISP’s and companies simply cannot afford. Internet and managed service providers and large organizations must take proper precautions to remain in compliance. Heavy fines, license and certification issues and other penalties can result from non-compliance with mandatory data retention requirements.

According to the Australian Attorney-General’s Department, Australian telecommunications companies must keep a limited set of metadata for two years. Metadata is information about a communication (the who, when, where and how)—not the content or substance of a communication (the what).

A commentator from the Sydney morning herald qualified“…Security, intelligence and law enforcement access to metadata which overrides personal privacy is now in contention worldwide…” and speculated that with the introduction of Australian metadata laws that “…this country’s entire communications industry will be turned into a surveillance and monitoring arm of at least 21 agencies of executive government. …”.

In Australia many smaller ISP’s are fearful that failing to comply will send them out of business. Internet Australia’s Laurie Patton said, “It’s such a complicated and fundamentally flawed piece of legislation that there are hundreds of ISPs out there that are still struggling to understand what they’ve got to do”.

As for the anticipated costs, a survey sent to ISPs by telecommunications industry lobby group Communications Alliance found that  “There is a huge variance in estimates for the cost to business of implementing data retention – 58 per cent of ISPs say it will cost between $10,000 and $250,000; 24 per cent estimate it will cost over $250,000; 12 per cent think it will cost over $1,000,000; some estimates go as high as $10 million.”

An important cost to consider in compliance is the ease of data reporting when requested by government or corporate compliance teams to produce information for a specific ipv4 or ipv6 address. If the data is stored in a data-warehouse that is difficult to filter this may cause the service provider to incur penalties or be seen to be non-complying. Flexible filtering and automated reporting is therefore critical to produce the forensics required for the compliance in a timely and cost effective manner.

Although there are different laws governing different countries the main requirement of mandatory data retention laws at ISP’s is to maintain sufficient information at a granular level in order to assist governments in finding bad actors such as terrorists, corporate espionage, ransom-ware and pedophiles. In some countries this means that telcos are required to keep data of the IP addresses users connect to, for up to 10 weeks and in others just the totals of subscriber usage for each IP used for up to 2 years.

Although information remains local to each country and governed by relevant privacy laws, the benefits to law enforcement in the future will eventually provide the ability to have the visibility to track relayed data such as communications used by Tor Browsers, Onion routers and Freenet beyond their relay and exit nodes.

There is no doubt in my mind that with heightened states of security and increasing online crime there is a global need for governments to intervene with online surveillance to protect children from exploitation, reduce terrorism and to build defensible infrastructures whilst at the same time implementing data retention systems that have the inbuilt smarts to enable a balance between compliance and privacy rather than just a blanket catch all. There is already an available solution for the Internet communications component based on Netflow that assists ISP’s to quickly comply at a low cost whilst properly allowing data retention rules to be implemented to limit intruding on an individual’s privacy.

NetFlow solutions are cheap to deploy and are not required to be deployed at every interface such as a packet analyzer and can use the existing router, switch or firewall investment to provide continuous network monitoring across the enterprise, providing the service provider or organization with powerful tools for data retention compliance.

NetFlow technology if sufficiently scalable, granular and flexible can deliver on the visibility, accountability and measurability required for data retention because it can include features that:

  • Supply a real-time look at network and host-based activities down to the individual user and device;
  • Increase user accountability for introducing security risks that impact the entire network;
  • Track, measure and prioritize network risks to reduce Mean Time to Know (MTTK) and Mean Time to Repair or Resolve (MTTR);
  • Deliver the data IT staff needs to engage in in-depth forensic analysis related to security events and official requests;
  • Seamlessly extend network and security monitoring to virtual environments;
  • Assist IT departments in maintaining network up-time and performance, including mission critical applications and software necessary to business process integrity;
  • Assess and enhance the efficacy of traditional security controls already in place, including firewalls and intrusion detection systems;
  • Capture and archive flows for complete data retention compliance

Compared to other analysis solutions, NetFlow can fill in the gaps where other technologies cannot deliver. A well-architected NetFlow solution can provide a comprehensive landscape of tools to help business and service providers to achieve and maintain data retention compliance for a wide range of government and industry regulations.

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

Scalable NetFlow – 3 Key Questions to Ask Your NetFlow Vendor

Why is flows per second a flawed way to measure a netflow collector’s capability?

Flows-per-second is often considered the primary yardstick to measure the capability of a netflow analyzers flow capture (aka collection) rate.

This seems simple on its face. The more flows-per-second that a flow collector can consume, the more visibility it provides, right? Well, yes and no.

The Basics

NetFlow was originally conceived as a means to provide network professionals the data to make sense of the traffic on their network without having to resort to expensive per segment based packet sniffing tools.

A flow record contains at minimum the basic information pertaining to a transfer of data through a router, switch, firewall, packet tap or other network gateway. A typical flow record will contain at minimum: Source IP, Destination IP, Source Port, Destination Port, Protocol, Tos, Ingress Interface and Egress Interface. Flow records are exported to a flow collector where they are ingested and information orientated to the engineers purposes are displayed.

Measurement

Measurement has always been how the IT industry expresses power and competency. However, a formula used to reflect power and ability changes when a technology design undergoes a paradigm shift.

For example, when expressing how fast a computer is we used to measure the CPU clock speed. We believed that the higher the clock speed the more powerful the computer. However, when multi-core chips were introduced the CPU power and speed dropped but the CPU in fact became more powerful. The primary clock speed measurement indicator became secondary to the ability to multi-thread.

The flows-per-second yardstick is misleading as it incorrectly reflects the actual power and capability of a flow collector to capture and process flow data and it has become prone to marketing exaggeration.

Flow Capture Rate

Flow capture rate ability is difficult to measure and to quantify a products scalability. There are various factors that can dramatically impact the ability to collect flows and to retain sufficient flows to perform higher-end diagnostics.

Its important to look not just at flows-per-second but at the granularity retained per minute (flow retention rate), the speed and flexibility of alerting, reporting, forensic depth and diagnostics and the scalability when impacted by high-flow-variance, sudden-bursts, number of devices and interfaces, the speed of reporting over time, the ability to retain short-term and historical collections and the confluence of these factors as it pertains to scalability of the software as a whole.

Scalable NetFlow and flow retention rates are particularly critical to determine as appropriate granularity is needed to deliver the visibility required to perform Anomaly Detection, Network Forensics, Root Cause Analysis, Billing substantiation, Peering Analysis and Data retention compliance.

The higher the flows-per-second and the flow-variance the more challenging it becomes to achieve a high flow-retention-rate to archive and retain flow records in a data warehouse.

A vendors capability statement might reflect a high flows-per-second consumption ability but many flow software tools have retention rate limitations by design.

It can mean that irrespective of achieving a high flow collection rate the netflow analyzer might only be capable of physically archiving 500 flows per minute. Furthermore, these flows are usually the result of sorting the flow data by top bytes to identify Top 10bandwidth abusers. Netflow products of this kind can be easily identified because they often tend to offer benefits orientated primarily to identifying bandwidth abuse or network performance monitoring.

Identifying bandwidth abusers is of course a very important benefit of a netflow analyzer. However, it has a marginal benefit today where a large amount of the abuse and risk is caused by many small flows.

These small flows usually fall beneath the radar screen of many netflow analysis products.  Many abuses like DDoS, p2p, botnets and hacker or insider data exfiltration continue to occur and can at minimum impact the networking equipment and user experience. Lack of ability to quantify and understand small flows creates great risk leaving organizations exposed.

Scalability

This inability to scale in short-term or historical analysis severely impacts a flow monitoring products ability to collect and retain critical information required in todays world where copious data has created severe network blind spots.

To qualify if a tool is really suitable for the purpose, you need to know more about the flows-per-second collection formula being provided by the vendor and some deeper investigation should be carried out to qualify the claims.

 

With this in mind here are 3 key questions to ask your NetFlow vendor to understand what their collection scalability claims really mean:

  1. How many flows can be collected per second?

  • Qualify if the flows per second rate provided is a burst rate or a sustained rate.
  • Ask how the collection and retention rates might be affected if the flows have high-flow variance (e.g. a DDoS attack).
  • How is the collection, archiving and reporting impacted when flow variance is increased by adding many devices and interfaces and distinct IPv4/IPv6 conversations and test what degradation in speed can you expect after it has been recording for some time.
  • Ask how the collection and retention rates might change if adding additional fields or measurements to the flow template (e.g. MPLS, MAC Address, URL, Latency)
  • How many flow records can be retained per minute?

  • Ask how the actual number of records inserted into the data warehouse per minute can be verified for short-term and historical collection.
  • Ask what happens to the flows that were not retained.
  • Ask what the flow retention logic is. (e.g. Top Bytes, First N)
  • What information granularity is retained for both short-term and historically?
    • Does the datas time granularity degrade as the data ages e.g. 1 day data retained per minute, 2 days retained per hour 5 days retained per quarter
    • Can you control the granularity and if so for how long?

 

Remember – Rate of collection does not translate to information retention.

Do you know whats really stored in the software’s database? After all you can only analyze what has been retained (either in memory or on disk) and it is that information retention granularity that provides a flow products benefits.

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

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

NetFlow for Usage-Based Billing and Peering Analysis

Usagebased billing refers to the methods of calculating and passing back the costs of running a network to the consumers of data that occur through the network. Both Internet Service Providers (ISP) and Corporations have a need for Usage-based billing with different billing models.

NetFlow is the ideal technology for usage-based billing because it allows for the capture of all transactional information pertaining to the usage and some smart NetFlow technologies already exist to assist in the counting, allocation, and substantiation of data usage.

Advances in telecommunication technology have enabled ISPs to offer more convenient, streamlined billing options to customers based on bandwidth usage.

One billing model used most commonly by ISPs in the USA is known as the 95th percentile. The ISP filters the samples and disregards the highest 5% in order to establish the bill amount. This is an advantage to data consumers who have bursts of traffic because they’re not financially penalized for exceeding a traffic threshold for brief periods of time. The solution measures traffic employing a five-minute granularity standard typically over the course of a month.

The disadvantage of the 95th percentile model is that its not sustainable business model as data continues to become a utility like electricity.

A second approach is a utility-based metered billing model that involves retaining a tally of all bytes consumed by a customer with some knowledge of data path to allow for premium or free traffic plans.

Metered Internet usage is used in countries like Australia and most recently Canada who have nationally moved away from a 95th percentile model. This approach is also very popular in corporations whose business units share common network infrastructure and who are unwilling to accept “per user” cost, but rather a real consumption-based cost.

Benefits of usage-based billing are:

  • Improved transparency about the cost of services;
  • Costs feedback to the originator;
  • Raised cost sensitivity;
  • Good basis for active cost management;
  • The basis for Internal and external benchmarking;
  • Clear substantiation to increase bandwidth costs;
  • Shared infrastructure costs can also be based on consumption;
  • Network performance improvements.

For corporations, usage-based billing enables the IT department to become a shared service and viewed as a profit center rather than a cost center. It can become viewed as something that’s a benefit and a catalyst for business growth rather than a necessary but expensive line item in the budget.

For ISPs in the USA, there is no doubt that utility-based costs per byte model will continually be contentious as video and TV over Internet usage increases. In other regions, new business models that include packaging of video over “free zones” services have become popular meaning that the cost of premium content provision has fallen onto the content provider making utility billing viable in the USA.

NetFlow tools can include methods for building billing reports and offer a variety of usage-based billing model calculations.

Some NetFlow tools even include an API to allow the chart-of-accounts to be retained and driven from traditional accounting systems using the NetFlow system to focus on the tallying. Grouping algorithms should be flexible within the solution to allow for grouping of all different variables such as interfaces, applications, Quality of Service (QoS), MAC Addresses, MPLS, and IP groups. For ISPs and large corporations Asynchronous Network Numbers (ASN) also allow for analysis of data-paths allowing sensible negotiations with Peering partners and Content partners.

Look out for more discussion on peering in an upcoming blog…

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

How to counter-punch botnets, viruses, ToR & more with Netflow [Pt 1]

You can’t secure what you can’t see and you don’t know what you don’t know.

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 their limitations as these devices are not designed for and cannot record and report on every transaction due to lack of granularity, scalability and historic data retention. Network devices like routers, switches, Wi-Fi or VMware servers also typically lack any sophisticated anti-virus software.

Presenting information in a manner that quickly enables security teams to act with simple views with deep contextual data supporting the summaries is the mark of a well constructed traffic analyzer ensuring teams are not bogged down by the detail unless required and even then allowing elegant means to extract forensics with simple but powerful visuals to enable quick contextual grasp and impact of a security event.

Using NetFlow Correlation to Detect intrusions  

Host Reputation is one of the best detection methods that can be used against Advanced Persistent Threats. There are many data sources to choose from and some are more comprehensive than others.

Today these blacklists are mostly IPv4 and Domain orientated designed to be used primarily by firewalls, network intrusion systems and antivirus software.

They can also be used in NetFlow systems very successfully as long as the selected flow technology can scale to support the thousands of known compromised end-points, the ability to frequently update the threat data and the ability to record the full detail of every compromised flow and subsequent conversations that communicate with the compromised systems to discover other related breaches that may have occurred or are occurring.

According to Mike Schiffman at Cisco,

“If a given IP address is known to be that of a spammer or a part of a botnet army it can be flagged in one of the ill repute databases … Since these databases are all keyed on IP address, NetFlow data can be correlated against them and subsequent malicious traffic patterns can be observed, blocked, or flagged for further action. This is NetFlow Correlation.“

The kind of data can we expect to find in the reputation databases are IP addresses that have known to be acting in some malicious or negative manner such as being seen by multiple global honeypots. Some have been identified to be part of a well-known botnet such as Palevo or Zeus whilst other IP’s are known to have been distributing Malware or Trojans. Many kinds of lists can be useful to correlate such as known ToR end points or Relays that have become particularly risky of late being a common means to introduce RansomWare and should certainly not be seen conversing to any host within a corporate, government or other sensitive environment.

Using a tool like CySight’s advanced End-Point Threat Detection allows NetFlow data to be correlated against hundreds of thousands of IP addresses of questionable reputation including ToR exits and relays in real-time with comprehensive historical forensics that can be deployed in a massively parallel architecture.

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

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.

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

5 Perks of Network Performance Management

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.

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

5 Benefits of NetFlow Performance Monitoring

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).

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