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New IPS Architecture Uses Network Flow Data for Analysis

Can a stream of data intended for network performance monitoring be the basis of network security? One company says the answer is 'yes.'

Network control and management components communicate with one another through flows — information generated and collected in data's passage through the routers, switches, and other network components scattered throughout the network. Now, a company is putting flow information to a new use: protecting the network.

Netography has launched the open beta of a service it's calling Distributed IPS (Intrusion Prevention Service). The service uses multiple forms of flow data, including Sflow, NetFlow, and VPC flow, to analyze and act on network activity. "Switches, routers, and others are flow services," says Barrett Lyon, Netography's CEO. He explains that the flow data such items have generated traditionally has been telemetry data used for activities such as bandwidth management. "When we looked at it, you saw the you could use the information in there for other things," Lyon says.

Flow Begins
The first flow format was NetFlow, introduced by Cisco in the mid-1990s to allow network administrators to analyze traffic sources and destinations, along with performance conditions and congestion causes. Roughly a decade later, the technology entered the IETF standards process. The standards-based flow is known as Internet Protocol Flow Information Export (IPFIX). IPFIX is used by a number of different network infrastructure vendors.

There are other flow services on the market, many of them proprietary services supported by a single vendor. One notable exception is Sflow, which is supported by more than two dozen vendors, including several — such as Cisco — that also have their own, proprietary flow formats.

Distributed IPS can be used to act on network flow information in several ways. "There are four ways you can do stuff with flow data," says Lyon. "As we analyze it, we can trigger against different anomalies. That can go into an API that DevOps developers can develop actions around," he says, checking off the first two. "You can subscribe to your blacklist feed via BGP or BGP flowspec, or subscribe to one of the other blacklists," and then compare network traffic sources and destinations to the entries on those lists, Lyon continues.

Resolution Questions
Distributed IPS is able to detect distributed denial-of-service attacks, botnets, data extraction, login attempts, and other illicit network activity. The flows are collected and analyzed by Netography's cloud-based engine, with actions then taken through APIs available through Netography or developed by (or on behalf of) the customer.

There are observers, though, who have questions about whether the technology developed for network control can be adequate for security. "The challenge with NetFlow is that it is very low resolution," says Chris Morales, head of security analytics at Vectra, a company that uses artificial intelligence as the basis for its cybersecurity detection. "Think of trying to repaint the 'Mona Lisa' from a 1970s Polaroid photo. The resolution is too low to detect hidden threats with high efficacy."

Flow Actions
Lyon says that one of the bigger problems Distributed IPS is hoped to solve is more human-based than technological. "A lot of companies have developed their own orchestration layers similar to this, but many of those were developed by individual developers who then moved on, taking details of how the layer works with them," he says. That leaves the organization with a "black box" that can be difficult to update and impossible to patch or improve.

There seems little question that those black-box solutions can be less than perfect answers to security problems, and even off-the-shelf IPS products have wide differences in what they can provide for customers. “The usefulness, and thus the effectiveness, of current IDS/IPS technology varies greatly by vendor and rulesets," says Terence Jackson, CISO at Thycotic. "The idea of a Netography's Distributed IPS is intriguing, as it turns non-security appliances into a source of telemetry and threat intelligence. I think the idea could be a good one, but that depends on how the data will be used to work with current firewall technology on-premises and in the cloud.”

"The need for intrusion detection is absolutely real," says Morales. And he agrees that "NetFlow seems like an obvious fit due to its ability to scale for size." The question is whether the combination of flows that Distributed IPS ingests will provide the fine-grained resolution necessary for network security. The open beta period should help both Netography and its customers answer those very real questions.

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Curtis Franklin Jr. is Senior Editor at Dark Reading. In this role he focuses on product and technology coverage for the publication. In addition he works on audio and video programming for Dark Reading and contributes to activities at Interop ITX, Black Hat, INsecurity, and ... View Full Bio

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User Rank: Ninja
7/24/2019 | 2:58:10 AM
Interesting article, there was someone doing this in the past
10 years ago, there was a company called "Enterasys", the product they put together was called "NetSight Atlas". Extremenetworks bought the company; they provided similar capabilities as mentioned where they used algorithms and policies provided by the vendor to mitigate attacks without user intervention (literally thousands). There are other systems doing similar work called Extrahop but it is not an IPS system, it does not make a decision but it can help the user to make a more informed decision.

Extrahop Security Solution

When the actor tried to access the network, their session was moved to a honeypot or an area on the network that was external to the production environment. The application would pull information from varying switches, IDS, routers and firewalls; it would make a determination if the packets were suspect; the system would isolate that traffic from other parts of the network even if the switches were different (not all functionality but enabled certain protection mechanisms). The solution was light-years ahead of its time. They used all aspects of flow, network, log data; the system would create a baseline and identify anomalies based on traffic patterns, use and application characteristics. The system would effectively block or move individual ports like SMTP (25, 110), Web (80, 443), RPC (111), SMB/CIFS (135-139) to honeypots if the policies identified the session as being problematic. It would record, report and notify of any changes before the individual came into the office.

"The challenge with NetFlow is that it is very low resolution," says Chris Morales, head of security analytics at Vectra, a company that uses artificial intelligence as the basis for its cybersecurity detection. "Think of trying to repaint the 'Mona Lisa' from a 1970s Polaroid photo. The resolution is too low to detect hidden threats with high efficacy."

So I agree that this can done, but one of the concerns is based on target movement (the bullseye is constantly adjusting). So there needs to be intelligence built in the application because the different attacks can be manipulated or changed on the fly; also by tying together similar attacks, based on region and type, faster processing mechanisms can be employed to address similar problems (i.e. Polymorphic APTs or different methods used by nation states).

Algorithms are good in certain regards but since the variants or attacks are morphing using varying techniques, does the algorithm allow for a sliding scales (adjustment), that is why machine learning will be essential in evaluating attack vectors and their level of penetration (the next level of cybersecurity evolution).

Attack Vector Types

As a result, the tools should be able to unravel traffic flows (learn), user access (normal behavior), remote penetration techniques (scanning) and varying interrelated traffic patterns (correlational analysis, similar to big data), this will be a game changer. This is a major task, not to say that is cannot be done, but there are considerations outside of the algorithm that should be evaluated and improved as attacks improve.

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