by Yiming Gong
Enterprise networks are facing ever-increasing security threats from worms, port scans, DDoS, and network misuse, and thus effective monitoring approaches to quickly detect these activities are greatly needed. Firewall and intrusion detection systems (IDS) are the most common ways to detect these activities, but additional technology such as NetFlow can be a valuable enhancement.
1. NetFlow overview
NetFlow is a traffic profile monitoring technology developed by Darren Kerr and Barry Bruins at Cisco Systems, back in 1996. As a de facto industry standard, NetFlow describes the method for a router to export statistics about the routed socket pairs, and it's now a built-in feature for most Cisco routers as well as Juniper, Extreme and some other vendor's routers and switches.
When a network administrator enables the NetFlow export on a router interface, traffic statistics of packets received on that interface will be counted as "flow" and stored into a dynamic flow cache.
1.1 What is "flow"?
Flow is defined as a unidirectional sequence of packets (which means there will be two flows for each connection session, one from the server to client, one from the client to server) between two endpoints. A flow can be identified by seven key fields: source IP address, destination IP address, source port number, destination port number, protocol type, type of services, and the router input interface. Any time after receiving a packet, a router will look for these seven fields and then make a decision: if the packet belongs to an existent flow, traffic statistics of the corresponding flow will be increased, otherwise a new flow entry will be created.
According to Cisco, as new flow is continuously created, the expired flow records will be exported by means of a UDP packet to a user-specified monitoring station if one of the following conditions occurs. The conditions are:
A number of network hardware vendors have implemented their version of NetFlow, but Version5 is now the most common. For a V5 datagram, every single UDP datagram contains one flow header and thirty flow records. Every flow record is made up of several fields, which include: the source and destination IP address, next hop address, input and output interface number, number of packet in the flow, total bytes in the flow, the source and destination port, the protocol, ToS, source and destination AS number, and TCP flags (Cumulative OR of TCP flags).
On the collection station, a flow file analyzer is needed to process the exported flow data in real time. It can be either commercial software/hardware or a station created with open source tools.
1.2 NetFlow versus intrusion detection systems
Looking through a flow record, you will find that there is no packet payload information in the flow field. This is one of the major differences with NetFlow as compared to a traditional IDS. A flow record doesn't contain any high-layer information, it just contains traffic profiles. As a result, this makes NetFlow lose the ability to dig deeply into packets and do any packet analysis work, yet there is still enough information to make some valuable conclusions from the data. The advantage to this approach is its high speed. Paying no attention to packet payloads greatly reduces the processing overhead and makes NetFlow an extraordinarily good fit for busy, high-speed network environments. In addition, this characteristic makes NetFlow very useful in zero-day or "mutant attack" detection in cases where signature-based intrusion detection systems would fail.
Because flow data is coming directly from the router, a core element of any large network, NetFlow is capable of providing a unique view on the entire traffic of a network at the infrastructure level. It also proactive detection of network infrastructure security events.
If analyzed properly, NetFlow records will be very suitable for early worm and other abnormal network activity detection in large enterprise networks and service providers. In this paper, I will discuss some flow-based analysis methods on network security.
2. Flow-based analysis methods
2.1 Top N and Baseline
A baseline is a model describing what 'normal' network activity is according to some historical traffic pattern; all traffic that falls outside the scope of this established traffic pattern will be flagged as anomalous.
Trend and baseline analysis reports, commonly referred to as Top N and Baseline Analysis, is the most common and basic method of doing flow-based analysis. With this approach, attention is paid to flow records which have some "special high volume" characteristics, especially the value of those flow fields that deviate significantly from an established historical baseline.
Normally there are two ways to make use of Top N and Baseline methods: Top N sessions and Top N data.
2.1.1 Top N session
A Top N session means a single host produces an abnormally high volume of connection requests to a single destination or block of destinations, and the volume departs from the established baseline. The most likely reason for these activities are the presence of new worms, DoS/DDoS attacks, network scans or certain kinds of network abuses.
Normal clients connecting to the Internet should keep a relatively normal connection frequency to the outside. But if a host is infected with a worm, it will absolutely act different. It will always launch a large number of connection requests to the outside for its attempts to infect the next batch of victims, and as a result, the connection request numbers sent out will be significantly high.
For the same reason, when a lesser-skilled "script kiddie" is scanning a large block of addresses for certain vulnerable services, we will see especially high volume sessions sent out by that single IP address.
We can also use Top N session methods to detect many kinds of network abuses, such as checking the flow records for port 25 connection requests sent out by every single host in real time. In a given duration, for any host, if the statistics of port 25 requests are above a 'normal' value, it could be considered to be a spammer or someone infected with some kinds of email worm. It would be better for the Internet as a whole if service providers started using this technology and shut down the spammers upon detection.
2.1.2 Top N data
A second method of using Top N and Baseline methods is with Top N data. This can be defined as a consistantly large amount of network data transferred in a certain period of time between two network nodes or from a single node to a block of addresses.
The Top N hosts that transfer traffic data to or from the outside in an enterprise should be ranked into relatively fixed groups. If this pattern changes, and a new host suddenly appears in the Top N hosts matrix, an alert should be triggered.
Here is a example demonstrating Top N data methods that were used to track down a network security problem. One day, one of our customers reported a network bandwidth usage and congestion problem. We quickly enabled NetFlow on their upstream router's interface to collect egress traffic from their network, and had the flow data sent to our monitoring station. A few minutes later, a flow file was created. We analyzed the file with our flow-tools to generate a usage report for the top 20 hosts, sorted by octets. When the result displayed on console, we noticed that a host now siting in first place had abnormally high communication octets. A further examination of the flow records showed that the host sent out a huge number of requests to destination port 1434, so we now had the answer. The host was infected with the SQL slammer worm, and it almost ate up all their available bandwidth. After the customer patched the vulnerable machine, their network connection situation recovered.
2.2 Pattern Matching
Pattern matching is another method we can use to identify abnormal network activities when doing flow-based analysis. With this method, the flow records will be searched and those hosts associated with flow fields that seem "suspicious" based on our criteria will be flagged.
All the flow fields in a flow record can be used to do a pattern match, but the source and destination IP addresses, and the source and destination port numbers, are the most commonly used.
2.2.1 Port matching
Generally speaking, in order to launch an attack almost every attack should target a specific, functional port. For example, the SQL Slammer worm works on port 1434, the Netbus Trojan works on port 12345. Administrators can filter out all the flow records whereby the destination ports are equal to some specific ports, in order to find the corresponding attacks. This method is very easy to implement and can be used in most cases, although it may also produce false positives.
2.2.2 IP address matching
IP address matching is another method that can be used for security purposes with NetFlow analysis. There are several ways to make an IP address match, such as the following:
The IANA has reserved large blocks of Internet address space which should not be used for global routing. If we find any flow record containing IANA reserved addresses, an alert should be triggered.
An important fact that the administrator must realize when performing IANA reserved address matches is that he can't trace back the potential host within the flow record if it is using spoofed IP addresses. At this point another flow field, Ifindex, should be used. We could check the corresponding router Ifindex number in the flow records to find the actual router interface where the flow comes from.
I've experienced an interesting case in which one of our customer's NetFlow records were appearing strange; the flow records showed a large number of connections whereby the source ports were all 80, the source addresses were 127.0.0.1, and the TCP flags of these flow records were all RST/ACK.
The following is an output example of flow-tools:
Sif SrcIPaddress DIf DstIPaddress Pr SrcP DstP Pkts Octets StartTime EndTime Active B/Pk Ts Fl 0059 127.0.0.1 005b 184.108.40.206 06 50 4f3 1 40 0721.21:58:00.593 0721.21:58:00.593 0.000 40 00 14 0059 127.0.0.1 005b 220.127.116.11 06 50 6ef 1 40 0721.21:57:56.533 0721.21:57:56.533 0.000 40 00 14
Using the router Ifindex (Sif) field in the flow records, the router interface where these packets came from was quickly identified. I informed the administrator who was in charge of the network on that interface, and after a little while he responded to me with the answer: a PC in his domain was broken in and had a DoS program installed. The program was designed to launch TCP port 80 DoS attacks with spoofed source IP addresses against a security website located in Guangdong, China, but the DNS A record of the website had been changed to 127.0.0.1. Thus, the attack packets were received by the PC itself, then reset to the spoofed source IP addresses.
(B). Match a special IP or IP list
There are always some default rules for any enterprise or ISP when performing flow-based abnormal detection. Some of those rules are based on:
3.0 Concluding part one
This concludes the first of our two-part series. Check back in two week's time where we'll continue the discussion of NetFlow. In part two, we'll look at how to filter our flow results via TCP flags, we'll discuss some ICMP issues, and then discuss some of the various tools that exist to help implement and analyze our NetFlow solution. Stay tuned.
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