Our architecture relies on the observation that IRC hosts are grouped into channels by a channel name (for example, "F7", or "ubuntu" might be channel names), and that an evil channel is an IRC channel with a majority of hosts performing TCP SYN scanning.
The front-end data collector gathers three kinds of list tuples that are useful for benign IRC, botnet detection, and scanner detection. The tuples consist of two kinds of IRC tuples and the TCP syn tuple. The probe gathers these tuples over its thirty second sampling period. All tuples are then sent to the back-end for further processing. In the probe, the entire campus TCP syn tuple set is filtered into a smaller subset which informally consists of hosts observed sending anomalous amounts of TCP syns. This syn tuple subset is called the "worm set" and is typically orders of magnitude smaller than the entire set of IP sources found in the campus TCP syn set.
The TCP syn scanner list tuple has the following simplified form:
(IP source address, SYNS, SYNACKS, FINSSENT, FINSBACK, RESETS, PKTSSENT, PKTSBACK)
The logical key in this tuple is an IP source address. SYNS, FINS (all kinds), and RESETS are counts of TCP control packets. SYNS are counts of SYN packets sent from the IP source, and SYNACKS are a subset of only those SYNS sent with the ACK flag set. FINS sent both ways are counted. RESETS are counted when sent back to the IP source. The PKTSSENT counts the total packets sent by the IP source. PKTSBACK counts the total pkts returned to the IP source. Other fields exist but are not relevant to this paper. This information is useful for determining what kind of scanning is occurring and often gives a rough network-based indication of the kind of exploit in use.
We define a metric which we call the TCP work weight. The work weight is easy to compute and is computed by the probe per IP source as follows:
It is expressed as a percent. The rough idea is that we take the count of TCP control packets (SYN's plus SYNACKs sent, FIN's sent and RESETS) and divide that count by the total number of TCP packets (). Obviously 100% here is a bad sign and implies a true anomaly of some sort. Such a value is typically associated with a scanner or worm although some forms of P2P (and email servers) may have high work weights for shorter periods of time. The IRC module in the probe uses the TCP list as an underlying "tool", and extracts the TCP work weight from it for any IRC host.
We should point out that we have over two years worth of experience with the work weight at this point. We have learned that high work weights with hosts are caused by three possible causes including 1. scanners (typically syn scanners), 2. clients lacking a server for some reason or 3. P2P hosts (usually Gnutella is the application) IP peers. In general scanners are the most common reason for a high work weight. We also know that that the work weight clusters into either high values or low values (say 0..30%). Attackers fall into the higher range. P2P clients on average fall into the lower range.
Typically the average over many samples is of interest. However in the case of IRC we decided to simply take the maximum work weight seen over all the thirty second samples for a day. This is because an otherwise normal host may be ordered remotely to do scanning for a short period of the day. One host by itself in an IRC channel with a high work weight may not be anomalous. However if a channel has ten hosts out of twelve with high work weights suspicion is justified. As a result, work weights associated with IRC channels in our summarization reports are maximum weights seen across all the samples in a daily report.
There are two IRC lists, called the channel list and the node list. The channel list has the following tuple structure:
(CHANNAME, HITS, JOINS, PRIVMSGS, NOIPS, IP_LIST}The channel name is the case-insensitive IRC channel name extracted from JOIN and PRIVMSG IRC messages by the IRC scanner. The probe's scanner is hand-crafted C code that looks at the first 256 bytes of the L7 payload for TCP messages only and extracts IRC tokens for the four kinds of messages of interest. HITS is the total count of JOINS and PRIVMSGS, JOINS and PRIVMSGS are counts of that particular kind of message. NOIPS is the number of IP addresses in the IP_LIST, which follows the tuple. Thus a channel tuple gives a key (the channel name) with a few message count statistics and a list of IRC hosts in the channel expressed as IP addresses.
The node list gives per IP statistics for any IP address in any IRC channel. Informally a channel may be viewed as a directory, and a host may be viewed as a directory entry (although a host may actually be in more than one channel). The node list has the following tuple structure (not all counters shown):
(IPSRC, TOTALMSG, JOINS, PINGS, PONGS, PRIVMSGS, CHANNELS, SERVERHITS, WW}The key per tuple is an IP source address. Various message statistics are given including JOIN, PING, PONG, and PRIVMSG counts. The number of observed per host channels is supplied. SERVERHITS indicates the number of messages sent to/from a host. Thus this counter indicates whether a host is acting as an IRC server. The WW (work weight) as mentioned previously is derived from the TCP syn module.
One additional IRC statistic is gathered by the front-end which consists of total counts of the four kinds of IRC messages seen by the probe during the sample period. (This tuple is displayed by the back-end as an RRDTOOL-based graph - due to space limitations we cannot show such a graph in the paper). It shows that IRC is basically a slow phenomenon with only a few messages per second, even though our campus may have 5000 IP hosts active during a day. As a result our IRC evil channel analysis is based on a slower time scale, hours and days.
The IRC tuples are passed to the backend for report generation. The backend program produces an hourly text report (updated on the hour) which is called ircreport_today.txt. This file is available on the web for analysis. Data in this report is broken up into three major sections including global counts, channel statistics, and per host statistics. Channel statistics and per host statistical sections are further broken up into various sub-reports where data is typically sorted by some key statistic.
We can distinguish the following IRC report sub-sections:
For purposes of illustration in table we look at one benign example which comes from the per channel host statistics section. Counts given for our example were taken from twelve hours of data (since midnight) and are typical for a small IRC chat group. 1
In our example , a channel named "ubuntu" has four hosts in it. Three are local and the server (S) is remote. Total message counts (of the 4 kinds parsed) and JOIN, PING, PONG, and PRIVMSG counts are given. Maxchans is the number of channels seen during the period for that host, and maxworm is the maximum work weight seen. We do not believe this channel based on the above data is "evil".
Now let us see how this data may be correlated to plainly point out anomalous IRC-based botnet behavior.