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IMC '05 Paper    [IMC '05 Technical Program]

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Collaborating Against Common Enemies

Sachin Katti Balachander Krishnamurthy Dina Katabi
MIT AT&T Labs-Research MIT


This paper presents the first wide-scale study of correlated attacks, i.e., attacks mounted by the same source IP against different networks. Using a large dataset from $1700$ intrusion detection systems (IDSs), we show that correlated attacks are prevalent in the current Internet; 20% of all offending sources mount correlated attacks and they account for more than 40% of all the IDS alerts in our logs. We also reveal important characteristics of these attacks. Correlated attacks appear at different networks within a few minutes of each other, indicating the difficulty of warding off these attacks by occasional offline exchange of lists of malicious IP addresses. Furthermore, correlated attacks are highly targeted. The $1700$ IDSs can be divided into small groups with 4-6 members that do not change with time; IDSs in the same group experience a large number of correlated attacks, while IDSs in different groups see almost no correlated attacks. Our results have important implications on collaborative intrusion detection of common attackers. They show that collaborating IDSs need to exchange alert information in realtime. Further, exchanging alerts among the few fixed IDSs in the same correlation group achieves almost the same benefits as collaborating with all IDSs, while dramatically reducing the overhead.

1 Introduction

In this paper, we study correlated attacks, which we define as attacks mounted by the same source IP against different networks. Currently, about 30,000 new machines are compromised daily [25], and then used to launch attacks on other parts of the Internet. In many cases, the same machines are involved in multiple attacks against different networks [25]-i.e., correlated attacks. In addition to being an Internet phenomenon worthy of careful study, correlated attacks are important for collaborative intrusion detection. The intrusion detection system (IDS) at a network can exchange information about recent alerts and offending IPs with other IDSs. Future packets from suspicious source IPs can be flagged to be dropped or scrutinized. Such collaboration is most effective when it happens between networks experiencing correlated attacks.[*]

We present the first large scale empirical investigation of attack correlation in the Internet. We analyze logs from $1700$ IDS/firewalls deployed in US and Europe. Our data is rich; in addition to sanitized logs from DSHIELD [2] and multiple universities, it contains detailed attack logs from 40 IDSs maintained by a Tier-1 provider to protect its customer networks. The logs cover 1-3 months, and a big chunk of the IP address space. In contrast to prior work, which has focused on the design of collaborative intrusion detection systems [28,29,21,9,26,22], we address the following two questions:

Our study results in 4 major findings. (a) The extent of attack correlation: Correlated attacks are prevalent in the Internet; 20% of the offending IP sources attack multiple networks, and these common attackers are responsible for 40% of the total alerts in our dataset. Further, shared attackers attack different networks within a few minutes of each other, emphasizing the advantage of realtime IDS collaboration, as opposed to sharing attack logs offline.

Table 1: Definitions of terms used in the paper
Term Definition
Correlated Attacks Two attacks are correlated if they are mounted by the same source IP.
Alert An alarm raised by a sensor when it encounters a suspicious event, e.g. a packet or set of packets that contain a known exploit.
Correlated IDSs Two IDSs are said to be correlated if more than $10\%$ of their attacks are correlated.
Correlation Group of IDSs A set of IDSs whose attacks are highly correlated.
Correlation Vector of IDS $i$ is $\vec v_i= (v_{i1},
...,v_{ij},..)$, where $v_{ij}=1$ if $j \in$ correlation group of $i$, and otherwise $v_{ij}=0$.
Blacklist A list of suspicious IP addresses whose packets are dropped or given unfavorable treatment.

(b) Reducing collaboration overhead by exploiting correlation structure: We analyze the spatial structure of attack correlation. We discover that the 1700 IDSs in our dataset can be divided into small groups of 4-6 members (about 0.4% of the IDSs in our set); IDSs in the same group experience highly correlated attacks, whereas IDSs in different groups see uncorrelated attacks. Collaborating with only IDSs in the same correlation group achieves the same utility obtained from collaborating with all IDSs, while dramatically reducing the collaboration overhead.

The small correlation groups seem to arise from recent attack trends. In particular, victim sites in the same group may be on a single hit list, or might be natural targets of a particular exploit like the Santy worm which attacked popular phpBB discussion forums scoured from search engines. We examined the correlated attacks in each group for cases where full attack details are available. Indeed, each group seems to be characterized by a specific attack type, e.g., there are SMTP groups, NT groups, IIS groups. This indicates that targeted attacks create small correlation groups of sites that run particular software/services.

(c) Scalable Trust Establishment: Our measurements reveal that correlation groups are fairly stable and their membership persists for the duration of the dataset (1-3 months). Thus, each network needs to collaborate with only 4-6 fixed networks in its group. The small number of IDSs in a group and their persistent membership allows a network to check their credibility offline and establish trust using an out-of-band mechanism such as legal contracts or reputation.

A network still needs to learn who is in its correlation group. This service can be provided by a few trusted nonprofit organizations, like CERT [1] and DSHIELD [2], or commercial entities. They receive sanitized alert data, (containing only time and offending source IP), from participating networks, analyze it for attack correlation, and inform the participating networks about others in their correlation group. The process is scalable because correlation groups are persistent for long intervals (months) and do not need frequent updates. Indeed, DSHIELD already has the means to provide this service to its participant networks.

(d) The importance of picking the right collaborators: We provide rough estimates of the overhead and detection capability obtained via different choices of collaborating IDSs. We focus on collaboration to quickly blacklist malicious IP sources. Using a trace driven simulation, we compare the following schemes: (1) correlation-based collaboration (CBC), where each IDS collaborates with only IDSs in its correlation group; (2) random collaborators, where an IDS collaborates with the same number of IDSs in its correlation group but picks the identity of its collaborators randomly. (3) local detection with no collaboration; (4) collaboration with all IDSs in the dataset;

The results of our evaluation emphasize the importance of picking the right collaborators. Mainly:

Table 1 defines the terms used in this paper.

Table 2: Description of the 3 datasets
ISP dataset DSHIELD University datasets
# of IDSs 40 1657 3
Address space Class A 5 Class B, 45 Class C and 2 Class B, 1 Class C
several smaller networks
Period July 1 - August 30, 2004 Dec. 15, 2004 - Dec. 15, 2004 -
Dec. 15, 2004 - Jan. 15, 2005 Jan. 15, 2005 Jan. 15, 2005
Richness Detailed alerts, Dest. IP addresses Detailed alerts,
unanonymized anonymized unanonymized
Avg #alerts/day/IDS 40000 15000 30000

2 Dataset and Method

1 Dataset

Figure 1: Log records for the ISP dataset and the DSHIELD dataset. The ISP dataset also has packet headers for each log record. The DSHIELD dataset has the destination IP anonymized.
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Our dataset is both large and rich. We use logs collected at $1700$ different IDSs deployed in US and Europe. Our logs can be divided into $3$ distinct sets based on their origin: (1) $40$ IDSs on different networks in a Tier-1 ISP; (2) DSHIELD Logs; (3) University logs. The logs cover periods of 1-3 months. They span a relatively large fraction of IP address space. In addition to a /8 ISP space, the DSHIELD data contain logs from many /16 and /24 networks. This is the first studied dataset of its size that provides detailed alert information from deployed IDSs in the commercial Internet. Table 2 provides a summary description of the dataset. A detailed description is below.

(a) ISP Logs: We have logs from 40 IDSs deployed in a large ISP with a /8 address space. The IDS boxes protect different customer networks and span a large geographic area, but they are all administered by the ISP and hence have identical characteristics and signature sets. The signature set is large and diverse consisting of over 500 different alerts. The logs contain full unanonymized packet headers for all suspicious packets, as shown in Figure 1a. Hence unlike the DSHIELD data described below, we have access to the offending packet as well as the nature of the offense. The logs cover two separate periods: one period from July 1 to August 30, 2004 and the other from December 15, 2004 to January 15, 2005. The data exhibits a large amount of variation in the kind of attacks seen (over 100 different attack types) as well as the distribution of attacking IP addresses (over 100000 unique source addresses) and 40000 alerts/day/IDS.

(b) DSHIELD Logs: DSHIELD is a global repository set up as a research initiative as part of the SANS institute. Participating organizations provide IDS/firewall logs, which DSHIELD uses for detection and analysis of new vulnerabilities, and blacklist generation. Since the IDS systems which participate in DSHIELD employ widely varying software, DSHIELD uses a minimal record format for its logs and scrubs the high order 8 bits of the destination IP address, as shown in Figure 1b. The entities participating in DSHIELD vary in size from several Class B networks to smaller Class C networks and are distributed throughout the globe [28,2]. The logs are of substantial size with nearly 15000 alerts/day/IDS. We have collected DSHIELD logs from 1657 IDSs for the period from Dec. 15, 2004 to Jan. 15, 2005 corresponding to the ISP dataset.

(c) University Logs: Finally, we collect a set of logs from IDS/firewall systems deployed at $3$ universities U1, U2 and U3. Of these we have access to raw data complete with packet headers and nature of offense detected in U1. The second university U2 provided us with logs from running the Bro IDS [19], but with protected addresses anonymized. The signature set deployed is different and the alerts consist mostly of scans of IP addresses as well as port-scans. The third university U3 provided us with firewall logs which consisted of blocked connection attempts. The University logs generate 30000 alerts/day/IDS on the average.

A few limitations are worth mentioning. Except for the ISP logs, the other IDSs in the logs are largely independent. We do not have access to their configurations, and hence we do not know the signature sets they employ, or even the platforms they use. This means that some of the attack correlation may be hidden because of differences between IDS signature sets. Second, we do not have information about the nature or the business of the protected networks, and thus cannot tell whether these issues play a role in attack correlation.

2 Method

Before studying attack correlation, we clean the data from obvious false positives, and analyze it to find a meaningful definition of the term ``attack correlation''.

1 Filtering

IDS logs are prone to flooding with alerts, many of which are innocuous alarms. For example, the ISP and University data sets contain innocuous alarms triggered by misconfigurations, P2P applications like eDonkey, malformed HTTP packets etc. Many of these were already flagged as false positives by the security administrators of the ISP. Since these are not actual attacks, they do not help in detecting attack correlation among different sites. Hence we filter out known false positives from the ISP and universities logs. We consider all the remaining alerts to be parts of valid attacks. Of course we cannot do this for the DSHIELD dataset, since we do not know the nature of the alert.

2 Attack Durations

To carry out this study, we need to extract attacks from IDS logs. We consider a stream of suspicious packets from the same source to an IDS with an inter-arrival smaller than 10 minutes as an attack. Below we explain why a separation window of $10$ minutes is reasonable.

Figure 2: CDF of inter-arrival times of consecutive alerts from a source in minutes. The CDF is taken over the inter-arrival times. 95% of consecutive alerts from a source arrive within 10 minutes of each other, the rest are separated by several hours.
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To find a meaningful separation window, we plot a CDF of inter-arrival times of consecutive alerts from the same source at an IDS in Figure 2. The CDF shows that $90\%$ of the alerts from a source arrive within a minute of each other, these are likely to belong to the same attack event. The knee in the CDF happens at $10$ minutes, inter-arrival times larger than $10$ minutes are spread out to several hours. We pick $10$ minutes as the window because about 95% of the alerts from the same source arrive separated by less than 10 minutes and the other 5% have widely-spread interarrivals.

3 Defining Attack Correlation

How should one define attack correlation? Should all fields in the alerts received at different IDSs be the same, or is it enough to consider one or two fields? Furthermore, how long can the interval between the two attacks at two different IDSs be for them to be still considered correlated?

Attack correlation can be parameterized by the set of correlated header fields and the time window used to compute the correlation. We define two attacks to be correlated if they share the source IP address and start within 10 minutes of each other. Both choices are based on detailed analysis of the data that showed almost no sensitivity to including additional fields in the correlation beyond the source IP and using time windows larger than 10 minutes. Below we describe this analysis in detail.

Figure 3: CDF of the size of the correlation groups for different definitions of attack correlation for the ISP and U1 datasets. The CDFs are taken over the IDSs. They show that the correlation is insensitive to the additional information obtained from the alert type and port, and can be discovered based solely on source IP.
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(a) Picking the correlation fields: Defining attack correlation based on the destination IP address is not useful since attacks seen by a particular IDS will have their destinations in the local network. Also the source port is likely to be picked randomly and is not useful for defining attack correlation.

We consider the following definitions of correlated attacks: 1) source based, 2) source and the destination port combined, 3) source and alert type combined, 4) source, alert type, and destination port combined, 5) and source subnet based. We conduct this analysis for the ISP dataset and the U1 datasets, for which we have access to all these fields.

Since our main interest is to find who is correlated with whom, we consider how different attack correlation definitions affect the size of the correlation group of a IDS (see Table 1). Correlated groups are explained further in §3, but for the purposes of this analysis they are simply the set of IDSs with which a particular IDS shares correlated attacks.

Figure 3 plots the cumulative distribution functions (CDFs) of the size of the correlation group of an IDS. Different CDFs correspond to different correlation fields. The figure shows that, except for the CDF for source subnets, all the other CDFs are very close together. Classification based on the attacking source subnet results in slightly higher correlation, but the difference is not substantial. Further, classifying based on source subnet carries the danger of blacklisting an entire subnet resulting in innocent sources being blocked. Since including extra fields in the definition of correlation in addition to the source IP has no significant impact on the correlation CDF, we define attack correlation based solely on the similarity of the offending source IP.

The above leads to an interesting result: performing attack correlation analysis requires minimal information, namely attack time and offending source IP.

(b) Picking the maximum time window between correlated attacks: Unless stated differently, a $10$ minute window is used for determining correlated attacks at different IDSs. We tried different time windows in the $[5,30]$ minutes range. Windows less than $10$ minutes resulted in decreased attack correlation while there was not much difference for windows greater than $10$. Hence we picked the minimum window possible i.e., $10$ minutes. Thus, if two attacks at two IDSs start within $10$ minutes of each other, then they are considered correlated.

(c) Correlation threshold: We say that two IDSs are correlated if more than $10\%$ of their attacks are correlated. We justify the threshold below. We compute the CDF of correlation taken over all IDSs with non-empty groups (i.e., IDSs that are correlated with at least one other IDS). For 90% of the IDS, the correlation (percentage of correlated attacks w.r.t all attacks) was higher than $10\%$ ranging upto $57\%$. For the remaining $10\%$ of the IDS, the correlation was slightly higher than $0\%$. Such small values are due to a few attacks being shared and do not reflect any significant correlation between the two IDSs.

Figure 4: Prevalence of common attackers. Figure shows the CDFs of the average number of common attackers and local attackers per day per IDS. A common attacker is a source IP that is flagged as suspicious at two or more IDSs. 90% of the studied IDSs see more than 100 common attacking IPs per day. The average number of common attacking IPs at an IDS is about 1,500 while the maximum can be as large as 25,000.
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3 Extent of Attack Correlation

1 Do IDSs see common attackers?

A common attacker is an IP address that generates alerts at two or more IDSs. We compute the average number of common and uncommon attacking IP addresses for each IDS per day. Figure 4 compares the CDF of common attackers with the uncommon/local ones. The CDF is taken over all IDSs. The graphs show that on average an IDS sees $1500$ shared offending IPs per day, and $6000$ unshared offenders. Thus, about $20\%$ of the suspicious source IP addresses observed at an IDS are also seen at some other IDS in the dataset. These common source IP addresses account for $40\%$ of all alerts in the logs. Thus, correlated attacks happen quite often and constitute a substantial fraction of all attacks.

2 How many victims does a common attacker attack?

The previous section quantified how many source IP addresses at each IDS are common attackers, here we focus on the number of victims of a common attacker. Figure 5 plots the CDF of the number of IDSs targeted by a common attacker. The CDF is taken over all common attacker IPs. On the average, a common attacker appears at $10$ IDSs, which is about 0.6% of all IDSs in the dataset. The high average of $10$ victims seems to comply with recent trends in using botnets to mount multiple attacks against many target networks [25].

Figure 5: Figure shows the CDF of the number of different IDSs targeted by a common attacker. Common sources are detected at 10 different IDSs on the average, implying that such sources are employed to mount a large number of attacks at different victims.
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3 Time Between Correlated Attacks

Figure 6: Figure shows the CDF of the interarrival times of correlated attacks at different IDSs. More than 75% of the correlated attacks arrive within 10 minutes of each other. This emphasizes the need for realtime exchange of attack data.
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How long does it take a common attacker before he attacks the next network? If this time is long then the exchange of alert data can be offline, but if it is short then effective collaboration against common attackers requires realtime exchange of information. We compute interarrival times of attacks from the same source at multiple IDSs, i.e., the difference between when the first time the attacker is observed at different IDSs. Figure 6 shows the CDF of these interarrival times. More than $75\%$ of the time, a common attacker attacks the next IDS within $10$ minutes from the previous IDS. Attackers therefore mount multiple attacks within a span of a few minutes, suggesting that collaborative detection of such attackers has to be in realtime.

4 Attack Correlation Structure

Why is the structure of attack correlation important? Since correlation is prevalent, it would be beneficial for IDSs to collaborate to speedup the detection of common attackers. However, in §3.3, we have shown that common attackers attack their victim networks within a few minutes of each other. Thus, to effectively collaborate against common attackers, the IDSs need to exchange information in realtime. An IDS in our dataset generates on average $1500$ alerts/hour. Exchanging alerts in realtime with thousands of IDSs creates an unacceptable overhead. Thus, we are interested in finding how many collaborators each IDS needs to have in order to achieve the benefits of collaboration without incurring much overhead. To answer this question, we examine the spatial and temporal structures of attack correlation, i.e., how many IDSs are usually correlated with each other and how often does the set of IDSs a particular IDS is correlated with change over time?

1 Correlated IDSs

For the objective of detecting common attackers, an IDS benefits from exchanging alerts with only those IDSs whose attacks are correlated with its own. We call this set of IDSs its correlation group. If correlation groups are small, i.e., much smaller than all IDSs, then by focusing only on the IDSs in its correlation group, an IDS can achieve most of the benefits of the collaboration at little overhead.

We plot in Figure 7 the CDF of the number of IDSs with which an IDS is correlated (i.e., the size of its correlation group) for all $1700$ IDSs in our dataset. We consider two cases: simultaneous correlation, in which two attacks are correlated if they share the same source IP and happen within $10$ minutes of each other, and general correlation, in which two attacks are correlated if they share the source IP. The former helps detect distributed attacks, while the latter helps detect malicious sources which should be blacklisted. General correlation is by definition greater than simultaneous correlation. The figure shows that on average each IDS is correlated with $4-6$ other IDSs, i.e., less than $0.4\%$ of the total number of IDSs. Further, 96% of the IDSs are correlated with less than $10$ IDSs.

Note that the plots for simultaneous and general correlation are fairly similar. Though the average number of IDSs with which an IDS shares attacks increases to nearly $5$, the CDF does not change much. Again, this shows that when correlated attacks happen at different locations in the Internet, most likely they happen with a short period.

Figure 7: Cumulative Distribution of the number of IDSs with which any IDS exhibits correlation for all 3 datasets. Figure shows most IDSs are correlated with 4-6 (among 1700) IDSs with the average being slightly higher than 4.
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2 Persistence of IDS Correlation

Figure 8: Figure shows that the group of IDSs which experiences attacks correlated with attacks at a particular IDS does not change for the duration of our 3 datasets (about a month). The y-axis is the normalized difference in the correlation vector defined in Equation 1.
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We would like to examine how often the correlation group of an IDS changes. If the membership of the correlation group of an IDS is stable then each network can spend the time to identify its correlation group offline. Once the correlation group is identified, the actual exchange of alerts is done in realtime. On the other hand, if the members of an IDS' correlation group keep changing over short intervals, collaboration will be hard as it requires re-examining attack correlation and deciding in realtime whether to collaborate.

We need to define a measure of how a group of IDSs is changing. We assign the IDSs consecutive IDs. For each IDS $i$ in our dataset, we create a correlation vector $\vec v_i(n)$ whose length is equal to the total number of IDSs in the dataset. We set $v_{ij}(n)=1$ if IDS $i$ is correlated with IDS $j$, and $0$ otherwise based on the alerts they generate on day $n$. For example, $\vec v_i (16)=(0,1,1,0,1,0,...,0)$ means that IDS $i$ and IDSs 2,3, and 5 see correlated attacks on the 16th day in our dataset.

The difference vector for two days for a given IDS is the vector obtained by subtracting the corresponding correlation vectors for those days. For example, the difference $v_i(17) - v_i(0)$ indicates how the correlation group of IDS $i$ changes over a period of 17 days, starting on day $0$ in our logs.

We measure the persistence of attack correlation as a function of time using the following metric:

f_{m-n} = \frac {1}{N}\sum_{i} \frac{\vert\vert\vec v_i(m) - \vec v_i(n)\vert\vert}{\vert\vert\vec v_i(n)\vert\vert},
\end{displaymath} (1)

where $N=1700$ is the number of IDSs; $v_i$ is the correlation vector of IDS $i$; and $\vert\vert\vec v\vert\vert$ is the Euclidean norm of the vector. Thus, $f_{m-n}$ is the average change in the norm of the correlation vector between day $n$ and day $m$ where $m>n$, normalized by the size of that vector.

Figure 8 plots our measure of the difference in attack correlation $f_i$ as a function of time in days along with the standard deviation. It shows that, the correlation vector does not change significantly with time. In particular, on average the correlation vector changes by less than $0.025$ of its original value over a period that spans a whole month. The insignificant change shows that correlation happens consistently with the same group of IDSs and is persistent over time.

3 Robustness to Source Spoofing

Figure 9: Comparison of attack correlation among connection-oriented attacks and all attacks for the ISP dataset. The figure plots the CDF of the number of IDSs that experience correlated attacks to a particular IDS. The two CDFs are very close indicating that our results are robust against source spoofing.
\epsfig{file=figures/correlation_no_spoof.eps, width=3.25in}

The correlation shown above considers all attacks, including those which could be from spoofed source addresses. Intuitively, one would expect that source spoofing does not affect the correlation structure as it is usually done randomly, and thus unlikely to create a well-defined structure. In order to estimate the effect of spoofed sources on our results we divide the logged attacks into two classes:

We can perform this classification only on the ISP data and one of the Univ logs (U1). The rest of the logs do not contain the necessary information. Connection-oriented attacks should not have spoofed IP addresses since they require the attacking machine to respond to the TCP ACKs sent by the victim.

Figure 9 compares the correlation exhibited by the connection oriented attacks to that exhibited by the combination of all attacks. The figure plots the CDF of the size of the correlation group for each IDS for each kind of attack. The figure shows that the two CDFs are very close, indicating that the correlation structure is highly robust to source spoofing. Similarly, we have performed the correlation persistence test in §4.2 on connection oriented attacks and found the results to be compatible with those in §4.2.

4 Is the structure due to random scans?

The fact that each IDS in our dataset shares attacks with only a small and persistent set of IDSs is intriguing. Why do certain IDSs share attacks? Before answering this question, we would like to do an additional test to ensure that the spatial structure of attack correlation is not random. Suppose each worm or attacker picks for victims a random subset of all destinations, could this be responsible for generating the attack correlation structure we see in the data? The test described below shows that the answer to the this question is ``no''. The correlated attacks we see are likely targeted attacks, i.e., the victims are not randomly chosen; the same group of correlated victim networks are chosen repeatedly, probably because they are on one hit list circulating among the attackers, or because they run the same software (as in the case of the Santy worm [6]).

Figure 10: Figure shows that the set of IDSs with which an IDS is correlated is far from random. We compare the distribution of correlated IDSs in our dataset with that generated by having each common attacker target a small random set of IDSs. The difference in entropy between random targeting and the empirical data is plotted. The empirical distribution has, on average, 7.2 bits less entropy than the one generated by random targeting; correlated IDSs are therefore far from random.
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We consider the distribution of IDSs with which a particular IDS is correlated. We compare this distribution in our data with the corresponding distribution generated by random targeting. We simulate random targeting as follows. We pick an IDS, $i$, and look at all of its correlated attacks. For each correlated attack, we replace the set of IDSs with which IDS $i$ shares this attack with a random set of IDSs of the same size. We repeat this process for each attack at IDS $i$. For each IDS $j$, where $j \neq i$, the number of correlated attacks with $i$, after proper normalization, represents the probability that IDS $j$ is correlated with IDS $i$. We compare this probability distribution in our data with the one generated by random attack targeting. In our data, this distribution is highly biased, i.e., an IDS $i$ is correlated with a few other IDSs and uncorrelated with the rest of IDSs. Since we are interested in measuring how far our data is from random targeting, we compare the entropy of the two distributions. The entropy of the distribution of a random variable $X$ is:

H(X) = -\sum_{x_i}P(x_i)log(P(x_i)).
\end{displaymath} (2)

This analysis is repeated for each IDS and the difference in entropies are computed for each IDS. Figure 10 shows the CDF of these entropy differences. The figure shows that the set of IDSs with which an IDS is correlated is far from random. It shows that the empirical distribution has, on average, $7.2$ bits less entropy than the one generated by random targeting. Note that number of IDSs in our system is $1700$, hence the maximum entropy is $10.73$ bits. The difference in entropy is also bounded by the same value. Thus, an entropy difference of $7.2$ bits is very high, which shows that the set of correlated IDSs in our data is far from random.

5 Origin of IDS Attack Correlation

So why two IDSs share correlated attacks? We investigate two possible reasons: 1) closeness in the protected IP space, 2) similarity in the software and services run on the two sites. Our results show that the latter is the likely reason of attack correlation between two IDSs.

(a) Closeness in IP space: Some attackers employ scanning techniques to discover vulnerabilities. They start from a randomly selected IP and then scan sequentially. If the scanned address spaces belong to different sites, the IDS at the respective sites are likely to show attack correlation. Thus, closeness in the IP space could be a reason for attack correlation.

We compute the distance between two prefixes $P_1$ and $P_2$ of equal length as the decimal value of the bit-string produced by taking $XOR$ of $P_1$ and $P_2$. If the prefixes are of unequal length, the shorter prefix is bit-shifted to the left to equalize the lengths. The distance in IP space between two IDSs $i$ and $j$, $D_{ij}$, is defined as the IP distance between their protected address prefixes. Also for each IDS pair we generate the vector of correlation $\vec C_{ij}$, where $c_{ij}$ is the percentage of attack at $i$ which are correlated with some attacks at $j$. If proximity in the IP space is a reason for attack correlation, then the more the distance between IDSs $i$ and $j$ is, the less likely they share correlated attacks-i.e., $\vec D_{ij}$ and $\vec C_{ij}$ should be inversely correlated. Thus, we compute the cross correlation between these two vectors.[*] Note that a cross correlation around zero means independence. Figure 11 plots the cross correlation between attack correlation and distance in IP space. The x-axis is the IDS id. Note that the correlation with IP space hovers around zero, indicating that attack correlation is independent from the distance in IP space. Thus, having nearby IP prefixes does not have a visible impact on sharing correlated attacks.

(b) Similarity in Software and Services: Small correlation groups may be due to recent attack trends. In particular, two IDSs may share correlated attacks because they are on a single hit list, or they run software or a service that is targeted by the common attacker. For example, the Santy worm uses a vulnerability in popular phpBB discussion forum software to spread and uses a search engine to find vulnerable servers [6].

Unfortunately, except for the university logs (U1), we do not know the identity of the protected networks, the type of software they run, or the services they provide, and hence cannot check attack correlation against that information. Instead we perform two indirect tests.

First, we have examined the correlated attacks in each group for the case of the ISP data where full attack details are available. Indeed, except for one correlation group, each group seems to focus on a specific shared attack, i.e., more than 60% of the correlated alerts in that group are of a particular type. There are SMTP groups, NT groups, IIS groups, etc. This should not be surprising as recent attacks obtain a list of networks that run a software with the targeted vulnerability via a search engine or other ways and send only to those sites [6].

Second, we try to indirectly infer the software and services run on the correlated networks by comparing the type of alerts they generate. We compute the distribution of alert types generated by each network and compare them against each other. We divide alerts into $13$ broad categories: alerts due to attacks on DNS servers, web servers, ftp, RPC services, Windows Server 2003, servers running RPC, mail servers, servers using SQL (both MS and MySQL), telnet and ssh servers, attacks on routers, IRC servers, CIFS (SMB) servers and miscellaneous. We compute the fraction of alerts of each type in the IDS log. We consider this distribution to be characteristic of the network itself, and check whether attack correlation is correlated with correlation in this distribution.

We express the alert distribution in a vector $\vec V_{i}$ with $13$ elements. For example, $\vec V_i = (0.03, 0.2, ...)$ means that $0.03\%$ of the alerts generated by IDS $i$ are of category 1, etc. We measure the distance between the alert distributions at IDS $i$ and $j$ by the difference $\vec D_{ij}=\vert\vert\vec V_i - \vec V_j\vert\vert$, where $\vert\vert.\vert\vert$ is the Euclidean norm. Similarly to the analysis in §4.5(a), we compare $\vec D_{ij}$ with $\vec C_{ij}$, where $c_{ij}$ is the percentage of attack at $i$ which are correlated with some attacks at $j$. If similar software and services are reasons for attack correlation, then $\vec D_{ij}$ and $\vec C_{ij}$ should be inversely correlated. We compute the cross correlation between these two vectors. Note that a cross correlation around zero means independence. whereas a negative cross correlation means that an increase in the distance, $\vec D_{ij}$, is correlated with a decrease in attack correlation $\vec C_{ij}$. Figure 11 plots the cross correlation between attack correlation and our indirect measurement of the similarity of the software and services on the protected networks. Note that attack correlation is negatively correlated with our measure of the distance between the software and services on the protected networks-i.e., an increase in this distance results in a decrease in correlation. Thus, it seems that one origin of attack correlation across different networks is the similarity in the software and services they run.

Figure 11: Cross correlation between attack correlation and: 1) distance in IP space, 2) an indirect measure of site's software and services. Figure shows that attack correlation is independent of closeness in IP space. In contrast, attack correlation seems to decrease with decreasing similarity between the software run on the protected networks. The figure is for the ISP and U1 datasets for which we have detailed alert logs.
\epsfig{file=figures/cross_correlation.eps, width=3.25in}
\vskip -0.1in

5 Summary of Empirical Results

The results of our study of attack correlation can be summarized as follows:

6 Efficient Collaboration with
Trusted Partners

The major impediments to having independently administrated IDSs collaborate on detecting common attackers are: overhead and trust. Since common attackers attack different networks within a few minutes from each other, the IDSs need to exchange their alerts in realtime. But exchanging alerts with thousands of IDSs in realtime is impractical because of the resulting overhead, and the potential of having malicious IDSs incriminating innocent hosts or using the alert data to discover the vulnerabilities of other networks.

We exploit the structure of attack correlation to solve the above two problems.[*] We propose a correlation-based method for picking collaborators. By exchanging alert data with only those IDSs in its correlation group, an IDS minimizes the overhead of the collaboration while maximizing its chances of detecting common attackers. Furthermore, since the size of a correlation group is small and its membership is stable, an IDS can check using out-of-band mechanisms the reputability of each of the IDSs in its correlation group. It can use this information to decide whether to collaborate. If needed, the IDS can use legal contracts to enforce trust and privacy. If the IDSs choose to collaborate, they use a secure channel to exchange information so that eavesdroppers cannot snoop.

IDSs need to know which other IDSs are in their correlation group. We envision a number of non-profit organizations (like CERT and DSHIELD) and commercial entities that discover attack correlation across IDSs and report to each network the identity of the other networks in its correlation group. We call these entities Attack Correlation Detectors (ACD). A network may participate in one or more ACDs. The choice of ACD may depend on the number and types of networks participating in the ACD, its reputation, etc. The ACD occasionally collects logs from participant IDSs. The logs cover a particular period that can be as small as a single day randomly chosen by the ACD. The logs have minimal sensitive information. Each record in the log provides the following fields: (Time, Source IP, Packet Count). Our analysis in §2.2.3 shows that these fields are enough for detecting attack correlation. The ACD performs the correlation analysis and informs each network of its correlation group, expressed as a list of the following records: (correlated IDSs, level of correlation). The correlation analysis is not intensive, the average time taken to analyze a days worth of logs is just $4$ hours on an Intel Itanium $1.5$ GHz SMP machine with $2$ GB of memory. Further since IDS correlation is persistent over atleast a month, the analysis is repeated only after such long periods of time. Once organizations know their correlation group, they can independently decide with whom to collaborate, basing their decisions on the level of correlation and the identity of the peer network.

Integrating new IDSs and updating participant IDSs about changes in their correlation can be performed incrementally. A new IDS provides logs from the same collection point so that its correlation group can be found. Updates are incremental, since IDSs need to be informed only if their correlation group changes. Due to the persistence of membership in these correlation groups (a month or more), the update process can be performed in a lazy fashion with the cost amortized over long periods of time.

It should be noted that acting as an ACD is relatively simple. Indeed, DSHIELD already has the means to provide this service to its participant networks.

1 Discussion

(a) Scalability: Correlation-based collaboration ensures scalability by the small size of the groups and the persistence of correlation among IDSs across long timescales. In particular, over 96% of the IDSs in our dataset are correlated with less than $10$ other IDSs. The overhead of setting up peering and exchanging information is therefore relatively small. Additionally, the persistence of correlation over months ensures the scalability of ACDs. The ACDs analyze correlation at these timescales, amortizing the cost of the analysis.

(b) Privacy: Recall that for discovering its correlation group an IDS provides the ACD with logs of attacking IP addresses, alert time, and packet count. Thus, none of the sensitive alert fields such as the attack type, the destination, and the destination port, are needed. Also the data is revealed only to the ACD and does not get published. On the other hand, privacy of the data exchanged with one's collaborators is provided largely because IDSs have the ability to independently decide which IDSs to collaborate with, and what to reveal. Further, the persistence of correlation allows the collaborators to use legal contracts to protect their data, if necessary.

(c) Protecting against spreading lies: An IDS that lies about its attackers to the ACD does not harm the system. Such lies are unlikely to be correlated with any of the attacks seen at other IDSs, even if they do, each IDS checks independently the credential of each of its collaborators before sharing any alert data with them. Lying to one's collaborators is unlikely as their reputations are carefully checked and information exchange is protected by legal contracts.

7 Picking the right collaborators

We present a rough evaluation of the overhead and the enhancement in detection capability obtained via various choices of collaborating IDSs for detecting correlated attacks. We compare the following 4 schemes for picking collaborators:

1 Blacklisting Malicious Sources

In order to compare the above schemes, we need to specify a protocol for exchanging alerts and processing the acquired information. We use the simple approach described below. This approach is not necessarily optimal, but it suffices to evaluate the relative benefits of the different methods of picking collaborators.

The IDSs collaborate to detect low rate attackers and speed up the detection of moderate rate attackers. Each IDS maintains a Blacklisting Threshold and a Querying Threshold. A source IP address is blacklisted when the number of suspicious packets from it crosses a Blacklisting Threshold. An IDS queries its collaborators when the number of malicious packets from a source IP address crosses the Querying Threshold. If the aggregate rate of the offending source at all collaborators exceeds the Blacklisting Threshold the source is blacklisted. Once a source is blacklisted it is set apart for further investigation and an alarm is triggered to all collaborators.

The time taken to blacklist a source depends on two factors; the rate at which the source is attacking as well as the chosen Blacklisting Threshold. In picking a particular threshold, there is an inherent tradeoff between false positive ratio and false negative ratio. A low Blacklisting Threshold will result in a high false positive ratio while a high threshold will miss many moderate rate attacks resulting in a high false negative ratio. The right value for the Blacklisting Threshold is site specific and should be picked to optimize the false negative and false positive ratios.

We use the ISP and U1 datasets to find a good value for the thresholds because these logs contain enough information to distinguish many cases of false positives. We set the Blacklisting Threshold to $1000$ malicious packets/day because in our dataset, this rate results in a false positive ratio less than 1%. We set the Querying Threshold to $50$ malicious packets/day. The Querying Threshold has to be substantially lower than the Blacklisting Threshold, but there is nothing special about the value of 50 packets/day. In reality, these thresholds will vary depending on the local sites configuration as well as the nature of the alert itself. The above thresholds seem reasonable for those IDSs in our dataset for which we have detailed attack information.

To simulate the attacks, we replay the traces in our datasets. We divide one month worth of traces into two equal parts, corresponding to $15$ days each. The correlation groups are generated from one set (the training set), while the various schemes for picking collaborators are tested on the other set (the test set).

2 Detection Speedup

Figure 12: Comparison of time taken in minutes for blacklisting a shared malicious source for CBC, Local Detection, Random Collaboration and Collaboration with All IDSs. Short duration attacks (under $5$ minutes) which number around 25% do not show significant difference, local detection works nearly as well. CBC performs nearly as well as collaboration with all IDSs in detecting longer duration, slower attacks. Random collaboration offers no benefit except for a few sources.
\epsfig{file=figures/bl_times_schemes.eps, width=3.25in}

Figure 12 plots the time it takes to blacklist a source in each of the four approaches: CBC, Local Detection, Random Collaboration and Collaboration with All IDSs. The time to blacklist a source is defined as the time difference between the instant the source is blacklisted by some IDS and the instant the source was first detected by any of the collaborators. The plots are only for sources detected at more than $1$ IDS, because localized sources always require the same time to detect under all four schemes. The malicious sources on the $x$ axis are sorted according to their detection time by Local Detection. Note that for this figure, we set Blacklisting Threshold to a total of $1000$ packets, rather than $1000$ packet/day, so that each approach will eventually detect the malicious source.

The figure shows that, for fast sources which can be detected locally in $5$ minutes or less, there is no significant difference among the four schemes. These form nearly $25\%$ of all classified common attackers. The curves diverge for slower sources which take longer to blacklist locally. Random collaboration offers no benefit, i.e., the time taken to blacklist is the same as Local Detection except for a few sources. In contrast, CBC speeds up detection for about $75\%$ of the studied sources, and performs nearly as well as collaborating with all IDSs. There are a small number around $5\%$ of slower sources which take longer to detect in CBC because of them being correlated across IDSs which do not belong to each other's correlation group.

3 Overhead

In comparison with Local Detection, the speedup in detecting malicious sources is obtained at the cost of communication among the collaborators. The average query rates in CBC and Random Collaboration are fairly close. They both have an average of about $1.3$ query/minute/IDS, with a standard deviation of $2.9$. In comparison, collaborating with all IDSs has a very high overhead; the average query overhead is $454.9$ query/minute/IDS, which is $2$ orders of magnitude higher than CBC.

4 Effectiveness

Table 3: Comparison between 4 schemes for picking collaborators in terms of alert volume reduction and the number of malicious sources missed.
CBC Local Random All
Detection Collaboration IDSs
Reduction $73.44\%$ $35.48\%$ $37.77\%$ $80.56\%$
missed $5.02\%$ $38.65\%$ $36.69\%$ $0\%$

Faster detection of malicious sources also results in significant reduction in alert volume. Table 3 lists the average reduction in alert volume from blacklisting under CBC, Random Collaboration, Collaborating with all IDSs and Local Detection. On average, CBC results in $73.44\%$ reduction in alert volume (i.e., the size of the log that admin should examine). The value is close to the one obtained by collaborating with all IDSs, which is $80.56\%$. Local Detection, on the other hand, performs significantly worse; it reduces the alert volume only by $35.48\%$. There is no discernible difference between Local Detection and Random Collaboration, the reduction in alert volume is only marginally better at $37.77\%$. The above numbers are for all attacks, correlated and uncorrelated. Thus by being able to quickly detect correlated attacks, CBC reduces alert volume by a further $38\%$ over Local Detection.

Table 3 also lists the fraction of correlated malicious sources missed by CBC, Local Detection, and Random Collaboration in comparison to Collaborating with all IDSs. A malicious source is missed if the scheme is unable to blacklist it due to incomplete information, though it is blacklisted all IDSs collaborate. CBC misses only $5.02\%$ of the malicious sources, while Local Detection misses $38.65\%$ of them. Random collaboration scheme is almost similar at $36.69\%$. These values depend on the thresholds used, but they demonstrate the order of magnitude improvement obtained in CBC and the insignificant difference between CBC and collaborating with all IDSs. In summary, CBC improves significantly over Local Detection. It increases the number of detected sources by $33\%$, and reduces the volume of alerts by an extra 38% beyond Local Detection. It performs almost as well as the collaborating with all IDSs. In contrast, a random choice of collaborators is as bad as not collaborating.

8 Related work

Several proposals exist for building collaborative and distributed intrusion detection systems [21,9,10,26,20,23,22,8,28], but none of them has studied attack correlation. Our work extends many of these proposals with a mechanism for picking collaborators, and maximizes the benefit of collaboration while limiting its overhead.

Early distributed intrusion detection systems collect audit data from distributed component systems but analyze them in a central place (e.g., DIDS [21], ISM [9], NADIR [10], NSTAT [26] and ASAX [8]). Recent systems have paid more attention to scalability (e.g., EMERALD [20], GrIDS [23], AAFID [22], and CSM [27]). We discuss a few of them below.

The Collaborative Intrusion Detection System [14] involves dynamic groups of nodes that rapidly change and exchange information. The set of nodes exchanging information is not constant and is changed continuously to cover all nodes in the system which limits its scalability. COSSACK [11], another collaborative response framework, is concerned more with alarm propagation than detection itself. DOMINO [28] relies on a hierarchy of nodes with different levels of trust and aims to exchange blacklist information. The nodes are placed such that IDSs protecting networks with close destination address spaces are close together.

The Distributed Intrusion Detection System (DIDS) [21] addresses system attacks across a network. Attacks such as doorknob, chaining, and loopback could be detected when data from hosts within a given network was combined under centralized control. Clever attackers could still subvert DIDS by reducing the volume of attacks for a given network.

EMERALD addresses intrusions within large separately administered networks [20]. EMERALD includes features for handling different levels of trust between the domains from the standpoint of a centralized system: individual monitors are deployed in a distributed fashion, but still contribute to a high-level event-analysis system. EMERALD appears to scale well to large domains. The Hummer project [15] focuses on the relationships between different IDSs (e.g., peer, friend, manager/subordinate relationships) and policy issues (e.g., access control, cooperation policies).

Finally, there has been work on specification and event abstraction to allow multiple IDS boxes to share attack information and collaborate on detection and protection [5,24,7].

Attack Measurements & Analysis: A lot of work has been done in characterizing attack characteristics. Yegneswaran et al. [28,18] study the global characteristics of intrusions as well as Internet background radiation. Network telescopes are used to study DoS activity in [17]. Placement of blackholes in a distributed Internet setting for global threat detection is addressed in [3].

Analysis of Intrusion Alerts: GrIDS [23] collects traffic and connections data. It analyzes TCP/IP network activities using activities graphs and reports anomalies when activity exceeds an user specified threshold. Methods of discovering intent by correlating alerts from different IDSs are presented in [12]. Algorithms for sharing of alerts [13] in a privacy-preserving manner could be a future avenue of research. Alert correlation to reduce the number of alerts to be manually examined is discussed in [4]. Alerts are inserted into a relational database to be aggregated and the summarized alert is presented to the operator. These are orthogonal to our work and can be easily integrated.

9 Concluding Remarks

We have presented the first wide-scale study of attack correlation in the Internet, i.e., attacks that share the source IP but occur at different networks. Our dataset, constituting of alert logs collected at $1700$ IDSs, show that correlated attacks are fairly prevalent in today's Internet; 20% of all the attacking sources are shared attackers, and they are responsible for 40% of all alerts in our logs. Shared attackers attack different networks within a few minutes of each other, emphasizing the advantage of realtime collaboration between victim networks as opposed to sharing attack information offline.

Our results also show that the $1700$ IDSs can be grouped into small correlation groups of 4-6 IDSs; two IDSs in the same correlation group share highly correlated attacks, whereas IDSs in different correlation groups see almost no correlated attacks. Furthermore, the correlation groups are stable and their membership persists for months. Though not conclusive, our analysis indicates that similarity in the software and services running on the protected networks causes their IDSs to show attack correlation.

Our empirical results have important implications for collaborative intrusion detection of common attackers. They show that it is quite important that each network/IDS picks the right collaborators. Exchanging alerts with thousands of IDSs in realtime is impractical because of the resulting overhead and the lack of trust between these networks. Using a trace-driven simulation, we show that picking at random a smaller and fixed set of collaborators has almost no benefits beyond local detection. In contrast, collaborating with the 4-6 IDSs in one's correlation group has almost the same utility as collaborating with all $1700$ IDSs in our dataset with $350$ times less overhead.

Finally, we note that our results reflect the state of the Internet at the end of 2004 and the beginning of 2005. It is hard to predict the extent of attack correlation in the future and the continuous existence of correlation groups. Future research should investigate these characteristics and track their evolution.

10 Acknowledgments

We would like to thank John Hardenbergh, Ben Leong, Harsha Madhyastha, Vyas Sekar and the anonymous referees for their comments; the Internet Storm Center for providing us the DSHIELD data; Ed Amoroso, Martin Arlitt, Tim Battles, Glenn Fowler, Patrick Haffner, Adam Hammer, Christopher Morrow, Manuel Ortiz, Dan Sheleheda, and Vinod Yegneswaran for their help with our project. Also, Katti and Katabi acknowledge the support of the National Science Foundation under NSF Career Award CNS-0448287. The opinions and findings in this paper are those of the authors and do not necessarily reflect the views of NSF.


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About this document ...

Collaborating Against Common Enemies

This document was generated using the LaTeX2HTML translator Version 2002-2-1 (1.71)

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Copyright © 1997, 1998, 1999, Ross Moore, Mathematics Department, Macquarie University, Sydney.

The command line arguments were:
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... attacks.[*]
Moore et al. [16] have shown that it is hard to contain epidemic worms such as Code Red using IP blacklists. In the 150 million alerts we observed in our ISP dataset logs from 40 IDS, only 4% are caused by epidemic worms. Thus, IP blacklisting continues to be an important tool for warding off attack.
... vectors.[*]
The cross correlation is defined as:

r_{xy} = \frac{\sum_{i}(x(i) - \bar{x})(y(i) - \bar{y})}{\s...
...m_{i}(x(i) - \bar{x})^{2}} \sqrt{\sum_{i}(y(i) - \bar{y})^2}}

where $r_{xy}$ is the cross correlation, $x$ and $y$ are vectors of equal length, and $\bar{x}$ and $\bar{y}$ are the corresponding means.
... problems.[*]
Message formatting and exchange protocols, though necessary for IDS collaboration, are beyond the scope of this paper. Some of the existing literature addresses these issues [7,5,24].

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