Minggu, 26 April 2009

Security Community

Security Community

We decided to share this data with the
security community and demonstrate its value. We will focus on two areas. First, we intend to demonstrate how
active the blackhat community can be. Regardless of who you are, you are not safe. Our goal is to make you
aware of this threat. Second, to test the concept of Early Warning and Prediction. By identifying trends and
methods, it may be possible to predict an attack and react, days before it happens. We test this theory using the
data the Honeynet Project has collected.
The Collected Data
The Honeynet Project maintains an eight IP network that is highly controlled and closely monitored.
This Honeynet consisted of eight IP addresses, using a simple ISDN connection provided by a local ISP. This
type of connection is the same used by many homeowners or small business users. In fact, the Honeynet was
located in the spare bedroom of one of the Project members. During that time period, there normally existed one
to three systems within the Honeynet.
The Honeynet network, the network used to capture data, is a basic network of commonly used operating
systems, such as Red Hat Linux or Windows NT, in a default configuration. No attempts were made to broadcast
the identity of the Honeynet, nor was any attempt made to lure attackers. Theoretically this site should see very
little activity, as we do not advertise any services nor the systems. However, attack they do, and frequently.
What makes Honeynet data even more valuable is the reduction of both false positives and false negatives, both
common problems of many organizations. False positives are when organizations are alerted to malicious activity,
when in fact there is nothing going wrong. When organizations are repeatedly hit with false positives, they begin
to ignore their alerting systems and the data it collects, making the systems potentially useless. For example, an
Intrusion Detection System mail alert administrators that a system is under attack, perhaps a commonly known
exploit was detected. However, this alert could have been mistakenly set off by a user's email that contains a
warning about known exploits, and includes the source code for the attack to inform security administrators. Or
perhaps network monitoring traffic such as SNMP or ICMP had mistakenly set off alerting mechanisms. False
positives are a constant challenge for most organizations. Honeynets reduce this problem by not having any true
production traffic. A Honeynet is a network that has no real purpose, other then to capture unauthorized activity.
This means any packet entering or leaving a Honeynet is suspect by nature. This simplifies the data capture and
analysis process, reducing false positives.
False negatives are another challenge most organizations face. False negatives is the failure to detect a truly
malicious attack or unauthorized activity. Most organizations have mechanisms in place to detect attacks, such as
Intrusion Detection System, Firewall logs, System logs, and process accounting. The purpose of these tools are
to detect suspicious or unauthorized activity. However, there are two major challenges leading to false negatives,
data overload and new threats. Data overload is when organizations capture so much data, not all of it can be
reviewed, so attacks are missed. For example, many organizations log Gigabytes of firewall or system activity. It is extremely difficult to review all of this information and identify suspect behavior. The second challenge is new
attacks, threats that organizations or security software is not aware of. If the attack is unknown, how can it be
detected? The Honeynet reduces false negatives (the missing of attacks) by capturing absolutely everything that
enters and leaves the Honeynet. Remember, there is little or no production activity within a Honeynet. This means
all the activity that is captured is most likely suspect. Even if we miss the initial attack, we still captured the
activity. For example, twice a honeypot has been compromised without Honeynet administrators alerted in real
time. We did not detect the successful attack until the honeypots initiated outbound connections. Once these
attempts were detected, we reviewed all of the captured activity, identified the attack, how it was successful, and
why we missed it. For research purposes, Honeynets help reduce false negatives.
The value of the data you are about to review is that both false negatives and false positives have been
dramatically reduced. Keep in mind, the findings we discuss below are specific to our network, this does not mean
your organization will see the same traffic patterns or behavior. We use this collected data to demonstrate the
nature of certain blackhats, and the potential for Early Warning and Prediction.
Analyzing the Past
While researching the blackhat community, the Honeynet Project has been astonished to see just how active the
blackhat community can be. The findings are scary. Below are some of the statistics we have identified from the
eleven month period of data we collected. The purpose of these figures are to demonstrate the active behavior of
the blackhat community. Keep in mind, these statistics represent a home network of little value that was neither
advertised nor made any attempts to lure blackhats. Larger organizations that have great publicity or value most
likely are probed and attacked in far greater numbers. This indicates that some blackhats are not bothering to confirm what
operating system nor what version of the service you are running. Some blackhats have streamlined their
scanning process to merely look for a specific service. If they find the service, they launch the exploit
without even first determining if the system is vulnerable, or even the correct system. This active approach
allows blackhats to scan and exploit more systems in less time. The most popular attack method was an overflow associated with rpc.statd for Intel based systems.
 The most popular scanning method detected was the SYN-FIN scan to search the entire IP range for specific ports (often in sequential order). This reflects the tactic of focusing on a single vulnerability, and
scanning as many systems as possible for the vulnerability. Many blackhats only use a single tool or
exploit that they know how to use, or is the most effective.
Predicting the Future
One of the areas the Honeynet intends to research is Early Warning and Prediction. It is our intent to give more
value to the data Honeynets collect by predicting future attacks. This theory is not new and is being pursued by
several outstanding organizations. It is our hope that this research benefits and substantiates these and other
organizations. In an effort to predict trends, two members of the Honeynet Project took two different approaches. However, their
findings were the similar, almost all attacks could be detected two to three days ahead of time.
Early warning using Statistical Process Controls (SPC):
The first was a very basic statistical analysis, similar to the statistical process control methodology used in the
manufacturing world to measure defects in a factory setting. This method, although very simple, proved extremely
accurate in providing short-term (three days or less), warning notice of impending attacks on the Honeynet.
All calculations were performed without prior notice of attempted or successful attacks. Only after the control chart
was calculated, were attempted and successful attacks plotted in the timelines. All data is available on the
Honeynet site. Between days 61 through 68, the 3DMA showed a run, or upward movement in the
control chart, indicating an abnormal amount of activity. On day 68, an attempted access was noted
using rpc.statd. Again, at day 153, and 170, an abnormal amount of activity was noted at port 111,
followed by a successful intrusion at day 177, using an rpc.statd overflow. Below is a graphical
representation of this model, X-axis are the days into the sample, Y-axis is the frequency.
 DNS/named: Days 81-85 showed unusual activity above control limits, querying named services. On
day 85, named services were unsuccessfully attacked.
Validation through Regression Analysis and ARIMA:
The second methodology was used to validate the results of the first. We felt that it would be a useful exercise to
look at the relationship between snort rpc rule violations and the number of days until system compromise. While
a more proper time series model is in order, a very quick and preliminary look can be had using a simple
predictive regression model regressing the frequency of a number of rpc rule violations on days until system
Figure 1 below reveals the predicted number of days until a system compromise with rpc.statd from this model.
The horizontal axis represents the date, in days during the sample time, from 1-180. Downward spikes indicate
significant activity, predicting an impending attack. This activity, is visible about 10 days before the actual
compromise occurs on day 68. Again there are three downward "threat spikes" near the end of the chart before
the system is again compromised by the same rpc attack on day 177. We have not yet confirmed what the
upward spikes are, preliminary analysis suggests this is 'quiet time' or relatively safe periods.

While it should be cautioned that there are some statistical problems with the model - including a large Durbin-
Watson statistic suggesting that there is some serious auto correlation that needs to be removed from the model -
preliminary examination suggests that there are methods to warn of an impending attack several days before it
happens. A more sophisticated time series analysis of this data in conjunction with other data would be most
useful in further supporting the idea of early warning.
Examining Characteristics of Pre-Attack Signals using an ARIMA Model
Another area of investigation is to discern the characteristics of certain types of attacks and probes. This second
example comes from one of the Honeynet Team's "Scans of the Month". Graph below portrays the number of port
scans over a 30 day period. One of the questions we would like to answer is, "What is the typical period of time
within which either an attack, further probing or a cessation of activities might be observed" for various probes and
pre-attack behaviors. In this case a simple time series ARIMA (Auto Regression Integrated with Moving
Averages) model was fitted to the data. ARIMA is a basic model used in time series analysis for looking at data
collected over a period of time. The graph below demonstrates the frequency of port scans for the month of
November.The results of the ARIMA model appear in the table below. This table suggests that a port scan session can
typically last up to three days before it terminates and another phase of the pre attack, the attack itself or
cessation of "hostilities" ensues. It also suggests that the 3 day moving average suggested by our other team
statistician may be too generous and that a 2 day moving average process may better describe at least this type
of attack.

In both of these analyses it should be noted that they are conducted on very limited and small set of data.
However it suggests that analyses on larger sets of data may in fact bear non-trivial fruit in helping to find
statistical models that can create "threat alerts" of attacks in advance of the attack itself. To further test and prove
these theories, we intend on developing the following:
 We need to acquire more and better data to get a good idea of patterns and relationships
 More variables, adding other types of snort captures will help us better understand the processes
 Different analysis techniques like Event History Analysis
We encourage the security community to test and develop these theories and perform their own statistical
analysis. We are especially interested in any other types of analysis or finding people may find. What we have
presented here is by no means an exhaustive analysis, rather this represents preliminary research. Linked below
is the data collected and used by the Honeynet Project. Th
During an eleven month period the Honeynet Project attempted to collect every probe, attack, and exploit sent
against it. This data was then analyzed with two goals in mind. The first goal was to demonstrate just how active
the blackhat community can be. The numbers demonstrate the hostile threat we all face. Remember, the
Honeynet used to collect this information had no production systems of value, nor was it advertised to lure
attackers. If your organization has any value, or is advertised in any way, you are most likely exposed to even
greater threat. The second goal was to test the theory of Early Warning and Prediction. We feel there is potential
in predicting future attacks. Honeynets are by no means the only method to collect such data, however they have
the advantage of reducing both false positives and false negatives.

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