Entropy and denial of service attacks

Master in Web Science
Mathematics Department
Aristotle University of Thessaloniki

“Entropy and Denial of Service
Attacks”

Zlatis Chris

Contents
• Denial of Service attacks – Definitions, related surveys
• Traceback of DDoS Attacks – Proposed method,
advantages, future work
• Detection methods with Shannon and Renyi cross
entropy – Previous works, proposed method, dataset
and results
• The added value of entropy detection methods
• References

Definitions
• Distributed Denial of Service Attacks (DDoS Attacks) are defined as
[4] “attempts to make a computer resource unavailable to its intended

users”.
– The attacker gains control of a huge number of independently owned
and geographically distributed computers, called “zombies”, almost
always without any knowledge of their owners.

• Ping flood is a type of DDoS attack directing a huge number of
“ping” requests to the target victim. It exploits the “Internet Control
Message Protocol” (ICMP). [4]
– Huge number of ping ‘echo requests’ from a very large number of
“zombies”  unable to conduct any network activity other than
answering the ping ‘echo requests’  overloaded and standstill.

Recent surveys
• It has been a major threat to the Internet since year 2000, and a
recent survey on the largest 70 Internet operators in the world [4]
demonstrated that:
1. DDoS attacks are increasing dramatically, and individual attacks
are more strong and sophisticated
2. The network security community does not have effective and
efficient traceback methods to locate attackers as it is easy for
attackers to disguise themselves
3. The Mafiaboy attacks of February 2000 against Amazon, eBay
caused millions of dollars damage

 There is a need to detect DDoS attacks as early as possible so that
proper countermeasures can be applied and damage can be
minimized.

Traceback of DDoS attacks
• IP traceback means the capability of identifying the actual source of
any packet sent across the Internet  successful if they can identify
the zombies from which the DDoS attack packets entered the
Internet.
There are two major methods for IP traceback: [6]
1. The probabilistic packet marking (PPM) and
2. The deterministic packet marking (DPM).
• The PPM strategy can only operate in a local range of the Internet
(ISP network)  we cannot traceback to the attack sources located
out of the ISP network. The DPM strategy requires all the Internet
routers to be updated for packet marking.
• Both of these strategies require routers to inject marks into
individual packets  vulnerable to hacking, referred to as “packet
pollution”.

IP Traceback using entropy variations
• IP traceback using information theoretical parameters [6]  there
is no packet marking in the proposed strategy  avoid the inherited
shortcomings of the packet marking mechanisms.

• The packets that are passing through a router are categorized into
flows [6], which are defined by the upstream router where a packet
came from, and the destination address of the packet.
– During non attack periods, routers are required to observe and
record entropy variations of local flows.
– Once a DDoS attack has been identified, the victim initiates a
pushback process to identify the locations of zombies…

IP Traceback using entropy variations
• The pushback process: [6]
1. The victim first identifies which of its upstream routers are in the
attack tree based on the flow entropy variations it has
accumulated, and then
2. submits requests to the related immediate upstream routers.
3. The upstream routers identify where the attack flows came from
based on their local entropy variations that they have monitored.
4. Once the immediate upstream routers have identified the attack
flows,
5. they will forward the requests to their immediate upstream
routers, respectively, to identify the attacker sources further.

 This procedure is repeated in a parallel and distributed fashion
until it reaches the attack source(s).

Advantages of traceback mechanism
The proposed traceback mechanism possesses the following
advantages: [6]
• It overcomes the inherited drawbacks of packet marking methods,
such as limited scalability, huge demands on storage space, and
vulnerability to packet pollutions.
• It brings no modifications on current routing software  work
independently as an additional module on routers for monitoring
and recording flow information.
• It will be effective for future packet flooding DDoS attacks because
it is independent of traffic patterns.
 It can archive real-time traceback to attackers. Once the short-
term flow information is in place at routers, and the victim notices
that it is under attack, it will start the traceback procedure.

Future work on Traceback methods
Future work could be carried out in the following promising directions: [6]
• 1. The metric for DDoS attack flows could be further explored. The
proposed method deals with the packet flooding type of attacks
perfectly.
 The attacks with small number attack packet rates, e.g., if the attack
strength is less than seven times of the strength of non attack flows, the
current metric cannot discriminate it. Therefore, a metric of finer
granularity is required to deal with such situations.

• 2. Location estimation of attackers with partial information. When
the attack strength is less than seven times of the normal flow packet
rate, the proposed method cannot succeed at the moment.
 The attack can be detected with the information that we have
accumulated so far using traditional methods or recently developed
tools.

Detection methods with Shannon and
Renyl cross entropy
An entropy-based method [1] is proposed to detect network attack:
• The Shannon entropy is used to analyze the distribution
characteristics of alert with five key attributes including source IP
address, destination IP address, source threat, destination threat
and datagram length that reflect the regularity of network status
– When the monitored network runs in normal way, the entropy values
are relatively smooth. Otherwise, the entropy value of one or more
features would change.

• Then, the Renyi cross entropy is employed to fuse the Shannon
entropy vector and detect the anomalies. The Renyi cross entropy
of these features is calculated to measure the network status and
detect network attacks.

Previous works on entropy
detection methods
• Gina investigated the extent of false alerts problem in Snort using
the 1999 DARPA IDS evaluation data.
– They found that 69% of total generated alerts are considered to be false
alerts. [1] These problems make it a frustrating task for security officers to
detect network attack quickly and accurately.
• Gu proposed an approach to detect anomalies in the network
traffic using Maximum Entropy estimation.
– The packet distribution of the benign traffic was estimated using
Maximum Entropy framework and used as a baseline to detect the
anomalies.
• Qin used Renyi cross entropy to detect dynamic changes in network
traffic of large enterprises. Three traffic features were proposed to
capture dynamic changes of traffic.
• A. Wagner and B Plattner applied entropy to detect worm and
anomaly in fast IP networks. The entropy contents of IP addresses
were used to indicate a massive network event.

Dataset and results
• Methodology: [1] Use of Snort to monitor 32 C-class subnets in the
campus network for two weeks, which include more than 3,000
end users.
– Alerts in Mar. 2nd as the experimental data: 1,147,906 alerts in this
day with 79 signatures, 32,409 source IP addresses, 12,642 destination
IP addresses  two alerts sets collected from different time period in
Mar. 2nd as training and test data.

The statistical results of alerts suggest several interesting results:
1. More alerts were generated in daytime than that in night due to
people’s living habit. There were two peaks of alerts: 12:00 to
14:00 and 21:00 to 23:30  the end users are campus students.
2. The destination IP addresses change abruptly from 0:00 to 4:00,
6:00 to 10:00, 12:00 to 16:00 and 18:00 to 22:00. By analyzing the
alerts, they found many host scan attacks at these time periods.

The added value of Entropy methods
• The Shannon entropy is used to analyze the alerts to measure the
regularity of current network status.
– They are relative smooth when no attack occurs; otherwise, one or
some of the values would change abruptly.

• The Renyi cross entropy is employed to detect network attack.
– The Renyi cross entropy value is near 0 when the network runs in
normal, otherwise the value will change abruptly when attack occurs.

• However, although the Shannon entropies reflect the regularity of
network status, it is difficult to detect attack directly by using five
fixed thresholds [1], because the Shannon entropy value varies with
the activities of end users even the network runs in normal way.

References
[1] Zhiwen Wang, Qin Xia, “An Approach on Detecting Network Attack Based
on Entropy”, Xi’an Jiaotong University, China
[2] Hoa Dinh Nguyen, Sandeep Gutta, Qi Cheng, “An Active Distributed
Approach for Cyber Attack Detection”, Oklahoma State University
[3] Tsern-Huei Lee - Jyun-De He, “Entropy-Based Profiling of Network Traffic
for Detection of Security Attack”, National Chiao Tung University, Taiwan
[4] Anna T. Lawniczak, Bruno N. Di Stefano, Hao Wu, “Detection & Study of
DDoS Attacks Via Entropy in Data Network Models”, CISDA, 2009
[5] Stephen Schwab, Brett Wilson, Roshan Thomas, “Methodologies and
Metrics for the Testing and Analysis of Distributed Denial of Service
Attacks and Defenses”, SPARTA Inc.
[6] Shui Yu, Wanlei Zhou, Robin Doss, Weijia Jia, “Traceback of DDoS Attacks
Using Entropy Variations”, IEEE, March 2011

Entropy and denial of service attacks

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