Mapping Internet Sensors with Probe Response Attacks

Authors: John Bethencourt, Jason Franklin, Mary VernonPublished At: Usenix Security Symposium, 2005Presented By: Anvita Priyam

Internet Sensor Networks

Used as a tool to detect malicious internet traffic.

e.g. honeypots, log analysis centers

They publish public reports without disclosing sensor locations.

Maintaining sensor anonymity is critical

Overview

Central Idea Internet Storm Center(ISC) Background Probe response attack Countermeasures Weaknesses Suggestions

Central Idea

This paper presents an attack technique, “Probe Response”

It is capable of determining the location of internet sensors that publicly display statistics.

It uses SANS internet storm center as case study.

Motivation for attack

Focus is on internet sensors that enable collaborative intrusion detection through wide area perspective of internet.

logs

source central Statistics Repository

0

10

20

3040

50

60

7080

90

1st Qtr 2nd Qtr 3rd Qtr 4th Qtr

East

West

North

Case Study: The SANS Internet Storm Center (ISC)

System that collects data from internet sensors and publishes public reports.

It analyzes and aggregates this information and automatically publishes several types of reports.

These reports are useful in detecting new worms and blacklisting hosts controlled malicious users.

Port Report

Attacks are primarily concerned with port reports.

For each port the report gives three statistics: > Number of reports: total entries in the log > Number of sources: distinct source IP addresses

with given port > Number of targets: distinct destination IP addresses

Example

Probe Response Attack- The Big Picture

Core Idea – Probe an IP address with activity that will be reported to the ISC.

NO YES

YES

NO

ATTACKERSends Packets

Monitored??Look for next IPAddress

Check the Reports

Reported??Host is submitting logsTo the ISC

Basic Probe Response Algorithm

Consists of two stages First Stage > Begins with an ordered

list of IP addresses (0,1,2…) to check.

> All invalid or unroutable addresses are filtered out

> SYN packets are sent on port Pi to each address in Si.

First Stage (cont’d)

Wait for 2 hours and retrieve port report

Intervals lacking activity are discarded

Remaining intervals are sent to 2nd stage with number of monitored addresses in each

Second Stage

Repeats until the attack is complete

Distribute the ports among remaining intervals

Divide each interval into subintervals

Send packets to every subinterval except the last

Second Stage (cont’d)

For each subinterval of remaining interval we retrieve the report

Number in last subinterval= (total in that interval-number in other

subintervals) Empty subintervals Are discarded Remaining subintervals are new set of

remaining intervals Continue to divide until only monitored or

unmonitored addresses are left

Example

Dealing with noise

Sources other than attacker may be sending packets to monitored address with same destination ports

This increases the number of targets reported Causes the algorithm to produce both false

positives and false negatives However, for a large number of ports this is low. Use Report Noise Cancellation factor- send

multiple number of packets & while reviewing the reports divide by the same factor

Simulation of Attack

First scenario- determine exact set of monitored addresses (accurate but time consuming)

Second scenario- finding superset and subset of monitored addresses

Use three different attackers T1- 1.544Mbps upload bandwidth T3- 38.4 Mbps upload bandwidth OC6- 384 Mbps upload bandwidth

Results

Results

Results

Finding a Superset

Maximum false positive rate= 0.94

Report noise cancellation factor= 4

Runtime of attacks is reduced from 112 to 78 hours

Accepts around 3.5 million false positives which had little effect on number of probes

Finding a Subset

Maximum false negative rate= 0.001 Report noise cancellation factor= 2 Reduces the runtime from 33 days and 17

hours to 15 days and 18 hours Reduces the number of probes sent from 9.5

billion to 4.4 billion But misses 26% of the sensors

Countermeasures

Hashing- some or all of the fields Encryption- encrypting a field with a key not

publicly available Private reports- limit the info in the reports Query limiting- limit the rate at which they can

be downloaded Sampling- sample the logs coming in for

analysis before generating reports

Weaknesses

Uses adaptive probe response algorithm as each round depends on the result of the previous one

The countermeasures suggested are not very effective

Suggestions

Developing and evaluating a non-adaptive approach

Come up with more effective countermeasure