BOTNET

Kumar Mukherjee

Mike Ladd

Nazia Raoof

Rajesh Radhakrishnan

Bret Walker

• network of infected hosts, under control of a human operator (botmaster)

• tens of thousands of nodes

• victims claimed by remote exploits

Botnet Background

• use of Command & Control (C&C) channels

• used to disseminate botmaster's commands

Defining Characteristic

• Spam• ID Theft• Piracy• DDOS

• Ex. 1000 bots w/ 128KBit/s connection > many corporate systems

• IP distribution makes filtering difficult

Uses of Botnets

Lifecycle of Botnet Infection

• IRC designed for both point-to-point and point-to-multipoint communication

• one-to-one, or one-to-group chat

• flexible, open-source protocol

Why IRC?

• authenticate to IRC server via PASS message

• C&C channel authentication

• Botmaster authenticates to bot population to issue commands

Bot-to-IRC Communication

• 400,000+ nodes• 50+ Forture 500

companies• 2x the size of ‘Storm’• Used for spam (bots

sending 500,000+ messages daily)

Bot-News: Kraken

• Designed as image file• Regular updates to binary• C&C communication via

customized UDP/TCP• Able to generate new

domain names if C&C is disabled

Bot-News: Kraken

• http://www.honeynet.org/papers/bots/

• http://www.wired.com/wired/archive/14.11/botnet_pr.html

• http://en.wikipedia.org/wiki/Storm_botnet

Further Background

•Collection of as many bot binaries as possible•Distributed darknet used •14 nodes access the darknet•Modified version of Nepenthes (a Malware collection framework) platform:

-- Mimics the replies generated by vulnerable services in order to collect the first stage exploit or shellcodes-- Generate URL that are to retrieve binaries

•Honeynet is used to compliment Nepenthes in order to catch exploits missed.

-- Honeypots are unpatched Windows XP VM’s-- Honeypots become infected and compared later to a clean Windows XP image. -- Infected Honey pots are also allowed to sustain IRC connections until VM gets reimaged

Methodology: Malware Collection Phase

Methodology: Data Collection Architecture

Methodology: Gateway

Darknet routing to various parts of the internal network Cross-infection prevention among honeypots

configuring honeypots in separate VLANSs Termination of traffic across VLANs and gateways Monitor and Analyze the malware traffic for infections Dynamic rule insertion

block further inbound attack traffic towards honeypot that is infected single malware instance honeypots due to lack of resources

Other funcitons Triggering re-imaging with clean Windows images pre-filtering and control during downloads local DNS to resolve queries

Methodology: Defense Points

With the methodology we now have the ability to model other types of bots.

Although methodology utilized Windows OS, we can model it for other platforms

The methodology analyzes all aspects of bots and botnets.

A multifaceted approach to understanding the Botnet Phenomenon

Results - I

Overall traffic27% of total traffic are from known botnet spreaders

73% of traffic includes traffic from unknown botnet spreaders

60% of malicious binaries were IRC bots

Only handful were HTTP based

Authors concerns about botnets spread are justifiable.

Traffic directed to vulnerable ports

76% of traffic targeted to vulnerable ports are from botnet spreaders

Malicious traffic to vulnerable ports cannot be differentiated between botnet and non-botnet traffic

How much of total traffic was directed to vulnerable ports is desired.

Peak traffics

90% of total traffic during the peak time targets ports used by botnet spreaders

70% of traffic during the peak time sent shell exploits similar to those sent by botnet spreaders.

Probed servers

11% of probed servers had at least one botnet activity

29% of probed .com servers had at least one cache hit

95% of probed .cn servers had at least one cache hit.

Probed Servers

At least one botnet activity

No botnet activity

Botnet Types

Total botnets captured 192

34 of 192 botnets captured were type I botnets (worm-like)

158 of them were type II

Botnets and Network types

When channel was set to topic

80% of targeted scanning was aimed at CLASS A networks

89% of localized scanning was aimed at CLASS B networks

When channel was set to botmaster commands

88% of targeted scanning was aimed at CLASS A networks

82% of localized scanning was aimed at CLASS B networks

DNS & IRC tracker views

Both DNS & IRC tracker views demonstrated three type of growth pattern:

semi exponential growth

Staircase type growth

Linear growth

Semi-exponential growth exhibited random scanning activity

Staircase type growth exhibited intermittent activity

Linear growth pattern exhibit time scoped activity

Key Points based on results

Botnets pose serious threats to the internet

Major contributor of unwanted traffic on the internet

IRC is the dominant protocol used in the Botnet communications

Botnets have achieved a high degree of sophistication in terms of self-protection mechanisms and modular package structures

Effective Botnet Sizes

Footprint Size vs. Effective Size• Significantly smaller• At most 3,000 bots online w/ networks of up to 10k bots

Smaller effective sizes limit certain activities:• Timely commands• DDoS attacks

Effective botnet sizes fluctuate with timezone changes

Lifetime

Botnets have relatively long lifetimes• Even after they’re shut down, live on average for 47 days• 84% of servers up longer than the 3 month survey• 55% of those botnets still scanning the Internet• If taken offline, able to be brought back online quickly

Bots do not stay long on IRC channels• Average time ~ 25 minutes• 90% stayed less than 50 minutes• High churn rate

Botmasters spend great lengths of time managing and monitoring their botnets

Botnet Software Dissection

49% disable firewall and anti-virus software

Many run inetd, which is used to identify the user of a computer. Used to verify bots joining an IRC channel

40% execute a System Security Monitor command, securing client machines from further exploitation

Average of 15 exploits per botnet binary -- bots can infect machines in a variety of ways

Windows XP constitutes 82.6% of observed exploited hosts, with 99% of those hosts running SP1 or less

Insight from an “Insider’s View”

Botmasters range in skill level

Botmasters:1. Share information about networks2. Tweak their bots to use the network efficiently3. Prune misbehaving bots and exploit “super-bots”

Botmasters are probably leasing their bots or attacking each other

Most commands (75%) are for control, scanning and cloning. 7% are for attacking.

Related Work

Honeynet group was the first to do an informal study Freiling et al. on countering certain classes of DDoS attacks Cooke et al. on prevalence of botnets by measuring elapsed

time before an un-patched system was infected by a botnet Barford et al. on an in-depth anaylsis on bot software

sourcecode Vrable et al. presented Potemkin, a scalable virtual honeynet

system Cui et al. presented RolePlayer—a protocol independent

lightweight responder that tries to overcome some of these limitations by reverting to a real server when the responder fails to produce the proper response

Dagon et al. provide an initial analytical model for capturing the spreading behavior of botnets.

Conclusion

Long presence and few formal studies One of the most severe threats to the Internet. Our knowledge of botnet behavior is incomplete To improve our understanding, we present a composite view Results show that botnets are a major contributor to the overall

unwanted traffic on the Internet Botnet scanning behavior is markedly different from that seen

by autonomous malware (e.g., worms) because of its manual orchestration

IRC is still the dominant protocol used for C&C communications Use is adapted to satisfy different botmasters’ needs Botnet footprints are usually much larger Graybox testing technique enabled us to understand the level ofsophistication reached by bot software today