Enabling Worm and Malware Investigation Using Virtualization (Demo and poster this afternoon) Dongyan Xu, Xuxian Jiang CERIAS and Department of Computer Science Purdue University

The Team  Lab FRIENDS  Xuxian Jiang (Ph.D. student)  Paul Ruth (Ph.D. student)  Dongyan Xu (faculty)  CERIAS  Eugene H. Spafford  External Collaboration  Microsoft Research

Our Goal In-depth understanding of increasingly sophisticated worm/malware behavior

Outline  Motivation  An integrated approach  Front-end : Collapsar (Part I)  Back-end : vGround (Part II)  Bringing them together  On-going work

The Big Picture Proxy ARP Domain A Domain B GRE Worm Analysis Worm Capture

Front-End: Collapsar Enabling Worm/Malware Capture * X. Jiang, D. Xu, “Collapsar: a VM-Based Architecture for Network Attack Detention Center”, 13 th USENIX Security Symposium (Security’04), Part I

General Approach  Promise of honeypots  Providing insights into intruders’ motivations, tactics, and tools  Highly concentrated datasets w/ low noise  Low false-positive and false negative rate  Discovering unknown vulnerabilities/exploitations  Example: CERT advisory CA (solaris CDE subprocess control daemon – dtspcd)

Current Honeypot Operation  Individual honeypots  Limited local view of attacks  Federation of distributed honeypots  Deploying honeypots in different networks  Exchanging logs and alerts  Problems  Difficulties in distributed management  Lack of honeypot expertise  Inconsistency in security and management policies  Example: log format, sharing policy, exchange frequency

Our Approach: Collapsar  Based on the HoneyFarm idea of Lance Spitzner  Achieving two (seemingly) conflicting goals  Distributed honeypot presence  Centralized honeypot operation  Key ideas  Leveraging unused IP addresses in each network  Diverting corresponding traffic to a “detention” center (transparently)  Creating VM-based honeypots in the center

VM-based Honeypot Collapsar Architecture Redirector Correlation Engine Management Station Production Network Collapsar Center Attacker Front-End

Comparison with Current Approaches  Overlay-based approach (e.g., NetBait, Domino overlay)  Honeypots deployed in different sites  Logs aggregated from distributed honeypots  Data mining performed on aggregated log information  Key difference: where the attacks take place (on-site vs. off-site)

Comparison with Current Approaches  Sinkhole networking approach (e.g., iSink )  “Dark” space to monitor Internet abnormality and commotion (e.g. msblaster worms)  Limited interaction for better scalability  Key difference: contiguous large address blocks (vs. scattered addresses)

Comparison with Current Approaches  Low-interaction approach (e.g., honeyd, iSink )  Highly scalable deployment  Low security risks  Key difference: emulated services (vs. real things)  Less effective to reveal unknown vulnerabilities  Less effective to capture 0-day worms

Collapsar Design  Functional components  Redirector  Collapsar Front-End  Virtual honeypots  Assurance modules  Logging module  Tarpitting module  Correlation module

Collapsar Deployment  Deployed in a local environment for a two-month period in 2003  Traffic redirected from five networks  Three wired LANs  One wireless LAN  One DSL network  ~ 50 honeypots analyzed so far  Internet worms (MSBlaster, Enbiei, Nachi )  Interactive intrusions (Apache, Samba)  OS: Windows, Linux, Solaris, FreeBSD

Incident: Apache Honeypot/VMware  Vulnerabilities  Vul 1: Apache (CERT® CA )  Vul 2: Ptrace (CERT® VU )  Time-line  Deployed: 23:44:03pm, 11/24/03  Compromised: 09:33:55am, 11/25/03  Attack monitoring  Detailed log 

Incident: Windows XP Honeypot/VMware  Vulnerability  RPC DCOM Vul. (Microsoft Security Bulletin MS03-026)  Time-line  Deployed: 22:10:00pm, 11/26/03  MSBlaster: 00:36:47am, 11/27/03  Enbiei: 01:48:57am, 11/27/03  Nachi: 07:03:55am, 11/27/03

Summary (Front-End)  A novel front-end for worm/malware capture  Distributed presence and centralized operation of honeypots  Good potential in attack correlation and log mining  Unique features  Aggregation of Scattered unused (dark) IP addresses  Off-site (relative to participating networks) attack occurrences and monitoring  Real services for unknown vulnerability revelation

Back-End: vGround Enabling Worm/Malware Analysis Part II * X. Jiang, D. Xu, H. J. Wang, E. H. Spafford, “Virtual Playgrounds for Worm Behavior Investigation”, 8 th International Symposium on Recent Advances in Intrusion Detection (RAID’05), 2005.

Basic Approach  A dedicated testbed  Internet-inna-box (IBM), Blended Threat Lab (Symantec)  DETER  Goal: understanding worm behavior  Static analysis/ execution trace  Reverse Engineering ( IDA Pro, GDB, … )  Worm experiment within a limited scale  Result:  Only enabling relatively static analysis within a small scale

The Reality – Worm Threats  Speed, Virulence, & Sophistication of Worms  Flash/Warhol Worms  Polymorphic/Metamorphic Appearances  Zombie Networks (DDoS Attacks, Spam)  What we also need  A high-fidelity, large-scale, live but safe worm playground

Picture by Peter Szor, Symantec Corp. A Worm Playground

Requirements  Cost & Scalability  How about a topology with nodes?  Confinement  In-house private use?  Management & user convenience  Diverse environment requirement  Recovery from damages from a worm experiment  re-installation, re-configuration, and reboot …

Our Approach  vGround  A virtualization-based approach  Virtual Entities:  Leveraging current virtual machine techniques  Designing new virtual networking techniques  User Configurability  Customizing every node (end-hosts/routers)  Enabling flexible experimental topologies

An Example Run: Internet Worms A shared infrastructure (e.g. PlanetLab) A worm playground Virtual Physical

Key Virtualization Techniques  Full-System Virtualization  Network Virtualization

Full-System Virtualization  Emerging and New VM Techniques  VMware, Xen, Denali, UML  Supporting for real-world services  DNS, Sendmail, Apache w/ “native” vulnerabilities  Adopted technique: UML  Deployability  Convenience/Resource Efficiency

User-Mode Linux ( )  System-Call Virtualization  User-Level Implementation Host OS Kernel Device Drivers Hardware Device DriversMMU Guest OS Kernel UM User Process 1 ptraceptrace UM User Process 2

New Network Virtualization  Link Layer Virtualization  User-Level Implementation Host OS Virtual Node 1Virtual Node 2 Virtual Switch 1 IP-IP

User Configurability  Node Customization  System Template  End Node ( BIND, Apach, Sendmail, … )  Router ( RIP, OSPF, BGP, … )  Firewall ( iptables )  Sniffer/IDS ( bro, snort )  Topology Customization  Language  Network, Node  Toolkits

Project Planetlab-Worm template slapper { image slapper.ext2 cow enabled startup { /etc/rc.d/init.d/httpd start } template router { image router.ext2 routing ospf startup { /etc/rc.d/init.d/ospfd start } router R1 { superclass router network eth0 { switch AS1_lan1 address /24 } network eth1 { switch AS1_AS2 address /24 } switch AS1_lan1 { unix_sock sock/as1_lan1 host planetlab6.millennium. berkeley.edu } switch AS1_AS2 { udp_sock 1500 host planetlab6.millennium. berkeley.edu } node AS1_H1 { superclass slapper network eth0 { switch AS1_lan1 address /24 gateway } node AS1_H2 { superclass slapper network eth0 { switch AS1_lan1 address /24 gateway } switch AS2_lan1 { unix_sock sock/as2_lan1 host planetlab1.cs.purdue.edu } switch AS2_AS3 { udp_sock 1500 host planetlab1.cs.purdue.edu } node AS2_H1 { superclass slapper network eth0 { switch AS2_lan1 address /24 gateway } node AS2_H2 { superclass slapper network eth0 { switch AS2_lan1 address /24 gateway } switch AS3_lan1 { unix_sock sock/as3_lan1 host planetlab8.lcs.mit.edu } router R2 { superclass router network eth0 { switch AS2_lan1 address /24 } network eth1 { switch AS1_AS2 address /24 } network eth2 { switch AS2_AS3 address /24 } node AS3_H1 { superclass slapper network eth0 { switch AS3_lan1 address /24 gateway } node AS3_H2 { superclass slapper network eth0 { switch AS3_lan1 address /24 gateway } router R3 { superclass router network eth0 { switch AS3_lan1 address /24 } network eth1 { switch AS2_AS3 address /24 } Networked Node Network System Template AS1_H1R1 AS1_H2 AS2_H1AS2_H2 R2R3 AS3_H1 AS3_H2

Features  Scalability  3000 virtual hosts in 10 physical nodes  Iterative Experiment Convenience  Virtual node generation time: 60 seconds  Boot-strap time: 90 seconds  Tear-down time: 10 seconds  Strict Confinement  High Fidelity

Evaluation  Current Focus  Worm behavior reproduction  Experiments  Probing, exploitation, payloads, and propagation  Further Potentials – on-going work  Routing worms / Stealthy worms  Infrastructure security (BGP)

Experiment Setup  Two Real-World Worms  Lion, Slapper, and their variants LionSlapper  A vGround Topology  10 virtual networks  1500 virtual Nodes  10 physical machines in an ITaP cluster

Evaluation  Target Host Distribution  Detailed Exploitation Steps  Malicious Payloads  Propagation Pattern

Probing: Target Network Selection Lion Worms Slapper Worms ,81

Exploitation (Lion) 1: Probing 2: Exploitation! 3: Propagation!

Exploitation (Slapper) 1: Probing 2: Exploitation! 3: Propagation!

Malicious Payload (Lion)

Propagation Pattern and Strategy  Address-Sweeping  Randomly choose a Class B address (a.b.0.0)  Sequentially scan hosts a.b.0.0 – a.b  Island-Hopping  Local subnet preference

Propagation Pattern and Strategy  Address-Sweeping (Slapper Worm) Infected Hosts: 2% Infected Hosts: 5% Infected Hosts: 10% a.b

Propagation Pattern and Strategy  Island-Hopping Infected Hosts: 2% Infected Hosts: 5% Infected Hosts: 10%

Summary (Back-End)  vGround – the back-end  A Virtualization-Based Worm Playground  Properties:  High Fidelity  Strict Confinement  Good Scalability 3000 Virtual Hosts in 10 Physical Nodes  High Resource Efficiency  Flexible and Efficient Worm Experiment Control

Combining Collapsar and vGround Domain A Domain B GRE Worm Analysis Worm Capture

Conclusions  An integrated virtualization-based platform for worm and malware investigation  Front-end : Collapsar  Back-end : vGround  Great potential for automatic  Characterization of unknown service vulnerabilities  Generation of 0-day worm signatures  Tracking of worm contaminations

On-going Work  More real-world evaluation  Stealthy worms  Polymorphic worms  Additional capabilities  Collapsar center federation  On-demand honeypot customization  Worm/malware contamination tracking  Automated signature generation

Thank you. Stop by our poster and demo this afternoon! For more information: URL: Google: “Purdue Collapsar Friends”