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Network-level Malware Detection. Mike McNett, Matthew Spear, Richard Barnes CS-851 – Malware 23 October 2004. Outline. Introduction: Design of a System for Real-Time Worm Detection Example 1: Detecting Early Worm Propagation through Packet Matching (DEWP)

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network level malware detection

Network-level Malware Detection

Mike McNett, Matthew Spear, Richard Barnes

CS-851 – Malware

23 October 2004

outline
Outline
  • Introduction: Design of a System for Real-Time Worm Detection
  • Example 1: Detecting Early Worm Propagation through Packet Matching (DEWP)
  • Example 2: Fast Detection of Scanning Worm Infections
  • Example Application: Therminator
  • Conclusions
introduction
Introduction

Questions Being Considered:

  • Why network level detection?
  • What are the alternatives?
  • Are there reasonable solutions?
  • What are the limitations, advantages, disadvantages compared to the alternatives?
introduction1
Introduction
  • Malware Detection Options?
    • Prevention vs. Treatment
    • Signature vs. Anomaly
    • Host-based containment
    • Network containment
    • Packet Header vs. Packet Payload
  • What are the advantages, disadvantages, and limitations of the above?
design of a system for real time worm detection
Design of a System for Real-Time Worm Detection
  • Hash
  • Count Vector
  • Character Filter
  • SRAM Analyzer
  • Alert Generator
  • Periodic Subtraction of Time Averages
design of a system for real time worm detection1
Design of a System for Real-Time Worm Detection
  • Scalable to high throughput
  • Solution depends on specialized hardware
  • Low false positive rate
  • What are the problems?
  • What are the advantages?
  • Are there other, more simplistic signatures?
  • Can similar attacks be detected at the host level?
slide8

Detecting Early Worm Propagation through Packet MatchingXuan Chen and John Heidemann ISI-TR-2004-585February 2004

slide9
DEWP
  • Router-based system:
    • automatically detects and quarantines Internet worm propagation
    • matches destination port numbers between incoming and outgoing connections (automated signature creation)
    • detects and suppresses worms due to unusual traffic patterns
    • detects worm propagation within about 4 seconds
    • protects > 99% hosts from random-scanning worms
dewp thesis
DEWP Thesis
  • Matches destination port numbers between incoming and outgoing connections. Two observations on worm traffic:
    • Worms usually exploit vulnerabilities related to specific network port numbers
    • Infected hosts will probe other vulnerable hosts exploiting the same vulnerability
  • So… high levels of bi-directional probing traffic with the same destination port number new worm
  • Scalable: Matching destination port numbers consumes low computational power
slide11
DEWP
  • Two components of DEWP: worm detector and packet filter
  • Two step detection: destination port matching and destination address counting
  • Uses packet filtering to suppress worm spreading
  • Can deploy at different levels of network
worm containment
Worm Containment
  • DEWP uses traffic filtering – routers drop packets with the automatically discovered destination port
  • Worm containment: protect internal hosts from internal and external threats; notify other networks about attacks
design
Design
  • Maintains one port-list for each direction (incoming and outgoing): records number of connections to different destination ports
  • Timer for each entry in port-lists:
    • If port has not been accessed for certain time interval, reset corresponding list entry
    • Monitor outgoing destination addresses of non-zero entries in both port-lists
    • Every T seconds, check number of unique addresses observed within last time interval. Worm traffic detected with the following condition:
  • Nis the number of unique addresses observed.
  • Long-term average:
  •  is the system sensitivity to changes
effectiveness of worm detection and quarantine
Effectiveness of Worm Detection and Quarantine
  • Random scanning worm: detects worm traffic in 4.8 seconds when fully deployed with a 1 second detection interval.
  • Always detects worm probing traffic in 4-5 seconds when deployed to different layers.
  • Number of infected hosts in the protected network – primarily determined by the number of probing packets received from outside
  • Can protect almost all hosts from infection when only deployed on the access router.
local scanning
Local Scanning
  • Local scanning: Can detect worm probing traffic in 3.87 seconds. But, almost all vulnerable hosts in the protected network are compromised
  • Deployment has little impact on either detection delay or infection percentage.
  • The infection percentage increases as number DEWP deployed layers are reduced: When only on the access router  all vulnerable hosts compromised within 10 seconds
  • More frequent detection reduces vulnerability to local-scanning worms
  • DEWP quickly detects worm attacks regardless probing techniques.
  • With full deployment about 9% vulnerable hosts compromised in the protected network
  • Due to difficulty to effectively quarantine local-scanning worms  a very small detection interval and wide deployment is critical to protect vulnerable hosts
effect of detection intervals
Effect of Detection Intervals
  • Address-counting with an interval of T seconds.
  • Different detection intervals affect detection delay and infection percentage
  • Random-scanning worm. Detection delay and the number of infected hosts increases with detection intervals.
  • Local-scanning worms: 1) No significant difference in detection delay; 2) Infection percentage increases dramatically at larger intervals:
  • So, automatic system needs to react to worm traffic within small time intervals
false detections
False Detections
  • No false positives
  • Discovered ~10 suspicious destination ports including 21 (FTP), 53 (DNS), and 80 (Web)
  • Depends on address-counting to reduce false positives
  • Worm scan rate C affects false negatives: when worm scan at low rate, probing traffic has less effect on overall traffic. DEWP routers have more difficulty distinguishing them from normal traffic.
  • With C = 500  worm traffic stands out compared to regular traffic
  • DEWP is not able to detect worms with scanning rate lower than C = 25.
conclusions
Conclusions
  • Detects and quarantines propagation of Internet worms
  • Uses port-matching and address-counting as the signature.
  • Detects worm attack within 4-5 seconds
  • By automatically blocking worm traffic, it protects most vulnerable hosts from random-scanning worms.
  • Authors believe that an automatic worm detection and containment system should be widely deployed and have very small detection intervals
  • Not realistic to deploy DEWP on all routers – for random scanning worms – sufficient to put on access router.
slide19

Worm Detection

Fast Detection of Scanning Worm Infection

detection techniques
Detection Techniques
  • Reverse Sequential Hypothesis Testing (TH)
    • Detects worms based upon number of failed connection attempts
    • Uses probability to determine if a local host is scanning
    • Designed to be tied into a containment system
  • Signature Based Analysis (Early Bird System (EBS))
    • Detects worms based upon Rabin signatures of content/port
    • Used in conjunction with a containment system
basic algorithm
Basic Algorithm
  • Maintain separate state information for each host (l) being monitored ( ), the hosts that have been previously contacted, and an FCC queue (FCCQ) of first contact attempts that have been attempted but have not been recorded in the observation (PCH).
  • When a packet is observed check to see if d is in the PCH ofl, if not then add d PCH andadd the attempt to FCCQ as PENDING.
  • When an incoming packet is sent to l and the source address exists in FCCQ update the record to SUCCESS in the FCCQ unless the packet is a TCP RST.
  • When the head entry of FCCQ has status of PENDING and has been in queue for longer than a predefined time limit set its status to FAILURE.
    • If the entry at the head of FCCQ has status other than PENDING update and compare it to η1
basic algorithm1
Basic Algorithm

Credit Based Connection Rate Limiting

(CBCRL)

  • Simple scheme to limit the amount of connections l can make in a given slot of time by allotting each l a set number of credits (Cl) that is modified given events.
  • Used in conjunction with TH to limit number of connections a host can make allowing TH time to determine if a host is infected.
experiment
Experiment
  • Conducted two experiments in 2003 (isp-2003) and 2004 (isp-04).
  • Worms identified via comparing traffic to known worm descriptions.
limitations future work
Limitations, Future Work?

Are there any serious flaws in this algorithm?

Future work?

  • Warhol type scanning
  • Network outages can cause TH to decide that a host is a worm
  • Worms could conceivably collaborate to defy detection
  • Worms could remember hosts that it can contact and defy detection through them
  • Spoofing attack to get an uninfected host blocked
  • Interleave scanning with benign activities (i.e. for every scan visit a website that is known to be running)
  • Can trivially modify to work with the containment strategies discussed earlier
therminator
THERMINATOR!!!

Science comes to the aid of network-level anomaly detection

network behavior is complicated
Network behavior is complicated
  • How do we use “microscopic” packet-level data to make “macro” network-level decisions?
    • Too broad, e.g. keeping track of global traffic patterns.
    • Too refined, e.g. looking at individual packets.
  • Hmm… who else tries to make sense of the overall behavior of millions of single objects?
  • Physicists and Chemists!
slide30
Idea
  • Given a computer network with >1000 nodes,
  • Want to detect anomalous traffic, without any foreknowledge.
  • Idea of THERMINATOR
    • Take advantage of lots of packet-level data.
    • Use physical techniques to distill information into relevant statistics: Temperature, entropy, etc.
data reduction
Data Reduction
  • Take the set of hosts and group them into “buckets” or “conversation groups”.
  • Observe communication among buckets.
  • Calculate physical statistics based on these higher-level communications.
  • By virtue of the mathematics, these are guaranteed to be the same as if we’d just looked at hosts.
physical network visualization
Physical Network Visualization
  • Based on reduced data, we know pseudo-physical statistics:
    • Bucket size
    • Temperature
    • Entropy
    • Heating rate
    • Work rate
  • Visualizing these data shows network events.

Image courtesy of DISA

therminator implementation
THERMINATOR Implementation
  • Jointly developed by DISA, NSA, and Lancope Inc.
  • Uses Lancope’s data-collection hardware to provide data to THERMINATOR.
  • THERMINATOR reduces data, computes stats, and provides visualization.
  • “Research tests validated that THERMINATOR detected anomalies that the intrusion detection systems did not capture.” -- NSA
conclusion
Conclusion
  • Combined approaches (host-based, network-based, visualization)?
  • Can signatures be automatically generated?
  • Can attacks be visualized?
  • Potential impacts of false positives (is the medicine worse than the sickness) and automated containment?
  • Need different solutions for local-scanning vs. non-local scanning worms?
  • Are there other scientific areas that malware research can leverage?