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SpyProxy : Execution-based Detection of Malicious Web Content

SpyProxy : Execution-based Detection of Malicious Web Content. 1 Intro. C an execution-based analysis successfully detect today’s malware threats? C an the analysis be performed without harming browser responsiveness?

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SpyProxy : Execution-based Detection of Malicious Web Content

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  1. SpyProxy: Execution-based Detection of Malicious Web Content

  2. 1 Intro • Can execution-based analysis successfully detect today’s malware threats? • Can the analysis be performed without harming browser responsiveness? • What are the limitations of this approach, in particular in the face of complex, adversarial scripts that contain non-determinism?

  3. 2 Architecture and Implementation • Defending Against Modern Web Threats • Design Goals • Proxy-based Architecture • Static Analysis • Any pages with active content must be analyzed dynamically • Execution-based Analysis through VM-based Page Rendering

  4. 2.4 Limitations • Non-determinism • client-side non-determinism • the VM worker will see a benign ad while the client will see a malicious ad. • Termination • Differences Between the Proxy and Client

  5. 3Performance Optimizations • Caching the Result of Page Checks • Squid Proxy • Identify each Object • Prefetching Content to the Client • The Staged Release of Content

  6. 3.4Additional optimizations • keep a browser process already running inside • Avoid set-up time • virtual disk backing the VM worker is stored in a RAM-disk file system in the host OS • Eliminating the disk traffic associated with storing cookies or files in the VM browser. • we re-use VM workers across requests, garbage collecting them only after a trigger fires or a configurable number of requests has occurred. • 50 requests • How to collect the garbage? Which issues will be regarded as garbage?

  7. 4 Evaluation • 3 key questions • how effective is our system at detecting and blocking malicious Web content • how well do our performance optimizations mask latency from the user • how well does our system perform given a realistic workload

  8. 4.1 Effectiveness at Blocking Malicious Code • Base: a list of 100 malicious Web pages on 45 distinct sites. • Result: 100% of the malicious Web pages is blocked • Question: False Positive?没有检测

  9. 4.2 Performance of the Unoptimized System • Timeline : the latency of each step from the client’s Web page request to the final page rendering in the client.

  10. 4.3 Performance Optimizations

  11. 4.4 Performance on a Realistic Workload • Base: 1909 requests generated by 703 different safe URLs from 124 different sites. Selecting a range of popular and unpopular sites ranked by the Alexa ranking service. • very few false positives: only 4 of the 1,909 • However, we do “reduce”the number of false positives by including the most common browser plug-ins, such as Flash, in the base VM image. • Average: 600ms

  12. 4.5 Scalability • Assumption: the CPU is likely to be the bottleneck of a deployed system • the system must be configured with an adequate amount of memory and network bandwidth to support the required concurrent virtual machines and Web traffic. • a single-CPU SpyProxy could support approximately 822 users. • A single quad-core machine should be able to handle the load from an organization containing a few thousand people.

  13. 5 Related Work • 5.1 Spyware and Malware Detection • Passive network monitor to measure adware on the campus of University of Washington • Crawling to find and analyze executable programs and Web pages that lead to spyware infections • Strider HoneyMonkey and SiteAdvisor • Not transparent rather than a measurement tool • Gatekeeper • only look for malware that is already installed

  14. 5.1Spyware and Malware Detection • Semantics-aware malware detection & automatic generation of signatures for detection of unknown malware variants • Not prevention • Prevx1 and Primary Response SafeConnect • must run on systems packed with client-installed programs • VM isolation, OS-level sandboxing, or logging/ rollback • Other challenges: data sharing and client-side performance overhead

  15. 5.2Intrusion Detection and Firewalls • Intrusion detection systems (e.g., Bro and Snort) • cannot detect new or otherwise undiscovered attacks. • attacks are detected but not prevented • Shadow honeypots • route risky network traffic to a heavily instrumented version of a vulnerable application • SpyProxy does not need to instrument the Web browser that it guards, and its run-time checks are more general and easier to define

  16. 5.3 Proxies • SpyProxy uses execution-based analysis to identify malicious content

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