Virtualized In-Cloud Security Services for Mobile Devices
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1 Virtualized In-Cloud Security Services for Mobile Devices Jon Oberheide, Kaushik Veeraraghavan, Evan Cooke, Jason Flinn, Farnam Jahanian University of Michigan June 17, 2008 MobiVirt '08
2 Roadmap Motivation Malware detection: high security, low resources Architecture Antivirus as an off-device network service Implementation and Evaluation Resource requirements Power consumption Detection capabilities Discussion and Wrap-up Slide #2 Jon Oberheide - MobiVirt - June, 2008
3 Motivation Mobile device capabilities increasing Approaching functionality of standard PCs Rich application development/delivery iphone, Android, Maemo, Symbian, WM, etc Enticing target for malware authors Mobile banking transactions Spying on business/enterprise users Need malware detection/mitigation! Slide #3 Jon Oberheide - MobiVirt - June, 2008
4 Current Approach Adapt host-based antivirus to mobile devices Numerous vendors have mobile products Problems with on-device AV model Detection capabilities vs. resource constraints Scalability protection for future threats Software complexity, platform diversity, AV vulnerabilities Goal: maintain/increase detection capabilities while reducing resource requirements Slide #4 Jon Oberheide - MobiVirt - June, 2008
5 New Approach Offload detection functionality Instead of analyzing a file locally on-device, send it to a network service for analysis Moves complexity and resource usage to off-device service Frees us of resource constraints of mobile device Trade-off network bandwidth/radio power to save on-device resources Slide #5 Jon Oberheide - MobiVirt - June, 2008
6 CloudAV Architecture Network Service Separating acquisition from analysis Host Agent Host/mobile agent runs on desktops, laptops, and mobile devices. Acts similar to host-based AV; interposes on file access Queries network service instead of analyzing locally Network service hosts the backend AV detection engines and fields file analysis requests from the host agent. Slide #6 Jon Oberheide - MobiVirt - June, 2008
7 Architecture Components Lightweight Mobile Agent Low resource requirements Easy to port to new platforms Simple code base reduced vuln footprint Network Service Can employ multiple detection engine in parallel Central management of AV signature updates More resource intensive and complex detection techniques (eg. behavioral detection engines) Slide #7 Jon Oberheide - MobiVirt - June, 2008
8 Advantages of Virtualization Network service backend Hosts detection engines in virtualized environments Scalability Dynamically spin up/down instances Isolation/Recovery AV engine vulnerabilities Restore to clean snapshot when compromise detected Slide #8 Jon Oberheide - MobiVirt - June, 2008
9 Caching, Caching, Caching Reducing network activity is desirable Transferring candidate file to network service for analysis on every access is infeasible Remote cache Shared between all users of network service Eliminate duplicate file transfer and analysis: Alice runs App1, App1 analyzed; Bob runs App1, remote cache hit! Local cache Stored on mobile device Eliminate unnecessary remote cache queries Bob runs App1, remote cache hit; Bob runs App1 again, local cache hit! Slide #9 Jon Oberheide - MobiVirt - June, 2008
10 Implementation CloudAV Host Agent Numerous platforms: Win32, BSD, Milter frontends Network Service: 10 antivirus engines: Avast, AVG, BitDefender, ClamAV, F-Prot, F-Secure, Kaspersky, McAfee, Symantec, and Trend Micro 2 behavioral engines Norman Sandbox, CWSandbox Slide #10 Jon Oberheide - MobiVirt - June, 2008
11 Implementation Mobile Extensions Nokia Maemo platform N770, N800, N810 devices Python, < 300 LOC, Dazuko syscall hooking Mobile-specific behavioral engine Runtime behavioral analysis of suspected malware Virtualized Maemo environment Monitors syscalls and D-Bus IPC to detect: Modification/destruction of personal user data Network communications to untrusted parties Skype VoIP calls to unauthorized numbers Slide #11 Jon Oberheide - MobiVirt - June, 2008
12 Evaluation Resource macro-benchmark Nokia N800 ClamAV vs. Mobile Agent (MA) Resource consumption (CPU/memory) Power consumption micro-benchmark Nokia N95 Kaspersky Mobile vs. Mobile Agent (MA) Power consumption of radios (WiFi/EDGE) Mobile agent cache states CL+CR, CL+WR, WL+WR Slide #12 Jon Oberheide - MobiVirt - June, 2008
13 Computational Resources Simulated real-world usage benchmark 5 common applications: web browser, IM client, VoIP client, media player, and PDF viewer Agent Startup Time Avg/Peak Memory User/Total Jiffies ClamAV 57 sec KB / KB / MA-CL+CR 0.2 sec 1502 KB / 2154 KB 1502 / 2185 MA-CL+WR 0.2 sec 1486 KB / 2124 KB 1486 / 1854 MA-WL+WR 0.2 sec 1189 KB / 1812 KB 1189 / 1714 Order of magnitude decrease in CPU and memory resources in all mobile agent cache states. Slide #13 Jon Oberheide - MobiVirt - June, 2008
14 Power Consumption Local Kaspersky vs. Mobile Agent Simple scan of ~25M of third-party apps and games Agent Avg Energy Peak Energy Total Energy None (Baseline) 0.36 W 0.63 W 43.2 W Kaspersky 0.86 W 1.27 W 89.4W MA-CL+CR (EDGE) 1.51 W 2.31 W W MA-CL+CR (WiFi) 1.31 W 2.44 W W MA-CL+WR (EDGE) 1.22 W 2.13 W W MA-CL+WR (WiFi) 0.92 W 1.83 W 74.5 W MA-WL+WR 0.82 W 1.20 W 59.5 W Decrease in power consumption in common mobile agent cache states. Slide #14 Jon Oberheide - MobiVirt - June, 2008
15 Scalability of Detection Capabilities Current state of mobile device malware Presently not a large number of threats to protect against Host-based AV model Resource/power consumption of on-device software scales with complexity/number of threats Detection Engine Symantec Mobile Kaspersky Mobile ClamAV Cloud AV / Mobile Agent CloudAV model Signature Database Size 27 signatures 284 signatures 262,289 signatures > 5 million sigs + behavioral Resource/power consumption stays static and independent of increase in threats Slide #15 Jon Oberheide - MobiVirt - June, 2008
16 Detection Coverage N-Version Protection The use of multiple detection engines in parallel from independent vendors Desktop malware results Subset of malware from Arbor Malware Library Engine Combination Detected Coverage CM 229/ % CM, SM 290/ % CM, SM, MA 358/ % CM, SM, MA, BD 417/ % CM, SM, MA, BD, FS 430/ % CM: ClamAV SM: Symantec MA: McAfee BD: BitDefender FS: F-Secure Transparent engine extensibility Slide #16 Jon Oberheide - MobiVirt - June, 2008
17 Management Capabilities Web interface: Forensics Drilldown Policy Enforcement Flexible Alerting Report Generation VM Monitoring: Real-time System Status Xen VM Management Visualization Eye-Candy! Slide #17 Jon Oberheide - MobiVirt - June, 2008
18 Limitations Disconnected operation Local caching mechanisms Limited app/content acquisition while disconnected Security policy decision Privacy concerns Tunable collection/display built into architecture User awareness of operation Slide #18 Jon Oberheide - MobiVirt - June, 2008
19 Question and Answer Questions? Contact information Jon Oberheide University of Michigan Slide #19 Jon Oberheide - MobiVirt - June, 2008
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