DoS Attacks on Your Memory in Cloud

In cloud computing, network Denial of Service (DoS) attacks are well studied and defenses have been implemented, but severe DoS attacks on a victim's working memory by a single hostile VM are not well understood. Memory DoS attacks are Denial of Service (or Degradation of Service) attacks caused by contention for hardware memory resources on a cloud server. Despite the strong memory isolation techniques for virtual machines (VMs) enforced by the software virtualization layer in cloud servers, the underlying hardware memory layers are still shared by the VMs and can be exploited by a clever attacker in a hostile VM co-located on the same server as the victim VM, denying the victim the working memory he needs. We first show quantitatively the severity of contention on different memory resources. We then show that a malicious cloud customer can mount low-cost attacks to cause severe performance degradation for a Hadoop distributed application, and 38X delay in response time for an E-commerce website in the Amazon EC2 cloud. Then, we design an effective, new defense against these memory DoS attacks, using a statistical metric to detect their existence and execution throttling to mitigate the attack damage. We achieve this by a novel re-purposing of existing hardware performance counters and duty cycle modulation for security, rather than for improving performance or power consumption. We implement a full prototype on the OpenStack cloud system. Our evaluations show that this defense system can effectively defeat memory DoS attacks with negligible performance overhead.

[1]  Harkeerat Singh Bedi,et al.  Securing cloud infrastructure against co-resident DoS attacks using game theoretic defense mechanisms , 2012, ICACCI '12.

[2]  Ricardo Bianchini,et al.  DeepDive: Transparently Identifying and Managing Performance Interference in Virtualized Environments , 2013, USENIX Annual Technical Conference.

[3]  F. Massey The Kolmogorov-Smirnov Test for Goodness of Fit , 1951 .

[4]  Taesoo Kim,et al.  STEALTHMEM: System-Level Protection Against Cache-Based Side Channel Attacks in the Cloud , 2012, USENIX Security Symposium.

[5]  Michael K. Reiter,et al.  Cross-VM side channels and their use to extract private keys , 2012, CCS.

[6]  Sai Prashanth Muralidhara,et al.  Reducing memory interference in multicore systems via application-aware memory channel partitioning , 2011, 2011 44th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).

[7]  Lingjia Tang,et al.  SMiTe: Precise QoS Prediction on Real-System SMT Processors to Improve Utilization in Warehouse Scale Computers , 2014, 2014 47th Annual IEEE/ACM International Symposium on Microarchitecture.

[8]  Benjamin Farley,et al.  Resource-freeing attacks: improve your cloud performance (at your neighbor's expense) , 2012, CCS.

[9]  Christina Delimitrou,et al.  Paragon: QoS-aware scheduling for heterogeneous datacenters , 2013, ASPLOS '13.

[10]  Huan Liu,et al.  A new form of DOS attack in a cloud and its avoidance mechanism , 2010, CCSW '10.

[11]  References , 1971 .

[12]  Stephen D. Wolthusen,et al.  Robust Coordination of Cloud-Internal Denial of Service Attacks , 2013, 2013 International Conference on Cloud and Green Computing.

[13]  David A. Patterson,et al.  A hardware evaluation of cache partitioning to improve utilization and energy-efficiency while preserving responsiveness , 2013, ISCA.

[14]  Ruby B. Lee,et al.  CloudMonatt: An architecture for security health monitoring and attestation of virtual machines in cloud computing , 2015, 2015 ACM/IEEE 42nd Annual International Symposium on Computer Architecture (ISCA).

[15]  Patrick P. C. Lee,et al.  An experimental study of cascading performance interference in a virtualized environment , 2013, PERV.

[16]  Ruby B. Lee,et al.  A Framework for Realizing Security on Demand in Cloud Computing , 2013, 2013 IEEE 5th International Conference on Cloud Computing Technology and Science.

[17]  O. Mutlu,et al.  Fairness via source throttling: a configurable and high-performance fairness substrate for multi-core memory systems , 2010, ASPLOS XV.

[18]  Xiao Zhang,et al.  CPI2: CPU performance isolation for shared compute clusters , 2013, EuroSys '13.

[19]  Dirk Grunwald,et al.  Microarchitectural denial of service: insuring microarchitectural fairness , 2002, MICRO.

[20]  Peter Desnoyers,et al.  Scheduler Vulnerabilities and Coordinated Attacks in Cloud Computing , 2011, 2011 IEEE 10th International Symposium on Network Computing and Applications.

[21]  Zhenyu Wu,et al.  Whispers in the Hyper-space: High-speed Covert Channel Attacks in the Cloud , 2012, USENIX Security Symposium.

[22]  Zhenyu Wu,et al.  A Measurement Study on Co-residence Threat inside the Cloud , 2015, USENIX Security Symposium.

[23]  Jack Sampson,et al.  Quality Time: A simple online technique for quantifying multicore execution efficiency , 2014, 2014 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS).

[24]  Hovav Shacham,et al.  Hey, you, get off of my cloud: exploring information leakage in third-party compute clouds , 2009, CCS.

[25]  Michael M. Swift,et al.  A Placement Vulnerability Study in Multi-Tenant Public Clouds , 2015, USENIX Security Symposium.

[26]  Lingjia Tang,et al.  Bubble-flux: precise online QoS management for increased utilization in warehouse scale computers , 2013, ISCA.

[27]  Alexandra Fedorova,et al.  Addressing shared resource contention in multicore processors via scheduling , 2010, ASPLOS XV.

[28]  No License,et al.  Intel ® 64 and IA-32 Architectures Software Developer ’ s Manual Volume 3 A : System Programming Guide , Part 1 , 2006 .

[29]  Michael K. Reiter,et al.  Cross-Tenant Side-Channel Attacks in PaaS Clouds , 2014, CCS.

[30]  Xiao Zhang,et al.  Hardware Execution Throttling for Multi-core Resource Management , 2009, USENIX Annual Technical Conference.

[31]  Hsien-Hsin S. Lee,et al.  Analyzing Performance Vulnerability due to Resource Denial›of›Service Attack on Chip Multiprocessors , 2007 .

[32]  Eduard Ayguadé,et al.  Non-intrusive Estimation of QoS Degradation Impact on E-Commerce User Satisfaction , 2011, 2011 IEEE 10th International Symposium on Network Computing and Applications.

[33]  Gernot Heiser,et al.  Last-Level Cache Side-Channel Attacks are Practical , 2015, 2015 IEEE Symposium on Security and Privacy.

[34]  Matti A. Hiltunen,et al.  An exploration of L2 cache covert channels in virtualized environments , 2011, CCSW '11.

[35]  Onur Mutlu,et al.  Memory Performance Attacks: Denial of Memory Service in Multi-Core Systems , 2007, USENIX Security Symposium.

[36]  Jaehyuk Huh,et al.  Dynamic Virtual Machine Scheduling in Clouds for Architectural Shared Resources , 2012, HotCloud.