ESF: AN ELASTIC SECURITY FRAMEWORK FOR CLOUD INFRASTRUCTURES

Transcription

1 M. Pourzandi 1 ESF: AN ELASTIC SECURITY FRAMEWORK FOR CLOUD INFRASTRUCTURES Makan Pourzandi Ericsson Cloud System Management, Affiliated Associate Professor Concordia University Apr 2013

2 M. Pourzandi 2 Plan Background Elastic Security Framework Elastic Enforcement Layer Security Enforcement Optimization

3 Contributions Publications: 16 patent applications issued by US and European patent offices 3 Book chapters, 7 Journal papers 31 papers in international conferences with peer review Standardizations: June 2005-Dec 2009: Leader for Service Availability Forum Security working group, Co-editor for Service Availability Forum Security service specifications version A.0.1, released Sept, June 2002-Sept 2003: Editor for security requirements of Carrier Grade Linux Release 2.0 for Open Source Development Lab, released July Open Source: M. Pourzandi 3 Main software architect and project leader for Distributed Security Infrastructure Team leader for "Model-Based Engineering of Secure Software and Systems", Development of Java based plug-ins for IBM Rational Software Architect

4 M. Pourzandi 4 RESEARCH THEMES

5 M. Pourzandi 5 Distributed Security Infrastructure: Middle ware security

6 M. Pourzandi 6 MOdel-Based Framework for the Engineering of Secure Software and Systems: Software Security

7 M. Pourzandi 7 Telecom networks security: SPAM Mitigation on LTE 4G Mobile Networks Distributed architecture on the LTE network for SPAM mitigation Solving the over dimensioning problem Using of-the-shelf hardware in distributed nodes

8 Threats Connection-Based: - RF Jamming - Wireless Scrambling - Eavesdropping - Message Modification & Injection - Protocol Failures - Physical Attacks & Natural Disasters M. Pourzandi 8 Smart Grid Communications Security Device-Based: - Physical Attacks, Nat. Disasters - Rogue Access Points - Man-in-the-middle Attacks - DoS Attacks, Replay Attacks - Illegitimate use of services - Masquerading - Wardriving Base Station Smart Meter Home Gateway Home Area Network Neighborhood Area Network

9 M. Pourzandi 9 Research Themes Software security Verification and validation of security requirements at design level Integration of enforcement mechanisms at the design level Distributed security infrastructure Application Middleware Security Distributed process based access control DDoS and SPAM mitigation mechanisms in Mobile Telecom networks Distributed Architecture for Spam Mitigation on LTE 4G Mobile Networks Cloud computing security Network & Cloud Computing Security Security and privacy of user-generated data in the cloud storage Self-protecting elastic security frameworks for large IT systems Communication Security for Smart Grid Distribution Networks Smart Grid Security

10 M. Pourzandi 10 WHY AN ELASTIC SECURITY FRAMEWORK IN CLOUD INFRASTRUCTURES?

11 M. Pourzandi 11 Agenda Background Elastic Security Framework Elastic Enforcement Layer Security Enforcement Optimization

12 M. Pourzandi 12 Cloud Computing: Infrastructure As A Service (IaaS) Enhanced by massive virtualization Internet Servers Physical Infrastructure Shared pool of configurable computing resources Elasticity: On-demand resource, auto-scaling Virtualization Virtual Infrastructure Self provisioning, Flexibility Physical Infrastructure

13 M. Pourzandi 13 Target systems: Large IT systems such as cloud infrastructure Cloud infrastructure build on top of large data centers Several thousands to hundreds of thousands of servers Cloud approach is based on pay for the resources that you need You just turn off the extra resources when there is no need Massive virtualization to provide elasticity and flexibility

14 M. Pourzandi 14 Cloud Computing Security: Status Security is a major concern for the industry when moving to Cloud Computing 72% of organizations are "extremely concerned" or "very concerned" about security in the cloud environment (2010 research firm TheInfoPro) Many of the cloud security issues are the same for enterprise security Some differences though

15 M. Pourzandi 15 Background Complexity of the application behaviour and sheer number of them make it difficult, costly and error prone to write down by hand different network security enforcement rules for the data centers Cloud elastic nature makes it necessary to be able to adapt security rules in an agile and fast way This makes a human intervention too slow and not realistic given the pace of changes An old problem: enforcing security in a complex network

16 M. Pourzandi 16 New dimensions for an old problem Scalability and elasticity in the cloud make it impossible to use old methods Multi Tenancy/Compartmentalization: Need to isolate communications/resources between different customers Scalability: Need to support tens of thousands of virtual machines, running on thousands of physical servers Flexibility: Need to support many different types of applications with different network topologies and security needs Elastic security: Need to maintain security policy as data and virtual machines migrate in the cloud, and auto-scale

17 M. Pourzandi Use Cases Consider security mechanisms for a 3-tier application Assume a deployment in the cloud: 6 instances of web server, 2 instances of business tier and 1 instance of database 17

18 M. Pourzandi Possible mapping of virtual machines into a physical network 18

19 M. Pourzandi Consequences of VM Migration on Security Rules If in the previous example WS6 migrates from PS2 to PS4 then: 1. WS6 rules should be removed from FW1 and added to FW2 2. WS3 WS6 rules in AppFW1 should be removed and added to AppFW2 3. Security policy of FW1, AppFW1, FW2, and AppFW2 should be verified and validated This means all FWs in the previous scenario are affected by this migration! 19

20 M. Pourzandi 20 Current approaches: Solution 1 Virtual FW defined for each VM When VM1 migrates to another data center, VM1 traffic is redirected back to the data

21 M. Pourzandi 21 Current approaches: Solution 2 Different VFWs are composed together Creating multitude of vfws Benefit from HW Firewalling

22 M. Pourzandi 22 Challenges remain When VM1 migrates, there is need for maintaining the same sec policy Validate that inserted rules do not introduce any anomalies in other FWs Security policy orchestration Topology based optimization

23 M. Pourzandi 23 How to address these challenges? Need for automatic and dynamic generation of security rules Maintenance and enforcement of security rules for a large number of components, e.g. virtual machines in the cloud infrastructure For an elastic network there is need for an elastic network security

24 M. Pourzandi 24 Agenda Background Elastic Security Framework Elastic Enforcement Layer Security Enforcement Optimization

25 M. Pourzandi 25 ESF: AN ELASTIC SECURITY FRAMEWORK FOR CLOUD INFRASTRUCTURES

26 M. Pourzandi 26 ESF High Level overview ESF presents a framework to implement security vertically through different layers of the cloud infrastructure Few steps involve human intervention: Developers describe their distributed application security policies Remaining steps are transparent to the developers and are generated automatically from the description

27 M. Pourzandi 27 Elastic Network Security: Functional Diagram Automatically generate security policy for different applications running in the cloud from their description Auditability: Being able to verify and validate the consistency and the compliance with predefined security policy Configure the enforcement measures to enforce those security rules in the cloud Compose/Consolidate different security rules in order to implement an efficient enforcement Dynamically modify/adapt the security enforcement measures based on the security policies

28 M. Pourzandi 28 Agenda Background Elastic Security Framework Elastic Enforcement Layer Security Enforcement Optimization

29 M. Pourzandi 29 ELASTIC ENFORCEMENT LAYER (EEL) Sticky Flow Algorithm Case

30 M. Pourzandi 30 Elastic Network Security: Functional Diagram

31 M. Pourzandi 31 EEL Virtual security architecture is anchored in the physical architecture As the applications evolve/migrate in the cloud, the enforcement measures should be adapted to enforce the security policies All life stages of VM must be taken into account: launch, termination, cloning, migration, etc.

32 M. Pourzandi 32 EEL functionality Dynamic and automatic enforcement of security mechanisms L3-L7 Firewalling, Secure connections establishment, e.g. IPSec tunnels, DPI, IDS/IPS, etc. Rapid scaling of protection mechanisms When one or several tenants are under attack, for example DDoS, mitigation mechanisms can be scaled up As the tasks performed by the cloud are Agile, Scalable, Elastic, their security policy enforcement should also be the same: Agile, Scalable, Elastic

33 M. Pourzandi 33 EEL flexible design EEL enforces security policies through different nodes in the cloud data center, Policy Enforcement Point (PEP) Policy Decision Points ( PDP) decide how and what PEPs enforce Based on resource availability (Bandwidth, CPU, Specialized HW, e.g. network processors) Latency Locality information

34 M. Pourzandi 34 EEL design application principles to the network layer: Sticky flow Network security is applied through different network middle boxes/security appliances, e.g. Firewall, IDS/IPS, Web App Firewall Different network traffic must traverse a pre-defined sequence of security appliances Automatic and Transparent Enforcement in consideration of multi-tenancy, elastic networking and VM cloning and migration Particularly, traffic should traverse security appliances in the sequence required by the tenant and should not traverse unnecessary security appliances

35 M. Pourzandi 35 State of the art: Policy aware network enforcement Sticky Flow Solution Support Middlebox Isolation Automatic Migration Dynamic Policy-aware [Stoica] Y Y Y N N NetOdessa [Bellessa] FML/FSL [Mitchell- Shenker] N Y Y N N Y Y N N N

36 Elastic enforcement M. Pourzandi 36

37 M. Pourzandi 37 Sticky flow design (1) Application ID (AppID) for each vapp inserted at hypervisor layer, e.g. IP options Each AppID is associated to some security sequence AppID is used for control level in SDN

38 M. Pourzandi 38 Sticky flow design (2) EEL-tags added at Ethernet layer: Generic Tags (gtags) Instance Tags (itags) EEL tags are used for forwarding layer Appliance types are not redundant in the sequence, in the security sequence then Reasonable as a sequence is applied to a communication between two VMs in the network

39 M. Pourzandi 39 Basic use case The OpenFlow-Controller (OFC) extracts the AppID and determine the chain of gtags to be traversed It then matches the Generic Tags (gtags) to an Instance Tags (itags) range It then chooses the middebox instances to send the packet to (based on cloud resource availability). In our example, let's assume the chosen instances of IDS, AppFW and DPI correspond to itags 2070, 1045 and 3093 respectively The OpenFlow-Switch (OFS) forwards the rst packet to the controller VM1 starts emitting packets. These packets are intercepted by the hypervisor that inserts the AppID into the ip options

40 M. Pourzandi 40 Basic use case Similar rules to the previous ones are to be set into all the middleboxes edge's OFS. Note that for the egress switch of the last middlebox in the chain, the packet should only be routed to the next switch towards the destination VM Along the path, the controller adds a rule to forward the packet to the next switch towards the middlebox instance, based on the EEL-tag. Elasticity: the security appliance instances can change as virtual network change The OFC also adds three new ow-entries into the IDS's ingress and egress OFS : { Packets tagged with EEL-tag 2070 must have their tag popped and be forwarded to the IDS (ingress). { Packets out of the IDS, from VM1 and to VM2 must have the EEL-tag 1045 pushed (egress). { Packets with EEL-tag 1045 must be routed to the next switch towards the AppFW 1045 instance (egress). The OFC adds a two new ow-entries into the VM1's edge OFS : { Packets from VM1 (to VM2) must be tagged with EEL-tag { Packets with EEL-tag 2070 must be routed to the next switch towards the IDS 2070 instance. Mulitenancy is enforced dynamically and automatically at layer 2.

41 M. Pourzandi 41 Migration use case: intra data center VM1' starts emitting packets. These packets are intercepted by the hypervisor that inserts the AppID into the ip options Similar rules to the previous ones are to be set into all the middleboxes edge's OFS. Same as previous. Note that the IDS itag is now Only the AppFW egress switch rules may be modifed, for example if VM1 and VM1' don't have the same MAC address. Network Security Policy is maintained dynamically and automatically after VM migration.

42 Elastic enforcement M. Pourzandi 42

43 M. Pourzandi 43 Sticky Flow Algorithm Traffic is steered inside the DC network based on App ID Open Flow controller is the PDP Open Flow switches and Security appliances are PEPs

44 M. Pourzandi 44 Implementation OpenFlow : NOX Openflow controller Python code added to support sticky flow functionality EEL-tags Usage of VLAN tag support Network : Mininet Custom topology Implemented as Python Sender, receiver, middlebox Implemented as Python processes

45 Proof of concept M. Pourzandi 45

46 M. Pourzandi 46 Sticky flow conclusions Automatic and transparent enforcement Isolation At switch level, L2 enforce the security isolation between tenants networks Maintaining security policies in an elastic environment VM migration/cloning Security policy can be maintained at network layer through different data centers Delegating the choice of security appliances instances according to data center resources No need for centralized decision making/resource management Better resiliency and efficiency in resource consumption

47 M. Pourzandi 47 Agenda Background Elastic Security Framework Elastic Enforcement Layer Security Enforcement Optimization

48 M. Pourzandi 48 SECURITY ENFORCEMENT OPTIMIZATION Local-Global Multi-objective Constraint-Based Path Optimization Algorithm in the cloud infrastructure (LGCM)

49 M. Pourzandi 49 Elastic Network Security: Functional Diagram

50 M. Pourzandi 50 Goal: Build an optimal path based on multiple factors passing through some predefined set of security appliances

51 M. Pourzandi 51 Multi-objective Optimization (1) Need for multiple criteria optimization algorithms Ex: cost, delay/latency, capacity, ownership for each network link Typically, there is no unique optimal solution for such problems Necessary to use decision maker s preferences to differentiate between solutions Difficulty comes from the presence of more than one criterion No longer a unique optimal solution to the problem that can be obtained without incorporating preference information

52 M. Pourzandi 52 Multi-objective Optimization (2) Concept of an optimal solution is often replaced by a set of non-dominated solutions A non-dominated solution has the property that it is not possible to move away from it to any other solution without sacrificing in at least one criterion The boxed points represent feasible choices, and smaller values are preferred to larger ones. Point C is not on the Pareto Frontier because it is dominated by both point A and point B. Points A and B are not strictly dominated by any other, and hence do lie on the frontier Fig from Wikipedia

53 M. Pourzandi 53 Solving Multi-objective Optimization: State of the art Scalarization: convert the original problem into one single problem Ex: Assign weights to different objectives in a linear scalarization Difficulty is to come up with right weights Human expert Difficult to be used in the cloud context, i.e. dynamic changes, large scale, elastic networks, short answer times needed Evolutionary Multi-objective Optimization Find all valid paths Low complexity comparative to other approaches, i.e. cost Difficult in cloud environment to define the convergence factor to the optimal solution

54 M. Pourzandi 54 Evolutionary Multi-objective Optimization Start from a set of initial individuals Iterate over generations Select the fittest individuals Mate the fittest Mutate over to create new individuals Converge toward a set of non-dominated individuals

55 M. Pourzandi 55 Bueno approach using SPEA2 for multicast flow routing Bueno algorithm* addresses building a multi-factor optimal multicast using SPEA2 An heuristic proposed to reduce the problem Mating selection Step 1: Fitness based on Pareto dominance: dominated by, dominating Dominance rank, dominance count Step 2: Refining through density, select individuals in less dense area to improve the diversity KNN density [*] Bueno, M.L.P.; Oliveira, G.M.B.;, "Multicast flow routing: Evaluation of heuristics and multiobjective evolutionary algorithms," Evolutionary Computation (CEC), 2010 IEEE Congress on, vol., no., pp.1-8, July 2010

56 M. Pourzandi 56 Supporting sequence of security appliances In Bueno Algo, there is no concept of sequence of middle boxes to respect Need for improving Bueno s algorithm with the concept of sequence

57 M. Pourzandi 57 LGMC: Illustrating Step by Step paradigm One step is defined to be an edge in the sequence diagram Bueno is used at each step Objective function must minimize link utilization, total cost, end-to-end delay, hops count

58 M. Pourzandi 58 LGMC Pseudo Code: define global paths Pre-defined security sequence of K middle boxes, i.e. K steps // Find Pareto front local paths for each step For each step do For every step I in the pre-defined sequence of middleboxes do According to step I for valid instances of middle box types then Assign Src and Dst to be two valid instance of the middle boxes Apply Bueno between Src Dst Find the Pareto front of local-paths between Src and Dst, i.e. local-path.. Assign Pareto front local-paths.. to step-paths.. // Build global paths from local steps Assign to Global-paths[m] the K-tupe (steppaths[1] step-paths[k])

59 M. Pourzandi 59 LGMC Pseudo code: finding Pareto front among global paths // Re-apply MOEA to the k-tuples while keeping the precedence of local-paths in the k-tuple Apply SPEA2 MOEA to the k-tuples Mating: fill mating pool through binary tournament with new (ktuple) individuals Mutation: Mutate new individuals by changing the local-paths respecting the sequence, i.e. mutation in step I from local-paths[i] End result: Pareto Front in the global paths, i.e. from Source VM to destination VM

60 M. Pourzandi 60 LGCM: Complexity LGCM is based on SPEA2 with the complexity log where M is number of individuals at each generation LGCM complexity is then K log where K is the number of elements in the security sequence LGCM complexity is independent from N number of nodes in the network We cannot really compare an evolutionary algorithm with exact algorithmic methods Chen and Nahrstedt showed on a paper that a similar kind of problem, i.e. Multi-constrained paths can be solved in complexity where N is the number of nodes in the graph and x is large enough (e.g. 10)

61 M. Pourzandi 61 Future work LGCM is our first attempt at using MOEA in a network with a pre-defined set of constraints First results are encouraging Theoretical complexity is comparatively low Proof of concept program results in valid graphs Need to validate approach through more complete set of examples Need for new improve current LGCM algorithm by extending our work to create virtual security appliances in the cloud infrastructure

62 M. Pourzandi 62 ESF conclusions ESF targets developing a homogeneous approach around complex problems Several problems have been addressed so far Elastic enforcement: Sticky Flow Algorithm Enforcement optimization: LGCM Verification and validation of security rules: Cloud Calculus Need to extend these results to a wider use cases