An Analysis on Software Defined Wireless Network using Stride Model

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 08 | Aug -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1008
An Analysis on Software Defined Wireless Network
Using STRIDE Model
Arockia Panimalar.S 1, Nishanth.R2, Sathish.G3, Manikandan.G4
1 Assistant Professor, Department of BCA & M.Sc SS, Sri Krishna Arts and Science College, Coimbatore, India
2,3,4 III BCA, Department of BCA & M.Sc SS, Sri Krishna Arts and Science College, Coimbatore, India
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Abstract - The present mobile and remote system are
becoming quicker in size and complex to quantify the
administrations. Security is a standout amongst the most
essential angles for such complex system and should be
checked appropriately to give early identification of security
ruptures and Denial of Service assault. Tools that measure
such detection of network threats and monitors network
services requires interior security in their own particular
component. This paper examines two of such checking and
estimation apparatuses: sFlow and FlowVisor for hidden
Software Defined Wireless Networking (SDWN) condition by
applying STRIDE threat model. This analytical study
represents that, sFlow requires an externalsecure deployment
environment to ensure security in data flow and datastorefor
SDWN. FlowVisor accompanies secured get to control in
information store wherein separated stream cut requires
instrument that enhance its security.
Key Words: Software Defined Wireless Networking
(SDWN), STRIDE, OpenFlow, sFlow, FlowVisor
1. INTRODUCTION
Wireless Networking turns into the most versatile
innovation for adaptability and portability inhumanlife. For
the most recent couple of years, Software Defined Wireless
Networking (SDWN), a branch of Software DefinedNetwork
(SDN) has been a key research innovation to dissect and
legitimate administration of the thickly populated cellular
network [1] [2]. Programming DefinedWirelessNetworking
(SDWN) guarantees straightforward and adaptable system
design and successful portability administration of the IP
networks. The Software Defined Wireless Networking
(SDWN) automatically concentrates and isolates the control
plane (otherwise knownas. Network OS)fromthedata plane
(otherwise known as Forwarding plane). A regular
engineering of SDN is delineated in Fig. 1. The southbound
interface is a medium between the control plane and data
plane while northbound is layer between application plane
and control plane. The southbound interface prepares the
controllers to gather data about Mobile Nodes (MNs) and
transmits and gets bundles to and from MNs utilizing SDWN
components [3]. To guarantee qulaity of service in SDWN
and persistent network services, operators need to monitor
the network and do legitimate service measurements from
time to time. Such observing will help in in analyzing
network parameters,i.e.throughput,roundtriptransmission
time, data transfer capacity in the remote connection,
mobility frequency and preparing a real-time view of the
network service standard on industry level. For such
analysis, observing and estimation, different open source
and business innovation and instruments are accessible for
SDWN including sFlow [4], FlowVisor [5], BigSwitch [6],
BigTap [7], SevOne [8] and so on. These tools provide the
operator with capabilities to perform troublesome network
activities and even monitor, detect and indicate security
attack in progress on a certain network entity.
Fig 1: SDN Architecture with Control and Data Plane
Beforehand a few research works isperformedondissecting
the security of OpenFlow-based SDN environment. Analysis
on 4D, PCE and SANE-based SDN architectures is performed
in paper [9], security use of SDN is completely investigated
and assessed in [10]. First, sFlow was represented as an
effective andscalablevulnerabilitymitigationmechanismfor
SDN [11]. FlowVisor turned a better solution for network
virtualization [12] and powerlessness answer for flow
isolation is proposed and assessed nearby [13]. Among the
tools that screen and measure the SDN, a comparative study
between sFlow (Open-Source) and BigTap (commercial) is
illustrated in paper [14]. Be that as it may,a securityandrisk

characterized ponder in SDWN monitoring and
measurement tools, thosefocusesonOpen-Flowflow entries
and communication among multiple controllers in wireless
platform, is the primary goal of this perspective study.
Consequently, sFlow and FlowVisor are decided for above
stream conditions.
The structure of this paper is as per the following. In Section
II, the STRIDE and Data Flow approach is portrayed. In
Section III and IV, security and threat risks of sFlow and
FlowVisor are broke down. Segment V represents a
comparative report between the two monitoring tools and
along these lines closing the paper in Section VI with future
prospects of this study outcome.
2. METHODS FOR IMPLEMENTING SDN
Several approaches are available for implementing SDN
concept including OpenFlow that separates the control and
forwarding plane in the network architecture. SDN
approaches were generalized using a concept of OpenFlow
and were introduced in mid-1990s.
A. OpenFlow
According to OpenFlow specification in [15], any OpenFlow
switch holds flow entries that contain incoming packet
header information, packet handling action for matched
packet entries in the list and statistics of number of bytes,
packets in a particular flow and time since last pass. Packets
as arrive at any OpenFlow switches, it executes the packet
header information and try matching the existing flow
entries. When the information does not match any of the
flow tables, switch then pass the packet to the controller to
take action and update the flow entries accordingly with
required information of the packet. Whenit’sa match switch
performs and forwards the packet to its next destination on
the basis of routing flow table information in it.
B. Software Defined Wireless Network
As SDN brings more advantage in connecting into the
internet, Software Defined Wireless Network (SDWN) has
got much importance and emerging research field with
attention. SDWN isorientedtowardsthemobileand wireless
network devices and aims at the research and study of
crucial technologies for the future mobile and wireless
network. This SDWN architecture is composed of both
North-South and East-West network dimensionwhereEast-
West operates for wireless and mobile devices using
intercontroller protocols such as Border Gateway Protocol
(BGP) [16]. Hence, security of the underlying network
depends on the secured flow information and control plane.
Tools that monitor and measure and flows between SDWN
entities, therefore, requires security from external access
and service oriented attacks. This study is concerned about
sFlow and FlowVisor as one of these tools.
C. Threat Modelling and STRIDE
Threat Modelling used to refer to analyzing any software or
system or organizational network. Threat Modeling
encompasses a wide variety of activities in the elicitations
and analysis of securitymechanismsindeployeddesigns and
network [17]. Some of the mostly applied models include
DREAD [18], Octave [19], STRIDE [20], Generic Risk Model,
Guerilla Threat Modelling, Process forAttack Simulationand
Threat Analysis (PASTA), Trike etc [21]. DREAD provides
threat identification rate as SQL injections and provides the
subjective assessments by the threat reporter.Octave model
is best suited for complex and larger system where STRIDE
focuses on network based application and systems. Trike
helps security auditing process with distinct risk-based
implementation than others, however, is yet in
experimentation stage and lacks proper documentationand
support. PASTA includes risk management steps in the final
stage of the process and is not limited to a specific risk
calculation formula [22]. Thereby, introduced by Microsoft,
STRIDE model method is used to identify and evaluate the
security threats on OpenFlow based SDWN network
measurement and monitoring tools: sFlow and FlowVisor.
STRIDE threat model reveals if a system or software in
concern is vulnerable to Spoofing, Tampering, Repudiation,
Information Disclosure, Denial of Service (DoS) and
Elevation of Privilege threat [20]. EachoftheSTRIDE threats
can be mapped to one security property as shownin Table 1.
and described in the following:
a)Spoofing: In spoofing malicious user or program
masquerades gain illegal access in privileged data by
falsifying user information.
b)Tampering: Data tampering involves malicious
modification of information and resources i.e. alteration of
data as it streams between two PCs over an open network
called the Internet.
c)Repudiation: Repudiation threats are associated with
malicious users and masquerades who performs an action
and deny without other parties having any way to prove
otherwise—for example, an attacker controller performs an
illegal operation in a SDN that lacks the ability to trace the
prohibited operations.
d)Information Disclosure: Thistreatmeanstheillegitimate
availability of resourceinformationofthe systemornetwork
or software to malicious and unauthorized users or
programs.
e) Denial of Service: This treat causesserviceunavailability
to the authorized legitimate users or programs.
f)Elevation of Privilege: In this type of threat, an
unprivileged user gains privileged access and thereby has
sufficient access tocompromiseordestroytheentiresystem.

This treat can cause penetration of all system or network
defense and declares it a trusted system.
Table 1 presents the STRIDE threat categorization model,
based on the above definitions, which includes the
corresponding security property and default controls
associated with the threat type.
TABLE 1: Threat Categorization, Security Properties
and Controls [17]
Threat Property Controls
Spoofing Authentication Authentication Stores,
Strong Authentication
mechanisms
Tampering Integrity/
Access Controls
Crypto Hash, Digital
watermark/ isolation
and access checks
Repudiation Non
Repudiation
Logging infrastructure,
full packet-capture
Information
Disclosure
Confidentiality Encryption or Isolation
Denial of
Service
Availability Redundancy, failover,
QoS, Bandwidth throttle
Elevation
of Privilege
Authorization
/Least Privilege
RBAC, DACL, MAC, Sudo,
UAC, Privileged account
protections
Data Flow Diagrams (DFD) are used to graphically represent
any system [17]. DFDs use a standard set of symbols
consisting of four elements: data flows, data stores,
processes, and interactors [17]. In Table2,DFD elements are
identified as a means of eliciting information which can be
used to drive STRIDE threat analysis. As illustrated in Table
3, each DFD elements can be vulnerable to one or many
STRIDE threats.
TABLE 2. DFD elements and their representation [17]
Name Representation Definition
Data Flow Directed Arrow Data sent among
network elements
Data Store Parallel Lines Stable Data
Process Circle Programs or
applications that
configures the system
Interactors Rectangular Box Endpoints out of
system scope to
control
Trust
Boundaries
Dotted Line Separation between
trusted and untrusted
elements of the system
TABLE 3. STRIDE Threats per DFD element [17]
Threat Data
Flow
Data
Store
Process Interactors
Spoofing Yes Yes
Tampering Yes Yes Yes
Repudiation Yes Yes Yes
Information
Disclosure
Yes Yes Yes
Denial of
Service
Yes Yes Yes
Elevation of
Privilege
Yes
3. sFLOW
sFlow is an open source sampling technology and traffic
measurement and monitoring tool for OpenFlow network
[4]. It is a traffic monitoring solution embedded with switch
and router of any possible OpenFlow based SDWN. Primary
elements of sFlow system consists sFlow agents and sFlow
collector, illustrated in Fig. 2. Agent is the software process
that is remotely configuredusinga Management Information
Base (MIB) within the device. Consolidating the interface
counters and flow tests into sFlow datagrams, these
datagrams are sent to the sFlow collector installed in the
checking host through the SDWN environment utilizing
Simple Network Management Protocol (SNMP) [23].
Including sFlow’s own collector sets: sFlow-RT, sFlow-
Trend, sflowtool, this sampling tool also support the third
party collectors: VitalSuit, Peakflow, Kentik Detect and
FlowTraq - those handle more details of sFlow datagrams
[4]. Illustration in Fig. 2 represents the Data Flow Diagram
(DFD) of sFlow that uncovers the crucial security risk.sFlow
doesn’t provide any security mechanism fordata flowrather
depends on secure third partymanagement environmentfor
sFlow agents.
Fig. 2: sFlow Data Flow Diagram

A. Data Flows
Data flows are vulnerable to Tampering, Information
Disclosure and DoS attack in absence of proper security
mechanism. A physical interface of switch, routers is
potential data sources in the underlying SDWN. These
provides sampled data packet to sFlow agents for
measurement. sFlow agents combine packet flow sampling
and counter sampling to sFlow datagrams. The sFlow
Datagrams are used to immediately forward the sampled
traffic statistics being unencrypted to a sFlow Collector for
analysis [24]. As collectors can be vendor provided, security
of the received datagrams depends on vendor’s will of
deployment and how they process the data. Hence, sFlow
doesn’t provide any security mechanism. For security
reasons SNMPv3 should be used to configureandcontrol the
sFlow agents to encrypt and authenticate the datagrams
before transmitting to the collector [24].
B. Data Stores
According to Table 1 data store are prone to Tampering,
Information Disclosure and DoS attack vulnerabilities alike
data flows. MIB contains information about sFlow agents,
collector ports and even IP addresses. Using SNMP, sFlow
agents can be configured through a local Command Line
Interface (CLI) or SNMP commands. In order to decline any
anonymous actions, switches and routers in the network
should have some Access Control (AC) mechanisms, i.e.
Discretionary Access Control (DAC), Role-Based Access
Control (RBAC) to ensure the interface’s security [14]. In
inverse case, if CLI is available from unapproved client, MIB
in sFlow is powerless against data Tampering, traffic
Information Disclosure and even DoS assault, holds the MIB
flow enteries and authority subtle elements open for
unapproved get to and considerably aggressor can alter the
information. If there should be an occurrence of SNMPv1,
SNMPv2 communication with collector is at comparable
dangers.
C. Interactors
sFlow agents performs one-way communication with the
sFlow collectors and sends the combination of packet based
and time-based sampled traffic data [24].According to Table
1. they are not considered as interactors.
D. Processes
sFlow agent processes are not accessible through interfaces,
therefore the STRIDE method is not applied. The collector
should check the time-based counter number of the sFlow
datagrams to provide a securitymechanismagainst spoofing
attacks [24].
E. Summary
Above analysis clarifies that sFlow requires a third party
deployment environment for security needs. However,
ensuring the Transport Layer Security (TLS) among sFlow
agents and collector, sFlow itself can emerge as a secured
SDWN monitoring and measurement tool for wireless and
Table 4 shows the probable vulnerabilities of sFlow agents.
Adapting an access control mechanism can eliminate the
security risks to a certain level of tampering and MIB
information disclosure.
TABLE 4. sFlow Vulnerabilities
Threat Data
Flow
Data
Store
Solution
Tampering Yes Yes ACL/RBAC/DAC for
CLI, NPMv3, TLS
Information
Disclosure
Yes Yes TLS
Denial of
Services(DoS)
Yes Yes AC in CLI for MIB
Security, TLS
4. FLOWVISOR
FlowVisor is an OpenFlow controller works as a proxy in
between the OpenFlow switches and several multiple
OpenFlow controllers, allowing visualization of OpenFlow
physical infrastructure into different virtual networks [5].
Using OpenFlow protocol, FlowVisor controls underlying
network, dividing the resources into slices isolated from
each other. And delegates control of each slice to a different
controller [5]. FlowVisor provides isolationfortopology and
addressing space. FlowVisor is architecturally a neutral
transparent proxy and makes no assumption about the
functions and operations of the switches and controllers.
FlowVisor sits between each of the controllers and switches
making sure that the guest controller has full accessibility of
the switches maintaining the flows that define the
corresponding slice. The DFD in Fig. 3 represents data flow
between OpenFlow enabled switches and controllerswhere
messages are intercepted through FlowVisor.
Fig. 3: FlowVisor Data Flow Diagram

FlowVisor partitions the base transmission transfer speed
for each slice doling out particular data rate to a set of flows
from that slice. FlowVisor screens each flow-entry for each
guest controller and portions the flow table among the
switches. Switches are arranged by the resource allocation
and directing approaches cuts of the FlowVisor controller.
Slices are isolated and have their own ‘flowspace’ or set of
region of data flows. These isolated slices can be broken
allowing different attacks.
A. Data Flows
FlowVisor adopts slicing policies for each guest controller.
Activity sent from the production network and guest
controllers if matches the sending enteries in the FlowVisor
are sliced for relevant switches as per 'flowspace'. Diverse
slices having adaptable and distinctive flow policies are
emphatically separated. Any traffic that does not coordinate
the current sending enteries are sent to the production
controller for inclusion. Production controller subsequently
revises the relevant slice. Assault on slice policy reworks
from assaulting entity can make vulnerabilities such system
with FlowVisor. Assuming, in this manner, data is sent from
an aggressor, the controller can't identifyasa resultofpolicy
revise and causes altering of flow rules and the system data
and even DoS dangers.
B. Data Stores
The switch arrangement is put away in the flow enteries of
the cuts by the respective guest controllers. This permits
data movement validation to flow between the controllers
and switches inside the wireless OpenFlow network even
under portability circumstances. This mechanism ensures
that data is secured against Tampering, Information
Disclosure and Spoofing threats.
C. Interactors
FlowVisor’s Command Line Interface (CLI) provides control
access to users for data and slice configuration. CLI uses
user-authentication in terms of username, host name and
port number on accessing the interface and slices and
therefore securefromSpoofing,Tampering,Repudiation and
Elevation of Privilege threats.
D. Processes
Slice processes are owned by the admin and groups of the
network operators and thereby Spoofing, Repudiation, DoS
and tampering threats are unable to make the network
vulnerable in FlowVisor’s process.
E. Summary
FlowVisor is open source to access controller’s processes,
data flow and action support forslices.Althoughthistool has
separate production controllerandisolated slicestoperform
the flow independently against any attacking entity,
FlowVisor is vulnerable to different threats at different flow
status described in Table 5.
TABLE 5: FlowVisor Vulnerabilities
Threat Data
Flow
Solution
Tampering Yes TLS
Information
Disclosure
Yes TLS
Denial of Services
(DoS)
Yes Access Control in CLI
for policy rewrite, TLS
Adjusting Transport Layer Security can safeguard the
arrangement revise production controller for unmatched
packets where virtual controllercan'tchangetheMACandIP
address for the packets uninhibitedly.
5. COMPARISON BETWEEN sFLOW AND
FLOWVISOR
sFlow and FlowVisor both provide different network
monitoring and measurement functionalities. The
comparative threat model analysis of them is illustrated in
Table 6. Above investigation holds sFlow giving no security
in data flow and data store in DFD wherein FlowVisor
acquires security threat vulnerabilities in disengaged cuts.
This makes FlowVisor defenseless against Spoofing,
Tampering and Information disclosure, even postponement
and Denial of Service dangers in data flow. However,
FlowVisor guarantees security of switch information put
away in its own controller where sFlow relies upon external
secure environment to guarantee security in MIB data
storage and flow entry information. This makes sFlow
helpless against spoofing, DoS and information divulgence
risk as switching operators send decoded datagrams to the
collector. Utilizing Transport LayerSecurity(TLS)insending
the datagrams to the collectors can take out information
exposure threatswhereintamperingcanbehandledutilizing
access control mechanism in CLI, agent arranging SNMPv3
protocols. FlowVisor includes access control in CLI for slice
information whichprotectsitfromspoofing,repudiation and
elevation of privilege attacks fromanykindof malicioususer
or masquerades.
TABLE 6: Comparison of FlowVisor and sFlow tools
Threat Data Flow Data Store
Tampering FlowVisor, sFlow sFlow
Information
Disclosure
FlowVisor, sFlow sFlow
Denial of Services
(DoS)
FlowVisor, sFlow sFlow

6. CONCLUSION
In this paper, an analysis on wireless SDN monitoring tools,
sFlow and FlowVisor, in terms of STRIDE security threat
model where both has different functionalities and
vulnerabilities in handling data traffic flows and network
entities. This analysis will provide suggestions in handling
the above mentioned security threats in SDWN using
existing well-to-do mechanisms. These study fall in the
category of security-centric SDWN and will be viable in
doing research on OrchSec wireless architecture [25].
7. FUTURE ENHANCEMENT
The future prospects of this SDWN securityanalysis will lead
to persistent research on assessment of SDWN appliance in
data center, cognitive networks and mobile communication.
As future work, the researchers would plan to study
FlowVisor topology isolation mechanism and queue-based
bandwidth isolation mechanism in securing the underlying
SDWN network. Prototyping the network in real timeSDWN
network devices and environment will be interesting and a
big challenge ahead.
8. REFERENCES
[1] Bernardos et al., "An architecture for software defined
wireless networking".
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bigswitch .com/topics/big-tap-monitoring-fabric.
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SDN Control Functions”

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