SlideShare a Scribd company logo
1 of 9
Download to read offline
International Journal of Innovative Research in Information Security (IJIRIS) ISSN: 2349-7017
Issue 01, Volume 6 (January 2019) www.ijiris.com
IJIRIS: Mendeley (Elsevier Indexed) CiteFactor Journal Citations Impact Factor 1.23
Impact Factor Value – SJIF: Innospace, Morocco (2016): 4.651| Indexcopernicus: (ICV 2016): 88.20
© 2014- 19, IJIRIS- All Rights Reserved Page -20
A MODEL BASED APPROACH FOR IMPLEMENTING
WLAN SECURITY
David Gitonga Mwathi
Department of Computer Science,
Chuka University, Kenya.
dgmwathi@chuka.ac.ke
Manuscript History
Number: IJIRIS/RS/Vol.06/Issue01/JAIS10082
DOI: 10.26562/IJIRAE.2019.JAIS10082
Received: 02, January 2019
Final Correction: 12, January 2019
Final Accepted: 21 January 2019
Published: January 2019
Citation: Mwathi (2019). A MODEL BASED APPROACH FOR IMPLEMENTING WLAN SECURITY . IJIRIS::
International Journal of Innovative Research in Information Security, Volume VI, 20-28.
doi://10.26562/IJIRIS.2019.JAIS10082
Editor: Dr.A.Arul L.S, Chief Editor, IJIRIS, AM Publications, India
Copyright: ©2019 This is an open access article distributed under the terms of the Creative Commons Attribution
License, Which Permits unrestricted use, distribution, and reproduction in any medium, provided the original author
and source are credited
Abstract- This paper presents various security features and configurations commonly implemented in WLANs and
their aggregated security levels and then proposes a model that enables implementation and evaluation of WLAN
security.
Keywords- WLAN Security model; WLAN Security; WLAN attacks & Vulnerabilities; WLAN Security Features
Selection; WLAN Security Measurement;
I. INTRODUCTION
In recent years, the proliferation of wireless local area network[WLAN] enabled laptop computers, smart phones
and tablets has caused an increase in the range of places people perform computing. As a result, WLANs of various
kinds have gained a lot of popularity. But with the added convenience of wireless access come new problems:
Security. When transmissions are broadcast over radio waves, interception and masquerading becomes trivial to
anyone with WLAN enabled device in the range. WLANs implement IEEE 802.11i and IEEE 802.11w standards
whose focus is provision of appropriate integrity and confidentiality levels.
These standards provide a variety of options for various security features.This flexible nature of the provisions of
these standards and supporting technologies create potential for selection of vulnerable cipher suite,
authentication & access control, end-user and server system security features. This makes selection and
configuration of the appropriate security features a challenge to many WLAN security implementers [1]. This issue
is a major concern because several software attack tools targeting vulnerabilities in authentication methods,
cipher suites and supporting technologies on client devices and server implementations continue to proliferate,
effectively empowering attackers.
This research aims to make a contribution by addressing the issue of poor implementation of WLAN security
through development of a model that enables design or selection of security features and their configuration for
WLAN security.
International Journal of Innovative Research in Information Security (IJIRIS) ISSN: 2349-7017
Issue 01, Volume 6 (January 2019) www.ijiris.com
IJIRIS: Mendeley (Elsevier Indexed) CiteFactor Journal Citations Impact Factor 1.23
Impact Factor Value – SJIF: Innospace, Morocco (2016): 4.651| Indexcopernicus: (ICV 2016): 88.20
© 2014- 19, IJIRIS- All Rights Reserved Page -21
II. RELATED WORK ON WLAN SECURITY IMPLEMENTATION APPROACHES
Several works propose the following variables for selection of appropriate cipher suite to support WLAN
authentication and access control: core cryptographic algorithm, key sizes, per packet key, integrity mechanism,
header protection, replay detection, authentication supported and mode of key distribution [2]. Other works focus
on selection of extensible authentication protocol [EAP] method for securing WLANs. Some of the variables
proposed to influence an implementer into selecting an EAP authentication mechanism in a WLAN include: mutual
authentication, identity privacy, dictionary attack resistance, replay attack resistance, derivation of strong session
keys, tested implementation, delegation, and fast reconnect.[3]
Mutual authentication requires that an EAP method should enable both client device and authentication server to
authenticate each other. It has been shown that the absence of mutual authentication in WEP based authentication
was responsible of many of its weaknesses [4]. Many network security administrators use their experience of past
solutions and documentation to configure security of network equipment [5]. In this case, selection and
configuration of security features is mainly guided by following a documented procedure or a wizard for
installation which enables network administrators bypass the “hardness” of dependency analysis. For example,
suppose a network administrator needs to configure a new access point’s security settings. The installation guide
may instruct one to select the cipher suite first, then authentication method. The network administrator may
follow these instructions without analyzing the cumulative effect of the combination of cipher suite and
authentication method selected on overall security [5]. Use of documentation and experience approaches alone is
not sufficient because documentation is not always accurate due to errors in the software, human error, time and
cost of developing detailed installation/configuration wizards by equipment manufacturers [5].Additionally,
documentation does not address all possible platforms because WLANs may comprise equipment/components
from various developers and vendors. Even where the equipment have been tested and verified fully by their
developers for any platform, it is possible that they can fail to function as specified in a particular
platform.Experience on the other hand may also not be always accurate and in a very dynamic environment, it is
possible to misconfigure the system by simply relying on experience that may not apply to the system’s current
state.
III. METHODOLOGY
Descriptive survey of 31 WLAN networks of public and private Universities in Kenya was made. Questionnaires
were sent to network administrators of these wireless networks to collect hard facts related to their network.
Observation of the configuration information on sampled networks was also made on the user devices and access
point using passive (nonintrusive) WLAN network search tools. This information was used to verify the
questionnaire responses. The data collected led to the discovery of security features and configuration options
which were aggregated as: cipher suite, authentication credentials, client driver, client utility, accespoint firmware,
authentication and access control mechanism, user database and authentication server. The researcher evaluated
the security levels of these security features/configuration options and used the results of evaluation to develop a
model that maps these security features/configuration options to security strengths as well as an algorithm that
combines and propagates the security strengths of the security features and configurations selected to generate
the overall security strength.
IV.RESULTS
This section presents the security features and configurations options available to the security implementer and
then proposes a model that maps these security features to security strengths as well as an algorithm that
combines and propagates the security strengths of the security features and configurations selected to generate
the overall security strength.
A. Model Operation Overview
The model has three steps that define its operation:
(i) Identification of security features and configuration options
(ii) Mapping security features/configurations options to security strengths.
(iii) Combining and propagating the security strengths of the security features and configurations selected to
generate the overall security strength
Subsequent sub-sections detail the activities of each step.
International Journal of Innovative Research in Information Security (IJIRIS) ISSN: 2349-7017
Issue 01, Volume 6 (January 2019) www.ijiris.com
IJIRIS: Mendeley (Elsevier Indexed) CiteFactor Journal Citations Impact Factor 1.23
Impact Factor Value – SJIF: Innospace, Morocco (2016): 4.651| Indexcopernicus: (ICV 2016): 88.20
© 2014- 19, IJIRIS- All Rights Reserved Page -22
B. Identification of Security Features and Configuration Options
This is the set of security features/configurations available to the security implementer for each of the eight key
components namely: cipher suite, authentication credentials. Client utility, client driver, access point utility,
authentication & access control mechanism, user database and authentication server. All these eight components
have been considered equivalent in relative importance in relation to their influence on WLAN security. This
means none of them can be considered superior to the other. However, their actual influence values/strength will
be determined by the security features selected or configurations on each of the components.
C. Mapping Security Features/Configuration options to Security Strengths
The model maps the security features/configuration option selected to “Very Low”, “Low”, “Moderate” or “High”
security strength based on already predetermined values. Each security feature/configuration is associated with
certain characteristics which determine its security strength. The decision on which strength a security
feature/configuration is mapped to is based on a value function as indicated in function table 1 to table 8.Looking
at each function table, whenever security strength of a security feature/configuration is mapped to level low,
moderate or high, it is denoted 1, 2 and 3 respectively.
Whenever security strength of a security feature/configuration is mapped to level very low, it is denoted as 0
which means that if this security feature/configuration is selected and implemented, the WLAN implementation is
highly susceptible to attacks. This means that such security feature/configuration should not be implemented in a
WLAN.
TABLE 1: VALUE FUNCTION TABLE FOR AUTHENTICATION CREDENTIALS
TABLE 2: VALUE FUNCTION TABLE FOR CIPHER SUITE
Security Strength [Weight of influence] Description of the Security feature/Configuration
High [3] CCMP (WPA2 +AES)
Moderate [2] TKIP(WPA +AES)
Low [1] TKIP(WPA +RC4)
Low [1] TKIP(WPA2 +RC4)
Very Low [0] WEP
TABLE 3: VALUE FUNCTION TABLE FOR WLAN CLIENT DRIVER
Security Strength
Weight of influence
Description of Security Feature/ Configuration
High [3]  Supports management frame protection (MFP/IEEE 802.11w) and validation.
 Supports configurable active scanning approach.
Moderate [2]  Supports management frame protection(MFP/IEEE 802.11w) and validation
 Lacks Support for Configurable active scanning approach
Moderate [2]  Lacks support for management frame protection (IEEE 802.11w) and validation
 Supports IEEE 802.11i.
 Supports configurable active scanning approach.
Low [1]  Lacks support for management frame protection (MFP/IEEE 802.11w) and validation
 Lacks support for Configurable active scanning approach.
 Supports IEEE 802.11i.
Very Low [0] Lacks support for IEEE 802.11i.
Security Strength [Weight of influence] Description of Security Feature/ Configuration
High [3] Both Client and Server Certificates
Moderate [2] PAC, One time password OR Server Side certificate only(Tunneled)
Low [1] Secret Key/password(Mutual or Unilateral)
Very Low [0] SSID
Very Low [0] MAC address
Very Low [0] PIN
International Journal of Innovative Research in Information Security (IJIRIS) ISSN: 2349-7017
Issue 01, Volume 6 (January 2019) www.ijiris.com
IJIRIS: Mendeley (Elsevier Indexed) CiteFactor Journal Citations Impact Factor 1.23
Impact Factor Value – SJIF: Innospace, Morocco (2016): 4.651| Indexcopernicus: (ICV 2016): 88.20
© 2014- 19, IJIRIS- All Rights Reserved Page -23
TABLE 4: VALUE FUNCTION TABLE FOR WLAN CLIENT UTILITY
Security Strength
Weight of influence
Description of Security Feature/ Configuration
High [3]  Configured to support both client and server side Certificate based mutual
Authentication.
 Supports Management frame protection.
 Configured to enforce validation of server certificates and server name.
 Configured not to allow Self signed certificates.
Moderate [2]  Configured to support server side only Certificate based mutual Authentication.
 Supports Management frame protection (IEEE 802.11w).
 Configured to enforce validation of server certificates and server name.
 Configured not to allow Self signed certificates.
Low [1]  Configured to support Password, pre-shared key or MAC address based mutual
Authentication mechanism.
 Supports Management frame protection (IEEE 802.11w)
Low [1]  Configured to support server side only or both client and server side Certificate based
mutual Authentication
 Lacks Support for Management frame protection (IEEE 802.11w) and validation.
 Supports IEEE 802.11i.
Low [1]  Configured to support Password, pre-shared key or MAC address based mutual
Authentication mechanism.
 Lacks Support for Management frame protection (IEEE 802.11w) and validation.
 Supports IEEE 802.11i.
Very Low [0] Lacks support for IEEE 802.11i.
Very Low [0] Configured to support server side only or both client and server side certificate but
Validation of server certificates and/or server name not enforced.
Very Low [0] Configured to support server side only or both client and server side certificate but
allows Self signed certificates.
Very Low [0] Mutual authentication not supported.
TABLE 5: VALUE FUNCTION TABLE FOR ACCESS POINT UTILITY
Security Strength
Weight of influence
Description of Security Feature/ Configuration
High [3]  Firmware configured to support management frame protection (MFP/IEEE 802.11w)
and validation and is set to required.
 Firmware configured to Support only RSNA connections(RSNA enabled)
Moderate [2]  Firmware configured to support optional management frame protection (MFP/IEEE
802.11w) and validation.
 Firmware configured to Support only RSNA connections(RSNA enabled)
Low [1]  Firmware does not support MFP/IEEE 802.11w and validation
 Firmware configured to Support only RSNA connections(RSNA enabled)
Very Low [0] Firmware not configured to Support only RSNA connections(Pre-RSNA enabled)
TABLE 6: VALUE FUNCTION TABLE FOR AUTHENTICATION AND ACCESS CONTROL MECHANISM
Security Strength
Weight of influence
Description of Security Feature/ Configuration
High [3] IEEE 802.1x With EAP method
High [3] Captive portal and IEEE 802.1x With EAP Method
Moderate [2] Captive Portal and Pre-shared Key
Low [1] Captive Portal Only
Low [1] Pre-shared Key Only
Very Low [0] MAC address filtering
International Journal of Innovative Research in Information Security (IJIRIS) ISSN: 2349-7017
Issue 01, Volume 6 (January 2019) www.ijiris.com
IJIRIS: Mendeley (Elsevier Indexed) CiteFactor Journal Citations Impact Factor 1.23
Impact Factor Value – SJIF: Innospace, Morocco (2016): 4.651| Indexcopernicus: (ICV 2016): 88.20
© 2014- 19, IJIRIS- All Rights Reserved Page -24
Very Low [0] Open SSID
Very Low [0] PIN based authentication(WPS)
Very Low [0] Button press based authentication(WPS)
D. Combining and Propagating the Security levels of the Security Features/Configurations in the Model
The model determines the overall security level of an implementation by aggregating security levels of individual
components based on security features and configurations set in them. The components whose security levels are
aggregated are: client utility, client driver, access point utility, authentication server, authentication & access
control mechanism, user database, cipher suite and authentication credentials. Fig. 1 shows the structure of
hierarchy and direction of propagation of security levels. The aggregation of security levels is hierarchical (parent-
child structure) and is done bottom up.
.
Fig 1: Structure of Hierarchy and Direction of Propagation of Security levels
TABLE 7: VALUE FUNCTION TABLE FOR AUTHENTICATION SERVER
Security Strength
Weight of influence
Description of Security Feature/ Configuration
High [3] DIAMETER. Configured to Support mutual authentication
Moderate [2] RADIUS. Configured to Support mutual authentication
Low [1] DIAMETER. Not Configured to Support mutual authentication
Low [1] RADIUS. Not Configured to Support mutual authentication
Low [1] KERBEROS
Very Low [0] None/Independent on each Access point
Client
Utility
Authentication
Credentials
Overall WLAN
Security
User Database
System
Authentication
server Authentication
and access
control
Client
Driver
Trusted
Computing
Base
(TCB)
Wireless
Trusted
Path (WTP)
Back-end
authentication
Systems
Front-end
System
software
Access point
Utility
Cipher
Suite
International Journal of Innovative Research in Information Security (IJIRIS) ISSN: 2349-7017
Issue 01, Volume 6 (January 2019) www.ijiris.com
IJIRIS: Mendeley (Elsevier Indexed) CiteFactor Journal Citations Impact Factor 1.23
Impact Factor Value – SJIF: Innospace, Morocco (2016): 4.651| Indexcopernicus: (ICV 2016): 88.20
© 2014- 19, IJIRIS- All Rights Reserved Page -25
TABLE 8: VALUE FUNCTION TABLE FOR USER DATABASE SYSTEM
The combination and propagation algorithm used to aggregate security levels is illustrated below:
(a) Starting with terminal/leaf nodes, every subtree has a parent node R and a set of child nodes C. The child
nodes may have a negative or positive type of influence on R. A positive influence of child Ci on R means that
when security level of Ci is high, that of R is influenced to move upwards too. On the other hand, a negative
influence of child Ci on R means that when security level of Ci is high, that of R is influenced to move
downwards.
(b) If a parent node R has at least one child with very low security level, the model gives a notification that the
security feature or configuration is not recommended. This is because this feature renders the security of the
entire WLAN very weak.
(c) If a parent node R has k child nodes with combination of positive and negative influences and of strength Si
(High, Moderate, and Low) and values of security level for all child nodes are known, the value of the parent
node, VR is computed based on the following weighted average.
∑k (Si * Vi)
VR = i=1
∑k (Si)
i=1
Where: Si refers to the strength of the security influence of a child C i on parent R which is equal to 1, 2, or 3 if the
influence of the child is low, moderate, and high respectively. Vi refers to the value of child Ci and is dependent on
Si and type of influence of child Ci on parent node R. If the child node Ci has a positive influence on Parent node R
and the strength of influence (Si) of node Ci is low, moderate, or high then Vi is equal to 1, 2, and 3 respectively. On
the other hand, if the child node Ci has a negative influence on parent node R and strength of influence (Si) of node
Ci is low, moderate or high then Vi is equal to 3, 2 or 1 respectively
(d) Once a value VR is determined, thresholds have been set to decide the values of VR as follows:
(i) If 1<=VR <1.5, then the value of R is low
(ii) If 1.5 <= VR <= 2.5, then the value of R is moderate
(iii) If 2.5 < VR<=3, then the value of R is High.
(e)The process is repeated recursively up the hierarchy until a value for the root node is established.
Based on the algorithm described above the security levels of the eight model components are propagated as
follows:
(i) Security level of client utility, client driver and access point utility are aggregated to derive a composite
security levels for front-end system software subtree.
(ii) Security levels of authentication server, authentication & access control mechanism and user database
are aggregated to derive a composite security levels for back-end authentication systems subtree.
(iii) The derived security levels for front-end system software is aggregated with that of back-end
authentication systems to derive a composite security levels for trusted computing base (TCB).
(iv) Security levels of cipher suite and authentication credentials are aggregated to derive a composite security
levels for wireless path subtree.
(v) Finally, the security levels of trusted computing base (TCB) and that of wireless path are aggregated to
form an overall security levels of the implementation.
The model therefore provides a what-if simulation of the security level expected from a combination of the
influences of the selected security features and/or configurations.
Security Strength
Weight of influence
Description of Security Feature/Configuration
High [3] Distributed Database Servers with an Intrusion Detection System(IDS)
Moderate [2] Distributed Database Servers without an Intrusion Detection System(IDS)
Moderate [2] Centralized Database Server with an Intrusion Detection System(IDS)
Low [1] Centralized Database Server without an Intrusion Detection System(IDS)
Very Low [0] None/Independent on each Access point
International Journal of Innovative Research in Information Security (IJIRIS) ISSN: 2349-7017
Issue 01, Volume 6 (January 2019) www.ijiris.com
IJIRIS: Mendeley (Elsevier Indexed) CiteFactor Journal Citations Impact Factor 1.23
Impact Factor Value – SJIF: Innospace, Morocco (2016): 4.651| Indexcopernicus: (ICV 2016): 88.20
© 2014- 19, IJIRIS- All Rights Reserved Page -26
E. Theoretical Analysis of the Model Algorithm Using Degenerate and Trace Tests
Degenerate and traces validation was used to perform theoretical validation of the model. Degenerate validation
involved analysis of input values to test the corresponding changes in the internal components e.g. does the
security level/strength go up when more secure configurations and security features are selected and vice versa?
Does the security level/strength go down when highly vulnerable configurations and security features are selected
and vice versa?
Validation using traces on the other hand was done to determine whether the mathematical logic of the technique
for propagation of values in the model maintains necessary accuracy and consistency. To achieve this, the
researcher tracked entities’ strength and type of security influence through each sub-model and the overall model
and analyzed the results.
Table 9 shows results for a one component sub-model when type of influence is positive and a similar scenario
when type of influence is negative. The following can be deduced from the table.
i. When the security strength of the child component is low, moderate or high, the security strength of the
root/parent component is low, medium and high respectively when the type of influence is positive (+ve).
ii. When the security strength of the child component is low, medium or high, the security strength of the
root/parent component is high, medium and low respectively when the type of influence is negative (-ve).
TABLE 9: ONE COMPONENT SUB-MODEL FOR BOTH POSITIVE AND NEGATIVE INFLUENCE
Strength of
component
Strength of sub-model Root(P) When
Relationship is +ve
Strength of sub-model Root(P) When
Relationship is -ve
Low [1] Low [1] High [3]
Moderate [2] Moderate [2] Moderate [2]
High [3] High [3] Low [1]
Table 10 shows a summary of results for a two component sub-model when type of influence is positive and a
similar scenario when type of influence is negative.
The following can be deduced from the table.
i. When all the two child components have low, moderate or high security strength, the root/overall security
strength is also low, moderate and high respectively.
ii. Where one of the components has high security strength, and the other low security strength, the
root/overall security strength is moderate.
iii. When the relationship changes to type negative, the results are inverted.
TABLE 10: TWO COMPONENT SUB-MODEL FOR BOTH POSITIVE AND NEGATIVE INFLUENCE.
Strength of
component 1(S1)
Strength of
component 2(S2)
Strength of sub-model Root(P)
when Relationship type +ve
Strength of Sub-model Root(P)
when Relationship type -ve
Low [1] Low [1] Low [1.00] High [3.00]
Low [1] Moderate[2] Moderate [1.67] Moderate [2.33]
Low [1] High [3] Moderate [2.50] Moderate [1.50]
Moderate[2] Low [1] Moderate [1.67] Moderate [2.33]
Moderate[2] Moderate[2] Moderate [2.00] Moderate [2.00]
Moderate[2] High [3] High [2.60] Low [1.40]
High [3] Low [1] Moderate [2.50] Moderate [1.50]
High [3] Moderate[2] High [2.60] Low [1.40]
High [3] High [3] High [3.00] Low [1.00]
Table11 shows a summary of results for a three component sub-model when type of influence is positive and a
similar scenario when type of influence is negative.
The following can be deduced from the table.
International Journal of Innovative Research in Information Security (IJIRIS) ISSN: 2349-7017
Issue 01, Volume 6 (January 2019) www.ijiris.com
IJIRIS: Mendeley (Elsevier Indexed) CiteFactor Journal Citations Impact Factor 1.23
Impact Factor Value – SJIF: Innospace, Morocco (2016): 4.651| Indexcopernicus: (ICV 2016): 88.20
© 2014- 19, IJIRIS- All Rights Reserved Page -27
i. When all the three components have low, moderate or high security strength, the root/overall security
strength is low, moderate and high respectively.
ii. For any given set of component input values, the results of situations where the relationship is of type
positive(+ve) are the inversion of the results under the same component values if relationship is of type
negative(-ve) as expected.
TABLE 11: THREE COMPONENT SUB-MODEL FOR BOTH POSITIVE AND NEGATIVE INFLUENCE
It was also observed that when all the eight components have low, moderate or high security strengths, the overall
security strength is low, moderate and high respectively. These observations indicate that the logic of the
algorithm for propagation of component values maintains accuracy and consistency as required and that the
model results are consistent with the design principles.
V. CONCLUSION
This paper has brought into perspective various security features and configurations commonly implemented in
WLANs and their aggregated security levels. It has also proposed a model that enables an implementer to establish
the level of security provided by the security features and configurations implemented in a WLAN. The results
from the theoretical analysis of the model indicate that the logic of the algorithm for propagation of component
values maintains accuracy and consistency as required and that the model results are consistent with the design
principles. This model can be used for WLAN security design, implementation and analysis.
Strength of
Component 1 (S1)
Strength of
Component 2(S2)
Strength of
Component 3(S3)
Strength of sub model
Root (P) when
Relationship type +ve
Strength of sub model
Root (P) when
Relationship type –ve
Low [1] Low [1] Low [1] Low [1.00] High [3.00]
Low [1] Low [1] Moderate[2] Moderate [1.50] Moderate [2.50]
Low [1] Low [1] High [3] Moderate [2.20] Moderate [1.80]
Low [1] Moderate[2] Low [1] Moderate [1.50] Moderate [2.50]
Low [1] Moderate[2] Moderate[2] Moderate [1.80] Moderate [2.20]
Low [1] Moderate[2] High [3] Moderate [2.33] Moderate [1.67]
Low [1] High [3] Low [1] Moderate [2.20] Moderate [1.80]
Low [1] High [3] Moderate[2] Moderate [2.33] Moderate [1.67]
Low [1] High [3] High [3] High [2.71] Low [1.29]
Moderate [2] Low [1] Low [1] Moderate [1.50] Moderate [2.50]
Moderate [2] Low [1] Moderate[2] Moderate [1.80] Moderate [2.20]
Moderate [2] Low [1] High [3] Moderate [2.33] Moderate [1.67]
Moderate [2] Moderate [2] Low [1] Moderate [1.80] Moderate [2.20]
Moderate [2] Moderate [2] Moderate[2] Moderate [2.00] Moderate [2.00]
Moderate [2] Moderate [2] High [3] Moderate [2.43] Moderate [1.57]
Moderate [2] High [3] Low [1] Moderate [2.33] Moderate [1.67]
Moderate [2] High [3] Moderate[2] Moderate [2.43] Moderate [1.57]
Moderate [2] High [3] High [3] High [2.75] Low [1.25]
High [3] Low [1] Low [1] Moderate [2.20] Moderate [1.80]
High [3] Low [1] Moderate [2] Moderate [2.33] Moderate [1.67]
High [3] Low [1] High [3] High [2.71] Low [1.29]
High [3] Moderate[2] Low [1] Moderate [2.33] Moderate [1.67]
High [3] Moderate[2] Moderate [2] Moderate [2.43] Moderate [1.57]
High [3] Moderate[2] High [3] High [2.75] Low [1.25]
High [3] High [3] Low [1] High [2.71] Low [1.29]
High [3] High [3] Moderate[2] High [2.75] Low [1.25]
High [3] High [3] High [3] High [3.00] Low [1.00]
International Journal of Innovative Research in Information Security (IJIRIS) ISSN: 2349-7017
Issue 01, Volume 6 (January 2019) www.ijiris.com
IJIRIS: Mendeley (Elsevier Indexed) CiteFactor Journal Citations Impact Factor 1.23
Impact Factor Value – SJIF: Innospace, Morocco (2016): 4.651| Indexcopernicus: (ICV 2016): 88.20
© 2014- 19, IJIRIS- All Rights Reserved Page -28
Particularly, when implemented in form of a software tool that mines data directly from devices, network
administrators can use it to establish highly vulnerable security features and configurations in an implementation,
visualize the security implications of selecting certain security features and configurations. Network
administrators can also use it as an audit tool for WLAN security and then recommend an appropriate security for
an organization’s security. Researchers in the area of WLAN security can also use it as a data collection tool.
REFERENCES
1. Khidir, M. and Ali, A. (2011).A Comparative Study of Authentication Methods for Wi-Fi Networks. In:
Proceedings of International Conference on Computational Intelligence, Communication Systems and Networks
[Online], pp. 190-194.
2. Sheila, F., Bernard, E., Les, O., Karen, S.(2007). Establishing Wireless Robust security Networks: A Guide to IEEE
802.11i, NIST.US.
3. Kwang-Hyun, B., Sean, W. and David, K. (2004). A Survey of WPA and 802.11i RSN Authentication Protocols.
Dartmouth College: Computer Science Technial Report TR2004-524. Available at:
www.cs.dartmouth.edu/~dfk/papers/baek-survey-tr.pdf
4. Borisov,N. , Goldberg,I. and Wagner, D.(2001). Intercepting Mobile Communications: The Insecurity of 802.11.
In: Proceedings of 7th Annual International Conference on Mobile Computing and Networking, Rome, Italy:
ACM Press.
5. Yizhan, S. (2006). Complexity of System Configuration Management, PhD thesis, Tufts University.

More Related Content

What's hot

IRJET- 3 Juncture based Issuer Driven Pull Out System using Distributed Servers
IRJET- 3 Juncture based Issuer Driven Pull Out System using Distributed ServersIRJET- 3 Juncture based Issuer Driven Pull Out System using Distributed Servers
IRJET- 3 Juncture based Issuer Driven Pull Out System using Distributed ServersIRJET Journal
 
Intrusion Detection System using Data Mining
Intrusion Detection System using Data MiningIntrusion Detection System using Data Mining
Intrusion Detection System using Data MiningIRJET Journal
 
A hierarchical security framework for defending against sophisticated attacks...
A hierarchical security framework for defending against sophisticated attacks...A hierarchical security framework for defending against sophisticated attacks...
A hierarchical security framework for defending against sophisticated attacks...redpel dot com
 
Deterring hacking strategies via
Deterring hacking strategies viaDeterring hacking strategies via
Deterring hacking strategies viaIJNSA Journal
 
Evaluation of enhanced security solutions in
Evaluation of enhanced security solutions inEvaluation of enhanced security solutions in
Evaluation of enhanced security solutions inIJNSA Journal
 
The Next Generation Cognitive Security Operations Center: Network Flow Forens...
The Next Generation Cognitive Security Operations Center: Network Flow Forens...The Next Generation Cognitive Security Operations Center: Network Flow Forens...
The Next Generation Cognitive Security Operations Center: Network Flow Forens...Konstantinos Demertzis
 
Cloud Monitoring And Forensic Using Security Metrics
Cloud Monitoring And Forensic Using Security MetricsCloud Monitoring And Forensic Using Security Metrics
Cloud Monitoring And Forensic Using Security MetricsSandeep Saxena
 
A Study and Comparative analysis of Conditional Random Fields for Intrusion d...
A Study and Comparative analysis of Conditional Random Fields for Intrusion d...A Study and Comparative analysis of Conditional Random Fields for Intrusion d...
A Study and Comparative analysis of Conditional Random Fields for Intrusion d...IJORCS
 
A Collaborative Intrusion Detection System for Cloud Computing
A Collaborative Intrusion Detection System for Cloud ComputingA Collaborative Intrusion Detection System for Cloud Computing
A Collaborative Intrusion Detection System for Cloud Computingijsrd.com
 
Gartner Newsletter: Cisco TrustSec Deployed Across Enterprise Campus, Branch ...
Gartner Newsletter: Cisco TrustSec Deployed Across Enterprise Campus, Branch ...Gartner Newsletter: Cisco TrustSec Deployed Across Enterprise Campus, Branch ...
Gartner Newsletter: Cisco TrustSec Deployed Across Enterprise Campus, Branch ...Cisco Security
 
Cloud Forensics- An IS Approach
Cloud Forensics- An IS ApproachCloud Forensics- An IS Approach
Cloud Forensics- An IS ApproachIOSR Journals
 
IRJET- Security Risk Assessment on Social Media using Artificial Intellig...
IRJET-  	  Security Risk Assessment on Social Media using Artificial Intellig...IRJET-  	  Security Risk Assessment on Social Media using Artificial Intellig...
IRJET- Security Risk Assessment on Social Media using Artificial Intellig...IRJET Journal
 
an efficient spam detection technique for io t devices using machine learning
an efficient spam detection technique for io t devices using machine learningan efficient spam detection technique for io t devices using machine learning
an efficient spam detection technique for io t devices using machine learningVenkat Projects
 
Encryption Security in SCADA Networks
Encryption Security in SCADA NetworksEncryption Security in SCADA Networks
Encryption Security in SCADA NetworksIJRES Journal
 
3778975074 january march 2015 1
3778975074 january march 2015 13778975074 january march 2015 1
3778975074 january march 2015 1nicfs
 
Malicious Code Intrusion Detection using Machine Learning and Indicators of C...
Malicious Code Intrusion Detection using Machine Learning and Indicators of C...Malicious Code Intrusion Detection using Machine Learning and Indicators of C...
Malicious Code Intrusion Detection using Machine Learning and Indicators of C...IJCSIS Research Publications
 
IMPROVED IDS USING LAYERED CRFS WITH LOGON RESTRICTIONS AND MOBILE ALERTS BAS...
IMPROVED IDS USING LAYERED CRFS WITH LOGON RESTRICTIONS AND MOBILE ALERTS BAS...IMPROVED IDS USING LAYERED CRFS WITH LOGON RESTRICTIONS AND MOBILE ALERTS BAS...
IMPROVED IDS USING LAYERED CRFS WITH LOGON RESTRICTIONS AND MOBILE ALERTS BAS...IJNSA Journal
 
Use of network forensic mechanisms to formulate network security
Use of network forensic mechanisms to formulate network securityUse of network forensic mechanisms to formulate network security
Use of network forensic mechanisms to formulate network securityIJMIT JOURNAL
 

What's hot (20)

IRJET- 3 Juncture based Issuer Driven Pull Out System using Distributed Servers
IRJET- 3 Juncture based Issuer Driven Pull Out System using Distributed ServersIRJET- 3 Juncture based Issuer Driven Pull Out System using Distributed Servers
IRJET- 3 Juncture based Issuer Driven Pull Out System using Distributed Servers
 
Utilization of Encryption for Security in SCADA Networks
Utilization of Encryption for Security in SCADA NetworksUtilization of Encryption for Security in SCADA Networks
Utilization of Encryption for Security in SCADA Networks
 
Intrusion Detection System using Data Mining
Intrusion Detection System using Data MiningIntrusion Detection System using Data Mining
Intrusion Detection System using Data Mining
 
A hierarchical security framework for defending against sophisticated attacks...
A hierarchical security framework for defending against sophisticated attacks...A hierarchical security framework for defending against sophisticated attacks...
A hierarchical security framework for defending against sophisticated attacks...
 
Deterring hacking strategies via
Deterring hacking strategies viaDeterring hacking strategies via
Deterring hacking strategies via
 
Evaluation of enhanced security solutions in
Evaluation of enhanced security solutions inEvaluation of enhanced security solutions in
Evaluation of enhanced security solutions in
 
The Next Generation Cognitive Security Operations Center: Network Flow Forens...
The Next Generation Cognitive Security Operations Center: Network Flow Forens...The Next Generation Cognitive Security Operations Center: Network Flow Forens...
The Next Generation Cognitive Security Operations Center: Network Flow Forens...
 
Cloud Monitoring And Forensic Using Security Metrics
Cloud Monitoring And Forensic Using Security MetricsCloud Monitoring And Forensic Using Security Metrics
Cloud Monitoring And Forensic Using Security Metrics
 
A Study and Comparative analysis of Conditional Random Fields for Intrusion d...
A Study and Comparative analysis of Conditional Random Fields for Intrusion d...A Study and Comparative analysis of Conditional Random Fields for Intrusion d...
A Study and Comparative analysis of Conditional Random Fields for Intrusion d...
 
A Collaborative Intrusion Detection System for Cloud Computing
A Collaborative Intrusion Detection System for Cloud ComputingA Collaborative Intrusion Detection System for Cloud Computing
A Collaborative Intrusion Detection System for Cloud Computing
 
Gartner Newsletter: Cisco TrustSec Deployed Across Enterprise Campus, Branch ...
Gartner Newsletter: Cisco TrustSec Deployed Across Enterprise Campus, Branch ...Gartner Newsletter: Cisco TrustSec Deployed Across Enterprise Campus, Branch ...
Gartner Newsletter: Cisco TrustSec Deployed Across Enterprise Campus, Branch ...
 
Cloud Forensics- An IS Approach
Cloud Forensics- An IS ApproachCloud Forensics- An IS Approach
Cloud Forensics- An IS Approach
 
IRJET- Security Risk Assessment on Social Media using Artificial Intellig...
IRJET-  	  Security Risk Assessment on Social Media using Artificial Intellig...IRJET-  	  Security Risk Assessment on Social Media using Artificial Intellig...
IRJET- Security Risk Assessment on Social Media using Artificial Intellig...
 
an efficient spam detection technique for io t devices using machine learning
an efficient spam detection technique for io t devices using machine learningan efficient spam detection technique for io t devices using machine learning
an efficient spam detection technique for io t devices using machine learning
 
Encryption Security in SCADA Networks
Encryption Security in SCADA NetworksEncryption Security in SCADA Networks
Encryption Security in SCADA Networks
 
3778975074 january march 2015 1
3778975074 january march 2015 13778975074 january march 2015 1
3778975074 january march 2015 1
 
Malicious Code Intrusion Detection using Machine Learning and Indicators of C...
Malicious Code Intrusion Detection using Machine Learning and Indicators of C...Malicious Code Intrusion Detection using Machine Learning and Indicators of C...
Malicious Code Intrusion Detection using Machine Learning and Indicators of C...
 
IMPROVED IDS USING LAYERED CRFS WITH LOGON RESTRICTIONS AND MOBILE ALERTS BAS...
IMPROVED IDS USING LAYERED CRFS WITH LOGON RESTRICTIONS AND MOBILE ALERTS BAS...IMPROVED IDS USING LAYERED CRFS WITH LOGON RESTRICTIONS AND MOBILE ALERTS BAS...
IMPROVED IDS USING LAYERED CRFS WITH LOGON RESTRICTIONS AND MOBILE ALERTS BAS...
 
Ijnsa050214
Ijnsa050214Ijnsa050214
Ijnsa050214
 
Use of network forensic mechanisms to formulate network security
Use of network forensic mechanisms to formulate network securityUse of network forensic mechanisms to formulate network security
Use of network forensic mechanisms to formulate network security
 

Similar to A MODEL BASED APPROACH FOR IMPLEMENTING WLAN SECURITY

AN EXTENDED SECURITY MEASUREMENT FRAMEWORK FOR OPEN-SOURCE ENTERPRISE RESOURC...
AN EXTENDED SECURITY MEASUREMENT FRAMEWORK FOR OPEN-SOURCE ENTERPRISE RESOURC...AN EXTENDED SECURITY MEASUREMENT FRAMEWORK FOR OPEN-SOURCE ENTERPRISE RESOURC...
AN EXTENDED SECURITY MEASUREMENT FRAMEWORK FOR OPEN-SOURCE ENTERPRISE RESOURC...IJNSA Journal
 
Evaluation of Enhanced Security Solutions in 802.11-Based Networks
Evaluation of Enhanced Security Solutions in 802.11-Based NetworksEvaluation of Enhanced Security Solutions in 802.11-Based Networks
Evaluation of Enhanced Security Solutions in 802.11-Based NetworksIJNSA Journal
 
Security issues in grid computing
Security issues in grid computingSecurity issues in grid computing
Security issues in grid computingijcsa
 
Access Control For Local Area Network Performance Essay
Access Control For Local Area Network Performance EssayAccess Control For Local Area Network Performance Essay
Access Control For Local Area Network Performance EssayDotha Keller
 
Android Based Total Security for System Authentication
Android Based Total Security for System AuthenticationAndroid Based Total Security for System Authentication
Android Based Total Security for System AuthenticationIJERA Editor
 
METRICS FOR EVALUATING ALERTS IN INTRUSION DETECTION SYSTEMS
METRICS FOR EVALUATING ALERTS IN INTRUSION DETECTION SYSTEMSMETRICS FOR EVALUATING ALERTS IN INTRUSION DETECTION SYSTEMS
METRICS FOR EVALUATING ALERTS IN INTRUSION DETECTION SYSTEMSIJNSA Journal
 
Multi-stage secure clusterhead selection using discrete rule-set against unkn...
Multi-stage secure clusterhead selection using discrete rule-set against unkn...Multi-stage secure clusterhead selection using discrete rule-set against unkn...
Multi-stage secure clusterhead selection using discrete rule-set against unkn...IJECEIAES
 
Security Issues and Challenges in Internet of Things – A Review
Security Issues and Challenges in Internet of Things – A ReviewSecurity Issues and Challenges in Internet of Things – A Review
Security Issues and Challenges in Internet of Things – A ReviewIJERA Editor
 
Vulnerability Management System
Vulnerability Management SystemVulnerability Management System
Vulnerability Management SystemIRJET Journal
 
INFORMATION AND COMMUNICATION SECURITY MECHANISMS FOR MICROSERVICES-BASED SYS...
INFORMATION AND COMMUNICATION SECURITY MECHANISMS FOR MICROSERVICES-BASED SYS...INFORMATION AND COMMUNICATION SECURITY MECHANISMS FOR MICROSERVICES-BASED SYS...
INFORMATION AND COMMUNICATION SECURITY MECHANISMS FOR MICROSERVICES-BASED SYS...IJNSA Journal
 
A CRYPTOGRAPHIC MUTUAL AUTHENTICATION SCHEME FOR WEB APPLICATIONS
A CRYPTOGRAPHIC MUTUAL AUTHENTICATION SCHEME FOR WEB APPLICATIONSA CRYPTOGRAPHIC MUTUAL AUTHENTICATION SCHEME FOR WEB APPLICATIONS
A CRYPTOGRAPHIC MUTUAL AUTHENTICATION SCHEME FOR WEB APPLICATIONSIJNSA Journal
 
IRJET - IDS for Wifi Security
IRJET -  	  IDS for Wifi SecurityIRJET -  	  IDS for Wifi Security
IRJET - IDS for Wifi SecurityIRJET Journal
 
Standards and methodology for application security assessment
Standards and methodology for application security assessment Standards and methodology for application security assessment
Standards and methodology for application security assessment Mykhailo Antonishyn
 
MAR SECURITY: IMPROVED SECURITY MECHANISM FOR EMERGENCY MESSAGES OF VANET USI...
MAR SECURITY: IMPROVED SECURITY MECHANISM FOR EMERGENCY MESSAGES OF VANET USI...MAR SECURITY: IMPROVED SECURITY MECHANISM FOR EMERGENCY MESSAGES OF VANET USI...
MAR SECURITY: IMPROVED SECURITY MECHANISM FOR EMERGENCY MESSAGES OF VANET USI...IJCNCJournal
 
Ontology-based context-sensitive software security knowledge management model...
Ontology-based context-sensitive software security knowledge management model...Ontology-based context-sensitive software security knowledge management model...
Ontology-based context-sensitive software security knowledge management model...IJECEIAES
 
Sheet1Country ACountry BProduct 110 Reds9 GreensProduct 22 Reds2.2.docx
Sheet1Country ACountry BProduct 110 Reds9 GreensProduct 22 Reds2.2.docxSheet1Country ACountry BProduct 110 Reds9 GreensProduct 22 Reds2.2.docx
Sheet1Country ACountry BProduct 110 Reds9 GreensProduct 22 Reds2.2.docxedgar6wallace88877
 
A Review On Data Security In Cloud Computing
A Review On Data Security In Cloud ComputingA Review On Data Security In Cloud Computing
A Review On Data Security In Cloud ComputingYolanda Ivey
 

Similar to A MODEL BASED APPROACH FOR IMPLEMENTING WLAN SECURITY (20)

AN EXTENDED SECURITY MEASUREMENT FRAMEWORK FOR OPEN-SOURCE ENTERPRISE RESOURC...
AN EXTENDED SECURITY MEASUREMENT FRAMEWORK FOR OPEN-SOURCE ENTERPRISE RESOURC...AN EXTENDED SECURITY MEASUREMENT FRAMEWORK FOR OPEN-SOURCE ENTERPRISE RESOURC...
AN EXTENDED SECURITY MEASUREMENT FRAMEWORK FOR OPEN-SOURCE ENTERPRISE RESOURC...
 
Evaluation of Enhanced Security Solutions in 802.11-Based Networks
Evaluation of Enhanced Security Solutions in 802.11-Based NetworksEvaluation of Enhanced Security Solutions in 802.11-Based Networks
Evaluation of Enhanced Security Solutions in 802.11-Based Networks
 
Security issues in grid computing
Security issues in grid computingSecurity issues in grid computing
Security issues in grid computing
 
Access Control For Local Area Network Performance Essay
Access Control For Local Area Network Performance EssayAccess Control For Local Area Network Performance Essay
Access Control For Local Area Network Performance Essay
 
Android Based Total Security for System Authentication
Android Based Total Security for System AuthenticationAndroid Based Total Security for System Authentication
Android Based Total Security for System Authentication
 
Secure final
Secure finalSecure final
Secure final
 
METRICS FOR EVALUATING ALERTS IN INTRUSION DETECTION SYSTEMS
METRICS FOR EVALUATING ALERTS IN INTRUSION DETECTION SYSTEMSMETRICS FOR EVALUATING ALERTS IN INTRUSION DETECTION SYSTEMS
METRICS FOR EVALUATING ALERTS IN INTRUSION DETECTION SYSTEMS
 
Multi-stage secure clusterhead selection using discrete rule-set against unkn...
Multi-stage secure clusterhead selection using discrete rule-set against unkn...Multi-stage secure clusterhead selection using discrete rule-set against unkn...
Multi-stage secure clusterhead selection using discrete rule-set against unkn...
 
network security.pdf
network security.pdfnetwork security.pdf
network security.pdf
 
Security Issues and Challenges in Internet of Things – A Review
Security Issues and Challenges in Internet of Things – A ReviewSecurity Issues and Challenges in Internet of Things – A Review
Security Issues and Challenges in Internet of Things – A Review
 
Vulnerability Management System
Vulnerability Management SystemVulnerability Management System
Vulnerability Management System
 
INFORMATION AND COMMUNICATION SECURITY MECHANISMS FOR MICROSERVICES-BASED SYS...
INFORMATION AND COMMUNICATION SECURITY MECHANISMS FOR MICROSERVICES-BASED SYS...INFORMATION AND COMMUNICATION SECURITY MECHANISMS FOR MICROSERVICES-BASED SYS...
INFORMATION AND COMMUNICATION SECURITY MECHANISMS FOR MICROSERVICES-BASED SYS...
 
A CRYPTOGRAPHIC MUTUAL AUTHENTICATION SCHEME FOR WEB APPLICATIONS
A CRYPTOGRAPHIC MUTUAL AUTHENTICATION SCHEME FOR WEB APPLICATIONSA CRYPTOGRAPHIC MUTUAL AUTHENTICATION SCHEME FOR WEB APPLICATIONS
A CRYPTOGRAPHIC MUTUAL AUTHENTICATION SCHEME FOR WEB APPLICATIONS
 
IRJET - IDS for Wifi Security
IRJET -  	  IDS for Wifi SecurityIRJET -  	  IDS for Wifi Security
IRJET - IDS for Wifi Security
 
Standards and methodology for application security assessment
Standards and methodology for application security assessment Standards and methodology for application security assessment
Standards and methodology for application security assessment
 
MAR SECURITY: IMPROVED SECURITY MECHANISM FOR EMERGENCY MESSAGES OF VANET USI...
MAR SECURITY: IMPROVED SECURITY MECHANISM FOR EMERGENCY MESSAGES OF VANET USI...MAR SECURITY: IMPROVED SECURITY MECHANISM FOR EMERGENCY MESSAGES OF VANET USI...
MAR SECURITY: IMPROVED SECURITY MECHANISM FOR EMERGENCY MESSAGES OF VANET USI...
 
Ontology-based context-sensitive software security knowledge management model...
Ontology-based context-sensitive software security knowledge management model...Ontology-based context-sensitive software security knowledge management model...
Ontology-based context-sensitive software security knowledge management model...
 
NSA and PT
NSA and PTNSA and PT
NSA and PT
 
Sheet1Country ACountry BProduct 110 Reds9 GreensProduct 22 Reds2.2.docx
Sheet1Country ACountry BProduct 110 Reds9 GreensProduct 22 Reds2.2.docxSheet1Country ACountry BProduct 110 Reds9 GreensProduct 22 Reds2.2.docx
Sheet1Country ACountry BProduct 110 Reds9 GreensProduct 22 Reds2.2.docx
 
A Review On Data Security In Cloud Computing
A Review On Data Security In Cloud ComputingA Review On Data Security In Cloud Computing
A Review On Data Security In Cloud Computing
 

More from AM Publications

DEVELOPMENT OF TODDLER FAMILY CADRE TRAINING BASED ON ANDROID APPLICATIONS IN...
DEVELOPMENT OF TODDLER FAMILY CADRE TRAINING BASED ON ANDROID APPLICATIONS IN...DEVELOPMENT OF TODDLER FAMILY CADRE TRAINING BASED ON ANDROID APPLICATIONS IN...
DEVELOPMENT OF TODDLER FAMILY CADRE TRAINING BASED ON ANDROID APPLICATIONS IN...AM Publications
 
TESTING OF COMPOSITE ON DROP-WEIGHT IMPACT TESTING AND DAMAGE IDENTIFICATION ...
TESTING OF COMPOSITE ON DROP-WEIGHT IMPACT TESTING AND DAMAGE IDENTIFICATION ...TESTING OF COMPOSITE ON DROP-WEIGHT IMPACT TESTING AND DAMAGE IDENTIFICATION ...
TESTING OF COMPOSITE ON DROP-WEIGHT IMPACT TESTING AND DAMAGE IDENTIFICATION ...AM Publications
 
THE USE OF FRACTAL GEOMETRY IN TILING MOTIF DESIGN
THE USE OF FRACTAL GEOMETRY IN TILING MOTIF DESIGNTHE USE OF FRACTAL GEOMETRY IN TILING MOTIF DESIGN
THE USE OF FRACTAL GEOMETRY IN TILING MOTIF DESIGNAM Publications
 
TWO-DIMENSIONAL INVERSION FINITE ELEMENT MODELING OF MAGNETOTELLURIC DATA: CA...
TWO-DIMENSIONAL INVERSION FINITE ELEMENT MODELING OF MAGNETOTELLURIC DATA: CA...TWO-DIMENSIONAL INVERSION FINITE ELEMENT MODELING OF MAGNETOTELLURIC DATA: CA...
TWO-DIMENSIONAL INVERSION FINITE ELEMENT MODELING OF MAGNETOTELLURIC DATA: CA...AM Publications
 
USING THE GENETIC ALGORITHM TO OPTIMIZE LASER WELDING PARAMETERS FOR MARTENSI...
USING THE GENETIC ALGORITHM TO OPTIMIZE LASER WELDING PARAMETERS FOR MARTENSI...USING THE GENETIC ALGORITHM TO OPTIMIZE LASER WELDING PARAMETERS FOR MARTENSI...
USING THE GENETIC ALGORITHM TO OPTIMIZE LASER WELDING PARAMETERS FOR MARTENSI...AM Publications
 
ANALYSIS AND DESIGN E-MARKETPLACE FOR MICRO, SMALL AND MEDIUM ENTERPRISES
ANALYSIS AND DESIGN E-MARKETPLACE FOR MICRO, SMALL AND MEDIUM ENTERPRISESANALYSIS AND DESIGN E-MARKETPLACE FOR MICRO, SMALL AND MEDIUM ENTERPRISES
ANALYSIS AND DESIGN E-MARKETPLACE FOR MICRO, SMALL AND MEDIUM ENTERPRISESAM Publications
 
REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS
REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS
REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS AM Publications
 
EVALUATE THE STRAIN ENERGY ERROR FOR THE LASER WELD BY THE H-REFINEMENT OF TH...
EVALUATE THE STRAIN ENERGY ERROR FOR THE LASER WELD BY THE H-REFINEMENT OF TH...EVALUATE THE STRAIN ENERGY ERROR FOR THE LASER WELD BY THE H-REFINEMENT OF TH...
EVALUATE THE STRAIN ENERGY ERROR FOR THE LASER WELD BY THE H-REFINEMENT OF TH...AM Publications
 
HMM APPLICATION IN ISOLATED WORD SPEECH RECOGNITION
HMM APPLICATION IN ISOLATED WORD SPEECH RECOGNITIONHMM APPLICATION IN ISOLATED WORD SPEECH RECOGNITION
HMM APPLICATION IN ISOLATED WORD SPEECH RECOGNITIONAM Publications
 
PEDESTRIAN DETECTION IN LOW RESOLUTION VIDEOS USING A MULTI-FRAME HOG-BASED D...
PEDESTRIAN DETECTION IN LOW RESOLUTION VIDEOS USING A MULTI-FRAME HOG-BASED D...PEDESTRIAN DETECTION IN LOW RESOLUTION VIDEOS USING A MULTI-FRAME HOG-BASED D...
PEDESTRIAN DETECTION IN LOW RESOLUTION VIDEOS USING A MULTI-FRAME HOG-BASED D...AM Publications
 
EFFECT OF SILICON - RUBBER (SR) SHEETS AS AN ALTERNATIVE FILTER ON HIGH AND L...
EFFECT OF SILICON - RUBBER (SR) SHEETS AS AN ALTERNATIVE FILTER ON HIGH AND L...EFFECT OF SILICON - RUBBER (SR) SHEETS AS AN ALTERNATIVE FILTER ON HIGH AND L...
EFFECT OF SILICON - RUBBER (SR) SHEETS AS AN ALTERNATIVE FILTER ON HIGH AND L...AM Publications
 
UTILIZATION OF IMMUNIZATION SERVICES AMONG CHILDREN UNDER FIVE YEARS OF AGE I...
UTILIZATION OF IMMUNIZATION SERVICES AMONG CHILDREN UNDER FIVE YEARS OF AGE I...UTILIZATION OF IMMUNIZATION SERVICES AMONG CHILDREN UNDER FIVE YEARS OF AGE I...
UTILIZATION OF IMMUNIZATION SERVICES AMONG CHILDREN UNDER FIVE YEARS OF AGE I...AM Publications
 
REPRESENTATION OF THE BLOCK DATA ENCRYPTION ALGORITHM IN AN ANALYTICAL FORM F...
REPRESENTATION OF THE BLOCK DATA ENCRYPTION ALGORITHM IN AN ANALYTICAL FORM F...REPRESENTATION OF THE BLOCK DATA ENCRYPTION ALGORITHM IN AN ANALYTICAL FORM F...
REPRESENTATION OF THE BLOCK DATA ENCRYPTION ALGORITHM IN AN ANALYTICAL FORM F...AM Publications
 
OPTICAL CHARACTER RECOGNITION USING RBFNN
OPTICAL CHARACTER RECOGNITION USING RBFNNOPTICAL CHARACTER RECOGNITION USING RBFNN
OPTICAL CHARACTER RECOGNITION USING RBFNNAM Publications
 
DETECTION OF MOVING OBJECT
DETECTION OF MOVING OBJECTDETECTION OF MOVING OBJECT
DETECTION OF MOVING OBJECTAM Publications
 
SIMULATION OF ATMOSPHERIC POLLUTANTS DISPERSION IN AN URBAN ENVIRONMENT
SIMULATION OF ATMOSPHERIC POLLUTANTS DISPERSION IN AN URBAN ENVIRONMENTSIMULATION OF ATMOSPHERIC POLLUTANTS DISPERSION IN AN URBAN ENVIRONMENT
SIMULATION OF ATMOSPHERIC POLLUTANTS DISPERSION IN AN URBAN ENVIRONMENTAM Publications
 
PREPARATION AND EVALUATION OF WOOL KERATIN BASED CHITOSAN NANOFIBERS FOR AIR ...
PREPARATION AND EVALUATION OF WOOL KERATIN BASED CHITOSAN NANOFIBERS FOR AIR ...PREPARATION AND EVALUATION OF WOOL KERATIN BASED CHITOSAN NANOFIBERS FOR AIR ...
PREPARATION AND EVALUATION OF WOOL KERATIN BASED CHITOSAN NANOFIBERS FOR AIR ...AM Publications
 
ANALYSIS ON LOAD BALANCING ALGORITHMS IMPLEMENTATION ON CLOUD COMPUTING ENVIR...
ANALYSIS ON LOAD BALANCING ALGORITHMS IMPLEMENTATION ON CLOUD COMPUTING ENVIR...ANALYSIS ON LOAD BALANCING ALGORITHMS IMPLEMENTATION ON CLOUD COMPUTING ENVIR...
ANALYSIS ON LOAD BALANCING ALGORITHMS IMPLEMENTATION ON CLOUD COMPUTING ENVIR...AM Publications
 
DATA MINING WITH CLUSTERING ON BIG DATA FOR SHOPPING MALL’S DATASET
DATA MINING WITH CLUSTERING ON BIG DATA FOR SHOPPING MALL’S DATASETDATA MINING WITH CLUSTERING ON BIG DATA FOR SHOPPING MALL’S DATASET
DATA MINING WITH CLUSTERING ON BIG DATA FOR SHOPPING MALL’S DATASETAM Publications
 

More from AM Publications (20)

DEVELOPMENT OF TODDLER FAMILY CADRE TRAINING BASED ON ANDROID APPLICATIONS IN...
DEVELOPMENT OF TODDLER FAMILY CADRE TRAINING BASED ON ANDROID APPLICATIONS IN...DEVELOPMENT OF TODDLER FAMILY CADRE TRAINING BASED ON ANDROID APPLICATIONS IN...
DEVELOPMENT OF TODDLER FAMILY CADRE TRAINING BASED ON ANDROID APPLICATIONS IN...
 
TESTING OF COMPOSITE ON DROP-WEIGHT IMPACT TESTING AND DAMAGE IDENTIFICATION ...
TESTING OF COMPOSITE ON DROP-WEIGHT IMPACT TESTING AND DAMAGE IDENTIFICATION ...TESTING OF COMPOSITE ON DROP-WEIGHT IMPACT TESTING AND DAMAGE IDENTIFICATION ...
TESTING OF COMPOSITE ON DROP-WEIGHT IMPACT TESTING AND DAMAGE IDENTIFICATION ...
 
THE USE OF FRACTAL GEOMETRY IN TILING MOTIF DESIGN
THE USE OF FRACTAL GEOMETRY IN TILING MOTIF DESIGNTHE USE OF FRACTAL GEOMETRY IN TILING MOTIF DESIGN
THE USE OF FRACTAL GEOMETRY IN TILING MOTIF DESIGN
 
TWO-DIMENSIONAL INVERSION FINITE ELEMENT MODELING OF MAGNETOTELLURIC DATA: CA...
TWO-DIMENSIONAL INVERSION FINITE ELEMENT MODELING OF MAGNETOTELLURIC DATA: CA...TWO-DIMENSIONAL INVERSION FINITE ELEMENT MODELING OF MAGNETOTELLURIC DATA: CA...
TWO-DIMENSIONAL INVERSION FINITE ELEMENT MODELING OF MAGNETOTELLURIC DATA: CA...
 
USING THE GENETIC ALGORITHM TO OPTIMIZE LASER WELDING PARAMETERS FOR MARTENSI...
USING THE GENETIC ALGORITHM TO OPTIMIZE LASER WELDING PARAMETERS FOR MARTENSI...USING THE GENETIC ALGORITHM TO OPTIMIZE LASER WELDING PARAMETERS FOR MARTENSI...
USING THE GENETIC ALGORITHM TO OPTIMIZE LASER WELDING PARAMETERS FOR MARTENSI...
 
ANALYSIS AND DESIGN E-MARKETPLACE FOR MICRO, SMALL AND MEDIUM ENTERPRISES
ANALYSIS AND DESIGN E-MARKETPLACE FOR MICRO, SMALL AND MEDIUM ENTERPRISESANALYSIS AND DESIGN E-MARKETPLACE FOR MICRO, SMALL AND MEDIUM ENTERPRISES
ANALYSIS AND DESIGN E-MARKETPLACE FOR MICRO, SMALL AND MEDIUM ENTERPRISES
 
REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS
REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS
REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS
 
EVALUATE THE STRAIN ENERGY ERROR FOR THE LASER WELD BY THE H-REFINEMENT OF TH...
EVALUATE THE STRAIN ENERGY ERROR FOR THE LASER WELD BY THE H-REFINEMENT OF TH...EVALUATE THE STRAIN ENERGY ERROR FOR THE LASER WELD BY THE H-REFINEMENT OF TH...
EVALUATE THE STRAIN ENERGY ERROR FOR THE LASER WELD BY THE H-REFINEMENT OF TH...
 
HMM APPLICATION IN ISOLATED WORD SPEECH RECOGNITION
HMM APPLICATION IN ISOLATED WORD SPEECH RECOGNITIONHMM APPLICATION IN ISOLATED WORD SPEECH RECOGNITION
HMM APPLICATION IN ISOLATED WORD SPEECH RECOGNITION
 
PEDESTRIAN DETECTION IN LOW RESOLUTION VIDEOS USING A MULTI-FRAME HOG-BASED D...
PEDESTRIAN DETECTION IN LOW RESOLUTION VIDEOS USING A MULTI-FRAME HOG-BASED D...PEDESTRIAN DETECTION IN LOW RESOLUTION VIDEOS USING A MULTI-FRAME HOG-BASED D...
PEDESTRIAN DETECTION IN LOW RESOLUTION VIDEOS USING A MULTI-FRAME HOG-BASED D...
 
INTELLIGENT BLIND STICK
INTELLIGENT BLIND STICKINTELLIGENT BLIND STICK
INTELLIGENT BLIND STICK
 
EFFECT OF SILICON - RUBBER (SR) SHEETS AS AN ALTERNATIVE FILTER ON HIGH AND L...
EFFECT OF SILICON - RUBBER (SR) SHEETS AS AN ALTERNATIVE FILTER ON HIGH AND L...EFFECT OF SILICON - RUBBER (SR) SHEETS AS AN ALTERNATIVE FILTER ON HIGH AND L...
EFFECT OF SILICON - RUBBER (SR) SHEETS AS AN ALTERNATIVE FILTER ON HIGH AND L...
 
UTILIZATION OF IMMUNIZATION SERVICES AMONG CHILDREN UNDER FIVE YEARS OF AGE I...
UTILIZATION OF IMMUNIZATION SERVICES AMONG CHILDREN UNDER FIVE YEARS OF AGE I...UTILIZATION OF IMMUNIZATION SERVICES AMONG CHILDREN UNDER FIVE YEARS OF AGE I...
UTILIZATION OF IMMUNIZATION SERVICES AMONG CHILDREN UNDER FIVE YEARS OF AGE I...
 
REPRESENTATION OF THE BLOCK DATA ENCRYPTION ALGORITHM IN AN ANALYTICAL FORM F...
REPRESENTATION OF THE BLOCK DATA ENCRYPTION ALGORITHM IN AN ANALYTICAL FORM F...REPRESENTATION OF THE BLOCK DATA ENCRYPTION ALGORITHM IN AN ANALYTICAL FORM F...
REPRESENTATION OF THE BLOCK DATA ENCRYPTION ALGORITHM IN AN ANALYTICAL FORM F...
 
OPTICAL CHARACTER RECOGNITION USING RBFNN
OPTICAL CHARACTER RECOGNITION USING RBFNNOPTICAL CHARACTER RECOGNITION USING RBFNN
OPTICAL CHARACTER RECOGNITION USING RBFNN
 
DETECTION OF MOVING OBJECT
DETECTION OF MOVING OBJECTDETECTION OF MOVING OBJECT
DETECTION OF MOVING OBJECT
 
SIMULATION OF ATMOSPHERIC POLLUTANTS DISPERSION IN AN URBAN ENVIRONMENT
SIMULATION OF ATMOSPHERIC POLLUTANTS DISPERSION IN AN URBAN ENVIRONMENTSIMULATION OF ATMOSPHERIC POLLUTANTS DISPERSION IN AN URBAN ENVIRONMENT
SIMULATION OF ATMOSPHERIC POLLUTANTS DISPERSION IN AN URBAN ENVIRONMENT
 
PREPARATION AND EVALUATION OF WOOL KERATIN BASED CHITOSAN NANOFIBERS FOR AIR ...
PREPARATION AND EVALUATION OF WOOL KERATIN BASED CHITOSAN NANOFIBERS FOR AIR ...PREPARATION AND EVALUATION OF WOOL KERATIN BASED CHITOSAN NANOFIBERS FOR AIR ...
PREPARATION AND EVALUATION OF WOOL KERATIN BASED CHITOSAN NANOFIBERS FOR AIR ...
 
ANALYSIS ON LOAD BALANCING ALGORITHMS IMPLEMENTATION ON CLOUD COMPUTING ENVIR...
ANALYSIS ON LOAD BALANCING ALGORITHMS IMPLEMENTATION ON CLOUD COMPUTING ENVIR...ANALYSIS ON LOAD BALANCING ALGORITHMS IMPLEMENTATION ON CLOUD COMPUTING ENVIR...
ANALYSIS ON LOAD BALANCING ALGORITHMS IMPLEMENTATION ON CLOUD COMPUTING ENVIR...
 
DATA MINING WITH CLUSTERING ON BIG DATA FOR SHOPPING MALL’S DATASET
DATA MINING WITH CLUSTERING ON BIG DATA FOR SHOPPING MALL’S DATASETDATA MINING WITH CLUSTERING ON BIG DATA FOR SHOPPING MALL’S DATASET
DATA MINING WITH CLUSTERING ON BIG DATA FOR SHOPPING MALL’S DATASET
 

Recently uploaded

Architect Hassan Khalil Portfolio for 2024
Architect Hassan Khalil Portfolio for 2024Architect Hassan Khalil Portfolio for 2024
Architect Hassan Khalil Portfolio for 2024hassan khalil
 
Application of Residue Theorem to evaluate real integrations.pptx
Application of Residue Theorem to evaluate real integrations.pptxApplication of Residue Theorem to evaluate real integrations.pptx
Application of Residue Theorem to evaluate real integrations.pptx959SahilShah
 
pipeline in computer architecture design
pipeline in computer architecture  designpipeline in computer architecture  design
pipeline in computer architecture designssuser87fa0c1
 
IVE Industry Focused Event - Defence Sector 2024
IVE Industry Focused Event - Defence Sector 2024IVE Industry Focused Event - Defence Sector 2024
IVE Industry Focused Event - Defence Sector 2024Mark Billinghurst
 
INFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETE
INFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETEINFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETE
INFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETEroselinkalist12
 
main PPT.pptx of girls hostel security using rfid
main PPT.pptx of girls hostel security using rfidmain PPT.pptx of girls hostel security using rfid
main PPT.pptx of girls hostel security using rfidNikhilNagaraju
 
Introduction-To-Agricultural-Surveillance-Rover.pptx
Introduction-To-Agricultural-Surveillance-Rover.pptxIntroduction-To-Agricultural-Surveillance-Rover.pptx
Introduction-To-Agricultural-Surveillance-Rover.pptxk795866
 
GDSC ASEB Gen AI study jams presentation
GDSC ASEB Gen AI study jams presentationGDSC ASEB Gen AI study jams presentation
GDSC ASEB Gen AI study jams presentationGDSCAESB
 
Effects of rheological properties on mixing
Effects of rheological properties on mixingEffects of rheological properties on mixing
Effects of rheological properties on mixingviprabot1
 
complete construction, environmental and economics information of biomass com...
complete construction, environmental and economics information of biomass com...complete construction, environmental and economics information of biomass com...
complete construction, environmental and economics information of biomass com...asadnawaz62
 
Software and Systems Engineering Standards: Verification and Validation of Sy...
Software and Systems Engineering Standards: Verification and Validation of Sy...Software and Systems Engineering Standards: Verification and Validation of Sy...
Software and Systems Engineering Standards: Verification and Validation of Sy...VICTOR MAESTRE RAMIREZ
 
What are the advantages and disadvantages of membrane structures.pptx
What are the advantages and disadvantages of membrane structures.pptxWhat are the advantages and disadvantages of membrane structures.pptx
What are the advantages and disadvantages of membrane structures.pptxwendy cai
 
Heart Disease Prediction using machine learning.pptx
Heart Disease Prediction using machine learning.pptxHeart Disease Prediction using machine learning.pptx
Heart Disease Prediction using machine learning.pptxPoojaBan
 
Gurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort service
Gurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort serviceGurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort service
Gurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort servicejennyeacort
 
CCS355 Neural Network & Deep Learning Unit II Notes with Question bank .pdf
CCS355 Neural Network & Deep Learning Unit II Notes with Question bank .pdfCCS355 Neural Network & Deep Learning Unit II Notes with Question bank .pdf
CCS355 Neural Network & Deep Learning Unit II Notes with Question bank .pdfAsst.prof M.Gokilavani
 
Internship report on mechanical engineering
Internship report on mechanical engineeringInternship report on mechanical engineering
Internship report on mechanical engineeringmalavadedarshan25
 
Call Girls Narol 7397865700 Independent Call Girls
Call Girls Narol 7397865700 Independent Call GirlsCall Girls Narol 7397865700 Independent Call Girls
Call Girls Narol 7397865700 Independent Call Girlsssuser7cb4ff
 
HARMONY IN THE NATURE AND EXISTENCE - Unit-IV
HARMONY IN THE NATURE AND EXISTENCE - Unit-IVHARMONY IN THE NATURE AND EXISTENCE - Unit-IV
HARMONY IN THE NATURE AND EXISTENCE - Unit-IVRajaP95
 

Recently uploaded (20)

Architect Hassan Khalil Portfolio for 2024
Architect Hassan Khalil Portfolio for 2024Architect Hassan Khalil Portfolio for 2024
Architect Hassan Khalil Portfolio for 2024
 
Application of Residue Theorem to evaluate real integrations.pptx
Application of Residue Theorem to evaluate real integrations.pptxApplication of Residue Theorem to evaluate real integrations.pptx
Application of Residue Theorem to evaluate real integrations.pptx
 
pipeline in computer architecture design
pipeline in computer architecture  designpipeline in computer architecture  design
pipeline in computer architecture design
 
Exploring_Network_Security_with_JA3_by_Rakesh Seal.pptx
Exploring_Network_Security_with_JA3_by_Rakesh Seal.pptxExploring_Network_Security_with_JA3_by_Rakesh Seal.pptx
Exploring_Network_Security_with_JA3_by_Rakesh Seal.pptx
 
IVE Industry Focused Event - Defence Sector 2024
IVE Industry Focused Event - Defence Sector 2024IVE Industry Focused Event - Defence Sector 2024
IVE Industry Focused Event - Defence Sector 2024
 
INFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETE
INFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETEINFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETE
INFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETE
 
main PPT.pptx of girls hostel security using rfid
main PPT.pptx of girls hostel security using rfidmain PPT.pptx of girls hostel security using rfid
main PPT.pptx of girls hostel security using rfid
 
Introduction-To-Agricultural-Surveillance-Rover.pptx
Introduction-To-Agricultural-Surveillance-Rover.pptxIntroduction-To-Agricultural-Surveillance-Rover.pptx
Introduction-To-Agricultural-Surveillance-Rover.pptx
 
GDSC ASEB Gen AI study jams presentation
GDSC ASEB Gen AI study jams presentationGDSC ASEB Gen AI study jams presentation
GDSC ASEB Gen AI study jams presentation
 
Effects of rheological properties on mixing
Effects of rheological properties on mixingEffects of rheological properties on mixing
Effects of rheological properties on mixing
 
complete construction, environmental and economics information of biomass com...
complete construction, environmental and economics information of biomass com...complete construction, environmental and economics information of biomass com...
complete construction, environmental and economics information of biomass com...
 
Software and Systems Engineering Standards: Verification and Validation of Sy...
Software and Systems Engineering Standards: Verification and Validation of Sy...Software and Systems Engineering Standards: Verification and Validation of Sy...
Software and Systems Engineering Standards: Verification and Validation of Sy...
 
What are the advantages and disadvantages of membrane structures.pptx
What are the advantages and disadvantages of membrane structures.pptxWhat are the advantages and disadvantages of membrane structures.pptx
What are the advantages and disadvantages of membrane structures.pptx
 
Heart Disease Prediction using machine learning.pptx
Heart Disease Prediction using machine learning.pptxHeart Disease Prediction using machine learning.pptx
Heart Disease Prediction using machine learning.pptx
 
Gurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort service
Gurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort serviceGurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort service
Gurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort service
 
CCS355 Neural Network & Deep Learning Unit II Notes with Question bank .pdf
CCS355 Neural Network & Deep Learning Unit II Notes with Question bank .pdfCCS355 Neural Network & Deep Learning Unit II Notes with Question bank .pdf
CCS355 Neural Network & Deep Learning Unit II Notes with Question bank .pdf
 
Internship report on mechanical engineering
Internship report on mechanical engineeringInternship report on mechanical engineering
Internship report on mechanical engineering
 
Call Girls Narol 7397865700 Independent Call Girls
Call Girls Narol 7397865700 Independent Call GirlsCall Girls Narol 7397865700 Independent Call Girls
Call Girls Narol 7397865700 Independent Call Girls
 
Call Us -/9953056974- Call Girls In Vikaspuri-/- Delhi NCR
Call Us -/9953056974- Call Girls In Vikaspuri-/- Delhi NCRCall Us -/9953056974- Call Girls In Vikaspuri-/- Delhi NCR
Call Us -/9953056974- Call Girls In Vikaspuri-/- Delhi NCR
 
HARMONY IN THE NATURE AND EXISTENCE - Unit-IV
HARMONY IN THE NATURE AND EXISTENCE - Unit-IVHARMONY IN THE NATURE AND EXISTENCE - Unit-IV
HARMONY IN THE NATURE AND EXISTENCE - Unit-IV
 

A MODEL BASED APPROACH FOR IMPLEMENTING WLAN SECURITY

  • 1. International Journal of Innovative Research in Information Security (IJIRIS) ISSN: 2349-7017 Issue 01, Volume 6 (January 2019) www.ijiris.com IJIRIS: Mendeley (Elsevier Indexed) CiteFactor Journal Citations Impact Factor 1.23 Impact Factor Value – SJIF: Innospace, Morocco (2016): 4.651| Indexcopernicus: (ICV 2016): 88.20 © 2014- 19, IJIRIS- All Rights Reserved Page -20 A MODEL BASED APPROACH FOR IMPLEMENTING WLAN SECURITY David Gitonga Mwathi Department of Computer Science, Chuka University, Kenya. dgmwathi@chuka.ac.ke Manuscript History Number: IJIRIS/RS/Vol.06/Issue01/JAIS10082 DOI: 10.26562/IJIRAE.2019.JAIS10082 Received: 02, January 2019 Final Correction: 12, January 2019 Final Accepted: 21 January 2019 Published: January 2019 Citation: Mwathi (2019). A MODEL BASED APPROACH FOR IMPLEMENTING WLAN SECURITY . IJIRIS:: International Journal of Innovative Research in Information Security, Volume VI, 20-28. doi://10.26562/IJIRIS.2019.JAIS10082 Editor: Dr.A.Arul L.S, Chief Editor, IJIRIS, AM Publications, India Copyright: ©2019 This is an open access article distributed under the terms of the Creative Commons Attribution License, Which Permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Abstract- This paper presents various security features and configurations commonly implemented in WLANs and their aggregated security levels and then proposes a model that enables implementation and evaluation of WLAN security. Keywords- WLAN Security model; WLAN Security; WLAN attacks & Vulnerabilities; WLAN Security Features Selection; WLAN Security Measurement; I. INTRODUCTION In recent years, the proliferation of wireless local area network[WLAN] enabled laptop computers, smart phones and tablets has caused an increase in the range of places people perform computing. As a result, WLANs of various kinds have gained a lot of popularity. But with the added convenience of wireless access come new problems: Security. When transmissions are broadcast over radio waves, interception and masquerading becomes trivial to anyone with WLAN enabled device in the range. WLANs implement IEEE 802.11i and IEEE 802.11w standards whose focus is provision of appropriate integrity and confidentiality levels. These standards provide a variety of options for various security features.This flexible nature of the provisions of these standards and supporting technologies create potential for selection of vulnerable cipher suite, authentication & access control, end-user and server system security features. This makes selection and configuration of the appropriate security features a challenge to many WLAN security implementers [1]. This issue is a major concern because several software attack tools targeting vulnerabilities in authentication methods, cipher suites and supporting technologies on client devices and server implementations continue to proliferate, effectively empowering attackers. This research aims to make a contribution by addressing the issue of poor implementation of WLAN security through development of a model that enables design or selection of security features and their configuration for WLAN security.
  • 2. International Journal of Innovative Research in Information Security (IJIRIS) ISSN: 2349-7017 Issue 01, Volume 6 (January 2019) www.ijiris.com IJIRIS: Mendeley (Elsevier Indexed) CiteFactor Journal Citations Impact Factor 1.23 Impact Factor Value – SJIF: Innospace, Morocco (2016): 4.651| Indexcopernicus: (ICV 2016): 88.20 © 2014- 19, IJIRIS- All Rights Reserved Page -21 II. RELATED WORK ON WLAN SECURITY IMPLEMENTATION APPROACHES Several works propose the following variables for selection of appropriate cipher suite to support WLAN authentication and access control: core cryptographic algorithm, key sizes, per packet key, integrity mechanism, header protection, replay detection, authentication supported and mode of key distribution [2]. Other works focus on selection of extensible authentication protocol [EAP] method for securing WLANs. Some of the variables proposed to influence an implementer into selecting an EAP authentication mechanism in a WLAN include: mutual authentication, identity privacy, dictionary attack resistance, replay attack resistance, derivation of strong session keys, tested implementation, delegation, and fast reconnect.[3] Mutual authentication requires that an EAP method should enable both client device and authentication server to authenticate each other. It has been shown that the absence of mutual authentication in WEP based authentication was responsible of many of its weaknesses [4]. Many network security administrators use their experience of past solutions and documentation to configure security of network equipment [5]. In this case, selection and configuration of security features is mainly guided by following a documented procedure or a wizard for installation which enables network administrators bypass the “hardness” of dependency analysis. For example, suppose a network administrator needs to configure a new access point’s security settings. The installation guide may instruct one to select the cipher suite first, then authentication method. The network administrator may follow these instructions without analyzing the cumulative effect of the combination of cipher suite and authentication method selected on overall security [5]. Use of documentation and experience approaches alone is not sufficient because documentation is not always accurate due to errors in the software, human error, time and cost of developing detailed installation/configuration wizards by equipment manufacturers [5].Additionally, documentation does not address all possible platforms because WLANs may comprise equipment/components from various developers and vendors. Even where the equipment have been tested and verified fully by their developers for any platform, it is possible that they can fail to function as specified in a particular platform.Experience on the other hand may also not be always accurate and in a very dynamic environment, it is possible to misconfigure the system by simply relying on experience that may not apply to the system’s current state. III. METHODOLOGY Descriptive survey of 31 WLAN networks of public and private Universities in Kenya was made. Questionnaires were sent to network administrators of these wireless networks to collect hard facts related to their network. Observation of the configuration information on sampled networks was also made on the user devices and access point using passive (nonintrusive) WLAN network search tools. This information was used to verify the questionnaire responses. The data collected led to the discovery of security features and configuration options which were aggregated as: cipher suite, authentication credentials, client driver, client utility, accespoint firmware, authentication and access control mechanism, user database and authentication server. The researcher evaluated the security levels of these security features/configuration options and used the results of evaluation to develop a model that maps these security features/configuration options to security strengths as well as an algorithm that combines and propagates the security strengths of the security features and configurations selected to generate the overall security strength. IV.RESULTS This section presents the security features and configurations options available to the security implementer and then proposes a model that maps these security features to security strengths as well as an algorithm that combines and propagates the security strengths of the security features and configurations selected to generate the overall security strength. A. Model Operation Overview The model has three steps that define its operation: (i) Identification of security features and configuration options (ii) Mapping security features/configurations options to security strengths. (iii) Combining and propagating the security strengths of the security features and configurations selected to generate the overall security strength Subsequent sub-sections detail the activities of each step.
  • 3. International Journal of Innovative Research in Information Security (IJIRIS) ISSN: 2349-7017 Issue 01, Volume 6 (January 2019) www.ijiris.com IJIRIS: Mendeley (Elsevier Indexed) CiteFactor Journal Citations Impact Factor 1.23 Impact Factor Value – SJIF: Innospace, Morocco (2016): 4.651| Indexcopernicus: (ICV 2016): 88.20 © 2014- 19, IJIRIS- All Rights Reserved Page -22 B. Identification of Security Features and Configuration Options This is the set of security features/configurations available to the security implementer for each of the eight key components namely: cipher suite, authentication credentials. Client utility, client driver, access point utility, authentication & access control mechanism, user database and authentication server. All these eight components have been considered equivalent in relative importance in relation to their influence on WLAN security. This means none of them can be considered superior to the other. However, their actual influence values/strength will be determined by the security features selected or configurations on each of the components. C. Mapping Security Features/Configuration options to Security Strengths The model maps the security features/configuration option selected to “Very Low”, “Low”, “Moderate” or “High” security strength based on already predetermined values. Each security feature/configuration is associated with certain characteristics which determine its security strength. The decision on which strength a security feature/configuration is mapped to is based on a value function as indicated in function table 1 to table 8.Looking at each function table, whenever security strength of a security feature/configuration is mapped to level low, moderate or high, it is denoted 1, 2 and 3 respectively. Whenever security strength of a security feature/configuration is mapped to level very low, it is denoted as 0 which means that if this security feature/configuration is selected and implemented, the WLAN implementation is highly susceptible to attacks. This means that such security feature/configuration should not be implemented in a WLAN. TABLE 1: VALUE FUNCTION TABLE FOR AUTHENTICATION CREDENTIALS TABLE 2: VALUE FUNCTION TABLE FOR CIPHER SUITE Security Strength [Weight of influence] Description of the Security feature/Configuration High [3] CCMP (WPA2 +AES) Moderate [2] TKIP(WPA +AES) Low [1] TKIP(WPA +RC4) Low [1] TKIP(WPA2 +RC4) Very Low [0] WEP TABLE 3: VALUE FUNCTION TABLE FOR WLAN CLIENT DRIVER Security Strength Weight of influence Description of Security Feature/ Configuration High [3]  Supports management frame protection (MFP/IEEE 802.11w) and validation.  Supports configurable active scanning approach. Moderate [2]  Supports management frame protection(MFP/IEEE 802.11w) and validation  Lacks Support for Configurable active scanning approach Moderate [2]  Lacks support for management frame protection (IEEE 802.11w) and validation  Supports IEEE 802.11i.  Supports configurable active scanning approach. Low [1]  Lacks support for management frame protection (MFP/IEEE 802.11w) and validation  Lacks support for Configurable active scanning approach.  Supports IEEE 802.11i. Very Low [0] Lacks support for IEEE 802.11i. Security Strength [Weight of influence] Description of Security Feature/ Configuration High [3] Both Client and Server Certificates Moderate [2] PAC, One time password OR Server Side certificate only(Tunneled) Low [1] Secret Key/password(Mutual or Unilateral) Very Low [0] SSID Very Low [0] MAC address Very Low [0] PIN
  • 4. International Journal of Innovative Research in Information Security (IJIRIS) ISSN: 2349-7017 Issue 01, Volume 6 (January 2019) www.ijiris.com IJIRIS: Mendeley (Elsevier Indexed) CiteFactor Journal Citations Impact Factor 1.23 Impact Factor Value – SJIF: Innospace, Morocco (2016): 4.651| Indexcopernicus: (ICV 2016): 88.20 © 2014- 19, IJIRIS- All Rights Reserved Page -23 TABLE 4: VALUE FUNCTION TABLE FOR WLAN CLIENT UTILITY Security Strength Weight of influence Description of Security Feature/ Configuration High [3]  Configured to support both client and server side Certificate based mutual Authentication.  Supports Management frame protection.  Configured to enforce validation of server certificates and server name.  Configured not to allow Self signed certificates. Moderate [2]  Configured to support server side only Certificate based mutual Authentication.  Supports Management frame protection (IEEE 802.11w).  Configured to enforce validation of server certificates and server name.  Configured not to allow Self signed certificates. Low [1]  Configured to support Password, pre-shared key or MAC address based mutual Authentication mechanism.  Supports Management frame protection (IEEE 802.11w) Low [1]  Configured to support server side only or both client and server side Certificate based mutual Authentication  Lacks Support for Management frame protection (IEEE 802.11w) and validation.  Supports IEEE 802.11i. Low [1]  Configured to support Password, pre-shared key or MAC address based mutual Authentication mechanism.  Lacks Support for Management frame protection (IEEE 802.11w) and validation.  Supports IEEE 802.11i. Very Low [0] Lacks support for IEEE 802.11i. Very Low [0] Configured to support server side only or both client and server side certificate but Validation of server certificates and/or server name not enforced. Very Low [0] Configured to support server side only or both client and server side certificate but allows Self signed certificates. Very Low [0] Mutual authentication not supported. TABLE 5: VALUE FUNCTION TABLE FOR ACCESS POINT UTILITY Security Strength Weight of influence Description of Security Feature/ Configuration High [3]  Firmware configured to support management frame protection (MFP/IEEE 802.11w) and validation and is set to required.  Firmware configured to Support only RSNA connections(RSNA enabled) Moderate [2]  Firmware configured to support optional management frame protection (MFP/IEEE 802.11w) and validation.  Firmware configured to Support only RSNA connections(RSNA enabled) Low [1]  Firmware does not support MFP/IEEE 802.11w and validation  Firmware configured to Support only RSNA connections(RSNA enabled) Very Low [0] Firmware not configured to Support only RSNA connections(Pre-RSNA enabled) TABLE 6: VALUE FUNCTION TABLE FOR AUTHENTICATION AND ACCESS CONTROL MECHANISM Security Strength Weight of influence Description of Security Feature/ Configuration High [3] IEEE 802.1x With EAP method High [3] Captive portal and IEEE 802.1x With EAP Method Moderate [2] Captive Portal and Pre-shared Key Low [1] Captive Portal Only Low [1] Pre-shared Key Only Very Low [0] MAC address filtering
  • 5. International Journal of Innovative Research in Information Security (IJIRIS) ISSN: 2349-7017 Issue 01, Volume 6 (January 2019) www.ijiris.com IJIRIS: Mendeley (Elsevier Indexed) CiteFactor Journal Citations Impact Factor 1.23 Impact Factor Value – SJIF: Innospace, Morocco (2016): 4.651| Indexcopernicus: (ICV 2016): 88.20 © 2014- 19, IJIRIS- All Rights Reserved Page -24 Very Low [0] Open SSID Very Low [0] PIN based authentication(WPS) Very Low [0] Button press based authentication(WPS) D. Combining and Propagating the Security levels of the Security Features/Configurations in the Model The model determines the overall security level of an implementation by aggregating security levels of individual components based on security features and configurations set in them. The components whose security levels are aggregated are: client utility, client driver, access point utility, authentication server, authentication & access control mechanism, user database, cipher suite and authentication credentials. Fig. 1 shows the structure of hierarchy and direction of propagation of security levels. The aggregation of security levels is hierarchical (parent- child structure) and is done bottom up. . Fig 1: Structure of Hierarchy and Direction of Propagation of Security levels TABLE 7: VALUE FUNCTION TABLE FOR AUTHENTICATION SERVER Security Strength Weight of influence Description of Security Feature/ Configuration High [3] DIAMETER. Configured to Support mutual authentication Moderate [2] RADIUS. Configured to Support mutual authentication Low [1] DIAMETER. Not Configured to Support mutual authentication Low [1] RADIUS. Not Configured to Support mutual authentication Low [1] KERBEROS Very Low [0] None/Independent on each Access point Client Utility Authentication Credentials Overall WLAN Security User Database System Authentication server Authentication and access control Client Driver Trusted Computing Base (TCB) Wireless Trusted Path (WTP) Back-end authentication Systems Front-end System software Access point Utility Cipher Suite
  • 6. International Journal of Innovative Research in Information Security (IJIRIS) ISSN: 2349-7017 Issue 01, Volume 6 (January 2019) www.ijiris.com IJIRIS: Mendeley (Elsevier Indexed) CiteFactor Journal Citations Impact Factor 1.23 Impact Factor Value – SJIF: Innospace, Morocco (2016): 4.651| Indexcopernicus: (ICV 2016): 88.20 © 2014- 19, IJIRIS- All Rights Reserved Page -25 TABLE 8: VALUE FUNCTION TABLE FOR USER DATABASE SYSTEM The combination and propagation algorithm used to aggregate security levels is illustrated below: (a) Starting with terminal/leaf nodes, every subtree has a parent node R and a set of child nodes C. The child nodes may have a negative or positive type of influence on R. A positive influence of child Ci on R means that when security level of Ci is high, that of R is influenced to move upwards too. On the other hand, a negative influence of child Ci on R means that when security level of Ci is high, that of R is influenced to move downwards. (b) If a parent node R has at least one child with very low security level, the model gives a notification that the security feature or configuration is not recommended. This is because this feature renders the security of the entire WLAN very weak. (c) If a parent node R has k child nodes with combination of positive and negative influences and of strength Si (High, Moderate, and Low) and values of security level for all child nodes are known, the value of the parent node, VR is computed based on the following weighted average. ∑k (Si * Vi) VR = i=1 ∑k (Si) i=1 Where: Si refers to the strength of the security influence of a child C i on parent R which is equal to 1, 2, or 3 if the influence of the child is low, moderate, and high respectively. Vi refers to the value of child Ci and is dependent on Si and type of influence of child Ci on parent node R. If the child node Ci has a positive influence on Parent node R and the strength of influence (Si) of node Ci is low, moderate, or high then Vi is equal to 1, 2, and 3 respectively. On the other hand, if the child node Ci has a negative influence on parent node R and strength of influence (Si) of node Ci is low, moderate or high then Vi is equal to 3, 2 or 1 respectively (d) Once a value VR is determined, thresholds have been set to decide the values of VR as follows: (i) If 1<=VR <1.5, then the value of R is low (ii) If 1.5 <= VR <= 2.5, then the value of R is moderate (iii) If 2.5 < VR<=3, then the value of R is High. (e)The process is repeated recursively up the hierarchy until a value for the root node is established. Based on the algorithm described above the security levels of the eight model components are propagated as follows: (i) Security level of client utility, client driver and access point utility are aggregated to derive a composite security levels for front-end system software subtree. (ii) Security levels of authentication server, authentication & access control mechanism and user database are aggregated to derive a composite security levels for back-end authentication systems subtree. (iii) The derived security levels for front-end system software is aggregated with that of back-end authentication systems to derive a composite security levels for trusted computing base (TCB). (iv) Security levels of cipher suite and authentication credentials are aggregated to derive a composite security levels for wireless path subtree. (v) Finally, the security levels of trusted computing base (TCB) and that of wireless path are aggregated to form an overall security levels of the implementation. The model therefore provides a what-if simulation of the security level expected from a combination of the influences of the selected security features and/or configurations. Security Strength Weight of influence Description of Security Feature/Configuration High [3] Distributed Database Servers with an Intrusion Detection System(IDS) Moderate [2] Distributed Database Servers without an Intrusion Detection System(IDS) Moderate [2] Centralized Database Server with an Intrusion Detection System(IDS) Low [1] Centralized Database Server without an Intrusion Detection System(IDS) Very Low [0] None/Independent on each Access point
  • 7. International Journal of Innovative Research in Information Security (IJIRIS) ISSN: 2349-7017 Issue 01, Volume 6 (January 2019) www.ijiris.com IJIRIS: Mendeley (Elsevier Indexed) CiteFactor Journal Citations Impact Factor 1.23 Impact Factor Value – SJIF: Innospace, Morocco (2016): 4.651| Indexcopernicus: (ICV 2016): 88.20 © 2014- 19, IJIRIS- All Rights Reserved Page -26 E. Theoretical Analysis of the Model Algorithm Using Degenerate and Trace Tests Degenerate and traces validation was used to perform theoretical validation of the model. Degenerate validation involved analysis of input values to test the corresponding changes in the internal components e.g. does the security level/strength go up when more secure configurations and security features are selected and vice versa? Does the security level/strength go down when highly vulnerable configurations and security features are selected and vice versa? Validation using traces on the other hand was done to determine whether the mathematical logic of the technique for propagation of values in the model maintains necessary accuracy and consistency. To achieve this, the researcher tracked entities’ strength and type of security influence through each sub-model and the overall model and analyzed the results. Table 9 shows results for a one component sub-model when type of influence is positive and a similar scenario when type of influence is negative. The following can be deduced from the table. i. When the security strength of the child component is low, moderate or high, the security strength of the root/parent component is low, medium and high respectively when the type of influence is positive (+ve). ii. When the security strength of the child component is low, medium or high, the security strength of the root/parent component is high, medium and low respectively when the type of influence is negative (-ve). TABLE 9: ONE COMPONENT SUB-MODEL FOR BOTH POSITIVE AND NEGATIVE INFLUENCE Strength of component Strength of sub-model Root(P) When Relationship is +ve Strength of sub-model Root(P) When Relationship is -ve Low [1] Low [1] High [3] Moderate [2] Moderate [2] Moderate [2] High [3] High [3] Low [1] Table 10 shows a summary of results for a two component sub-model when type of influence is positive and a similar scenario when type of influence is negative. The following can be deduced from the table. i. When all the two child components have low, moderate or high security strength, the root/overall security strength is also low, moderate and high respectively. ii. Where one of the components has high security strength, and the other low security strength, the root/overall security strength is moderate. iii. When the relationship changes to type negative, the results are inverted. TABLE 10: TWO COMPONENT SUB-MODEL FOR BOTH POSITIVE AND NEGATIVE INFLUENCE. Strength of component 1(S1) Strength of component 2(S2) Strength of sub-model Root(P) when Relationship type +ve Strength of Sub-model Root(P) when Relationship type -ve Low [1] Low [1] Low [1.00] High [3.00] Low [1] Moderate[2] Moderate [1.67] Moderate [2.33] Low [1] High [3] Moderate [2.50] Moderate [1.50] Moderate[2] Low [1] Moderate [1.67] Moderate [2.33] Moderate[2] Moderate[2] Moderate [2.00] Moderate [2.00] Moderate[2] High [3] High [2.60] Low [1.40] High [3] Low [1] Moderate [2.50] Moderate [1.50] High [3] Moderate[2] High [2.60] Low [1.40] High [3] High [3] High [3.00] Low [1.00] Table11 shows a summary of results for a three component sub-model when type of influence is positive and a similar scenario when type of influence is negative. The following can be deduced from the table.
  • 8. International Journal of Innovative Research in Information Security (IJIRIS) ISSN: 2349-7017 Issue 01, Volume 6 (January 2019) www.ijiris.com IJIRIS: Mendeley (Elsevier Indexed) CiteFactor Journal Citations Impact Factor 1.23 Impact Factor Value – SJIF: Innospace, Morocco (2016): 4.651| Indexcopernicus: (ICV 2016): 88.20 © 2014- 19, IJIRIS- All Rights Reserved Page -27 i. When all the three components have low, moderate or high security strength, the root/overall security strength is low, moderate and high respectively. ii. For any given set of component input values, the results of situations where the relationship is of type positive(+ve) are the inversion of the results under the same component values if relationship is of type negative(-ve) as expected. TABLE 11: THREE COMPONENT SUB-MODEL FOR BOTH POSITIVE AND NEGATIVE INFLUENCE It was also observed that when all the eight components have low, moderate or high security strengths, the overall security strength is low, moderate and high respectively. These observations indicate that the logic of the algorithm for propagation of component values maintains accuracy and consistency as required and that the model results are consistent with the design principles. V. CONCLUSION This paper has brought into perspective various security features and configurations commonly implemented in WLANs and their aggregated security levels. It has also proposed a model that enables an implementer to establish the level of security provided by the security features and configurations implemented in a WLAN. The results from the theoretical analysis of the model indicate that the logic of the algorithm for propagation of component values maintains accuracy and consistency as required and that the model results are consistent with the design principles. This model can be used for WLAN security design, implementation and analysis. Strength of Component 1 (S1) Strength of Component 2(S2) Strength of Component 3(S3) Strength of sub model Root (P) when Relationship type +ve Strength of sub model Root (P) when Relationship type –ve Low [1] Low [1] Low [1] Low [1.00] High [3.00] Low [1] Low [1] Moderate[2] Moderate [1.50] Moderate [2.50] Low [1] Low [1] High [3] Moderate [2.20] Moderate [1.80] Low [1] Moderate[2] Low [1] Moderate [1.50] Moderate [2.50] Low [1] Moderate[2] Moderate[2] Moderate [1.80] Moderate [2.20] Low [1] Moderate[2] High [3] Moderate [2.33] Moderate [1.67] Low [1] High [3] Low [1] Moderate [2.20] Moderate [1.80] Low [1] High [3] Moderate[2] Moderate [2.33] Moderate [1.67] Low [1] High [3] High [3] High [2.71] Low [1.29] Moderate [2] Low [1] Low [1] Moderate [1.50] Moderate [2.50] Moderate [2] Low [1] Moderate[2] Moderate [1.80] Moderate [2.20] Moderate [2] Low [1] High [3] Moderate [2.33] Moderate [1.67] Moderate [2] Moderate [2] Low [1] Moderate [1.80] Moderate [2.20] Moderate [2] Moderate [2] Moderate[2] Moderate [2.00] Moderate [2.00] Moderate [2] Moderate [2] High [3] Moderate [2.43] Moderate [1.57] Moderate [2] High [3] Low [1] Moderate [2.33] Moderate [1.67] Moderate [2] High [3] Moderate[2] Moderate [2.43] Moderate [1.57] Moderate [2] High [3] High [3] High [2.75] Low [1.25] High [3] Low [1] Low [1] Moderate [2.20] Moderate [1.80] High [3] Low [1] Moderate [2] Moderate [2.33] Moderate [1.67] High [3] Low [1] High [3] High [2.71] Low [1.29] High [3] Moderate[2] Low [1] Moderate [2.33] Moderate [1.67] High [3] Moderate[2] Moderate [2] Moderate [2.43] Moderate [1.57] High [3] Moderate[2] High [3] High [2.75] Low [1.25] High [3] High [3] Low [1] High [2.71] Low [1.29] High [3] High [3] Moderate[2] High [2.75] Low [1.25] High [3] High [3] High [3] High [3.00] Low [1.00]
  • 9. International Journal of Innovative Research in Information Security (IJIRIS) ISSN: 2349-7017 Issue 01, Volume 6 (January 2019) www.ijiris.com IJIRIS: Mendeley (Elsevier Indexed) CiteFactor Journal Citations Impact Factor 1.23 Impact Factor Value – SJIF: Innospace, Morocco (2016): 4.651| Indexcopernicus: (ICV 2016): 88.20 © 2014- 19, IJIRIS- All Rights Reserved Page -28 Particularly, when implemented in form of a software tool that mines data directly from devices, network administrators can use it to establish highly vulnerable security features and configurations in an implementation, visualize the security implications of selecting certain security features and configurations. Network administrators can also use it as an audit tool for WLAN security and then recommend an appropriate security for an organization’s security. Researchers in the area of WLAN security can also use it as a data collection tool. REFERENCES 1. Khidir, M. and Ali, A. (2011).A Comparative Study of Authentication Methods for Wi-Fi Networks. In: Proceedings of International Conference on Computational Intelligence, Communication Systems and Networks [Online], pp. 190-194. 2. Sheila, F., Bernard, E., Les, O., Karen, S.(2007). Establishing Wireless Robust security Networks: A Guide to IEEE 802.11i, NIST.US. 3. Kwang-Hyun, B., Sean, W. and David, K. (2004). A Survey of WPA and 802.11i RSN Authentication Protocols. Dartmouth College: Computer Science Technial Report TR2004-524. Available at: www.cs.dartmouth.edu/~dfk/papers/baek-survey-tr.pdf 4. Borisov,N. , Goldberg,I. and Wagner, D.(2001). Intercepting Mobile Communications: The Insecurity of 802.11. In: Proceedings of 7th Annual International Conference on Mobile Computing and Networking, Rome, Italy: ACM Press. 5. Yizhan, S. (2006). Complexity of System Configuration Management, PhD thesis, Tufts University.