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 targetjsolutions@gmail.com 
 (0)9611582234, (0)9945657526 
Targetj Solutions - 9611582234
Targetj Solutions - 9611582234
 Wireless spoofing attacks are easy to launch and can 
significantly impact the performance of networks. Although 
the identity of a node can be verified through cryptographic 
authentication, conventional security approaches are not 
always desirable because of their overhead requirements. In 
this paper, we propose to use spatial information, a physical 
property associated with each node, hard to falsify, and not 
reliant on cryptography, as the basis for 1) detecting spoofing 
attacks; 2) determining the number of attackers when multiple 
adversaries masquerading as the same node identity; and 3) 
localizing multiple adversaries. We propose to use the spatial 
correlation of received signal strength (RSS) inherited from 
wireless nodes to detect the spoofing attacks. We then 
formulate the problem of determining the number of attackers 
as a multiclass detection problem. 
Targetj Solutions - 9611582234
CONTINUE…. 
Cluster-based mechanisms are developed to determine the 
number of attackers. When the training data are available, we 
explore using the Support Vector Machines (SVM) method to 
further improve the accuracy of determining the number of 
attackers. In addition, we developed an integrated detection 
and localization system that can localize the positions of 
multiple attackers. We evaluated our techniques through two 
testbeds using both an 802.11 (WiFi) network and an 
802.15.4 (ZigBee) network in two real office buildings. Our 
experimental results show that our proposed methods can 
achieve over 90 percent Hit Rate and Precision when 
determining the number of attackers. Our localization results 
using a representative set of algorithms provide strong 
evidence of high accuracy of localizing multiple adversaries. 
Targetj Solutions - 9611582234
 In the EXISTING SYSTEM, due to the open 
medium in Wireless Sensor Networks, spoofing attacks 
are easy to launch and can significantly impact the 
performance of networks. So that the nodes can be easily 
compromised and perform malicious activities. Although 
the identity of a node can be verified through 
cryptographic authentication, conventional security 
approaches are not always desirable because of their 
overhead requirements. 
DISADVANTGES: 
 Spoofing Attacks can be easily launched. 
 Nodes can be easily compromised and perform malicious 
activities. 
Targetj Solutions - 9611582234
 In the PROPOSED SYSTEM, we are 
implementing three steps 1. Detection of Spoofing 
attacks based on Received Signal Strength. 2. 
Determining the number of attackers when multiple 
adversaries masquerading the same node identity. So 
that we can identify the attackers who are all 
performing the spoofing attacks. 3. Localizing the 
multiple adversaries and eliminate them from the 
network if necessary. So that the other nodes may 
know about the attacker nodes in the Wireless Sensor 
Networks. 
Targetj Solutions - 9611582234
ADVANTAGES: 
 Easy to detect the spoofing attacks. 
 Eliminating the attacker node will provide more 
security to the network. 
 Encrypting the data packets restrict the intermediate 
nodes from viewing the original data. 
Targetj Solutions - 9611582234
SOFTWARE REQUIREMENTS: 
 Platform : Windows XP 
 Front End : Java JDK1.5. 
 Back End : MYSQL 
HARDWARE REQUIREMENTS: 
 Processor : Pentium IV 
 RAM : 512 MB 
 HDD : 80 GB 
Targetj Solutions - 9611582234
 In this work, we proposed to use received signal 
strength based spatial correlation, a physical property 
associated with each wireless device that is hard to falsify 
and not reliant on cryptography as the basis for detecting 
spoofing attacks in wireless networks. We provided 
theoretical analysis of using the spatial correlation of RSS 
inherited from wireless nodes for attack detection. We 
derived the test statistic based on the cluster analysis of 
RSS readings. Our approach can detect the presence of 
attacks as well as determine the number of adversaries, 
spoofing the same node identity, so that we can localize 
any number of attackers and eliminate them. Determining 
the number of adversaries is a particularly challenging 
problem. 
Targetj Solutions - 9611582234
 We developed SILENCE, a mechanism that employs 
the minimum distance testing in addition to cluster 
analysis to achieve better accuracy of determining the 
number of attackers than other methods under study, 
such as Silhouette Plot and System Evolution that use 
cluster analysis alone. Additionally, when the training 
data are available, we explored using Support Vector 
Machines-based mechanism to further improve the 
accuracy of determining the number of attackers 
present in the system. 
Targetj Solutions - 9611582234
 [1] J. Bellardo and S. Savage, “802.11 Denial-of-Service 
Attacks: Real Vulnerabilities and Practical Solutions,” 
Proc. USENIX Security Symp., pp. 15-28, 2003. 
 [2] F. Ferreri, M. Bernaschi, and L. Valcamonici, “Access 
Points Vulnerabilities to Dos Attacks in 802.11 Networks,” 
Proc. IEEE Wireless Comm. and Networking Conf., 2004. 
 [3] D. Faria and D. Cheriton, “Detecting Identity-Based 
Attacks in Wireless Networks Using Signalprints,” Proc. 
ACM Workshop Wireless Security (WiSe), Sept. 2006. 
 [4] Q. Li and W. Trappe, “Relationship-Based Detection of 
Spoofing- Related Anomalous Traffic in Ad Hoc 
Networks,” Proc. Ann. IEEE Comm. Soc. on IEEE and 
Sensor and Ad Hoc Comm. and Networks (SECON), 2006. 
Targetj Solutions - 9611582234
 [5] B. Wu, J. Wu, E. Fernandez, and S. Magliveras, 
“Secure and Efficient Key Management in Mobile Ad 
Hoc Networks,” Proc. IEEE Int’l Parallel and 
Distributed Processing Symp. (IPDPS), 2005. 
 [6] A. Wool, “Lightweight Key Management for 
IEEE 802.11 Wireless Lans With Key Refresh and 
Host Revocation,” ACM/Springer Wireless Networks, 
vol. 11, no. 6, pp. 677-686, 2005. 
 [7] Y. Sheng, K. Tan, G. Chen, D. Kotz, and A. 
Campbell, “Detecting 802.11 MAC Layer Spoofing 
Using Received Signal Strength,” Proc. IEEE 
INFOCOM, Apr. 2008. 
Targetj Solutions - 9611582234

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Detection and localization of multiple spoofing attacks in

  • 1.  REAL TIME PROJECTS  IEEE BASED PROJECTS  EMBEDDED SYSTEMS  PAPER PUBLICATIONS  MATLAB PROJECTS  targetjsolutions@gmail.com  (0)9611582234, (0)9945657526 Targetj Solutions - 9611582234
  • 2. Targetj Solutions - 9611582234
  • 3.  Wireless spoofing attacks are easy to launch and can significantly impact the performance of networks. Although the identity of a node can be verified through cryptographic authentication, conventional security approaches are not always desirable because of their overhead requirements. In this paper, we propose to use spatial information, a physical property associated with each node, hard to falsify, and not reliant on cryptography, as the basis for 1) detecting spoofing attacks; 2) determining the number of attackers when multiple adversaries masquerading as the same node identity; and 3) localizing multiple adversaries. We propose to use the spatial correlation of received signal strength (RSS) inherited from wireless nodes to detect the spoofing attacks. We then formulate the problem of determining the number of attackers as a multiclass detection problem. Targetj Solutions - 9611582234
  • 4. CONTINUE…. Cluster-based mechanisms are developed to determine the number of attackers. When the training data are available, we explore using the Support Vector Machines (SVM) method to further improve the accuracy of determining the number of attackers. In addition, we developed an integrated detection and localization system that can localize the positions of multiple attackers. We evaluated our techniques through two testbeds using both an 802.11 (WiFi) network and an 802.15.4 (ZigBee) network in two real office buildings. Our experimental results show that our proposed methods can achieve over 90 percent Hit Rate and Precision when determining the number of attackers. Our localization results using a representative set of algorithms provide strong evidence of high accuracy of localizing multiple adversaries. Targetj Solutions - 9611582234
  • 5.  In the EXISTING SYSTEM, due to the open medium in Wireless Sensor Networks, spoofing attacks are easy to launch and can significantly impact the performance of networks. So that the nodes can be easily compromised and perform malicious activities. Although the identity of a node can be verified through cryptographic authentication, conventional security approaches are not always desirable because of their overhead requirements. DISADVANTGES:  Spoofing Attacks can be easily launched.  Nodes can be easily compromised and perform malicious activities. Targetj Solutions - 9611582234
  • 6.  In the PROPOSED SYSTEM, we are implementing three steps 1. Detection of Spoofing attacks based on Received Signal Strength. 2. Determining the number of attackers when multiple adversaries masquerading the same node identity. So that we can identify the attackers who are all performing the spoofing attacks. 3. Localizing the multiple adversaries and eliminate them from the network if necessary. So that the other nodes may know about the attacker nodes in the Wireless Sensor Networks. Targetj Solutions - 9611582234
  • 7. ADVANTAGES:  Easy to detect the spoofing attacks.  Eliminating the attacker node will provide more security to the network.  Encrypting the data packets restrict the intermediate nodes from viewing the original data. Targetj Solutions - 9611582234
  • 8. SOFTWARE REQUIREMENTS:  Platform : Windows XP  Front End : Java JDK1.5.  Back End : MYSQL HARDWARE REQUIREMENTS:  Processor : Pentium IV  RAM : 512 MB  HDD : 80 GB Targetj Solutions - 9611582234
  • 9.  In this work, we proposed to use received signal strength based spatial correlation, a physical property associated with each wireless device that is hard to falsify and not reliant on cryptography as the basis for detecting spoofing attacks in wireless networks. We provided theoretical analysis of using the spatial correlation of RSS inherited from wireless nodes for attack detection. We derived the test statistic based on the cluster analysis of RSS readings. Our approach can detect the presence of attacks as well as determine the number of adversaries, spoofing the same node identity, so that we can localize any number of attackers and eliminate them. Determining the number of adversaries is a particularly challenging problem. Targetj Solutions - 9611582234
  • 10.  We developed SILENCE, a mechanism that employs the minimum distance testing in addition to cluster analysis to achieve better accuracy of determining the number of attackers than other methods under study, such as Silhouette Plot and System Evolution that use cluster analysis alone. Additionally, when the training data are available, we explored using Support Vector Machines-based mechanism to further improve the accuracy of determining the number of attackers present in the system. Targetj Solutions - 9611582234
  • 11.  [1] J. Bellardo and S. Savage, “802.11 Denial-of-Service Attacks: Real Vulnerabilities and Practical Solutions,” Proc. USENIX Security Symp., pp. 15-28, 2003.  [2] F. Ferreri, M. Bernaschi, and L. Valcamonici, “Access Points Vulnerabilities to Dos Attacks in 802.11 Networks,” Proc. IEEE Wireless Comm. and Networking Conf., 2004.  [3] D. Faria and D. Cheriton, “Detecting Identity-Based Attacks in Wireless Networks Using Signalprints,” Proc. ACM Workshop Wireless Security (WiSe), Sept. 2006.  [4] Q. Li and W. Trappe, “Relationship-Based Detection of Spoofing- Related Anomalous Traffic in Ad Hoc Networks,” Proc. Ann. IEEE Comm. Soc. on IEEE and Sensor and Ad Hoc Comm. and Networks (SECON), 2006. Targetj Solutions - 9611582234
  • 12.  [5] B. Wu, J. Wu, E. Fernandez, and S. Magliveras, “Secure and Efficient Key Management in Mobile Ad Hoc Networks,” Proc. IEEE Int’l Parallel and Distributed Processing Symp. (IPDPS), 2005.  [6] A. Wool, “Lightweight Key Management for IEEE 802.11 Wireless Lans With Key Refresh and Host Revocation,” ACM/Springer Wireless Networks, vol. 11, no. 6, pp. 677-686, 2005.  [7] Y. Sheng, K. Tan, G. Chen, D. Kotz, and A. Campbell, “Detecting 802.11 MAC Layer Spoofing Using Received Signal Strength,” Proc. IEEE INFOCOM, Apr. 2008. Targetj Solutions - 9611582234