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Optimal Distributed Malware Defense in Mobile Networks 
with Heterogeneous Devices 
ABSTRACT: 
As malware attacks become more frequently in mobile networks, deploying an 
efficient defense system to protect against infection and to help the infected nodes 
to recover is important to prevent serious spreading and outbreaks. The technical 
challenges are that mobile devices are heterogeneous in terms of operating 
systems, the malware infects the targeted system in any opportunistic fashion via 
local and global connectivity, while the to-be-deployed defense system on the 
other hand would be usually resource limited. In this paper, we investigate the 
problem of how to optimally distribute the content-based signatures of malware, 
which helps to detect the corresponding malware and disable further propagation, 
to minimize the number of infected nodes. We model the defense system with 
realistic assumptions addressing all the above challenges that have not been 
addressed in previous analytical work. Based on the framework of optimizing the 
system welfare utility, which is the weighted summation of individual utility 
depending on the final number of infected nodes through the signature allocation, 
we propose an encounter-based distributed algorithm based on Metropolis sampler.
Through theoretical analysis and simulations with both synthetic and realistic 
mobility traces, we show that the distributed algorithm achieves the optimal 
solution, and performs efficiently in realistic environments. 
EXISTING SYSTEM: 
Mobile malware can propagate through two different dominant approaches. Via 
MMS, a malware may send a copy of itself to all devices whose numbers are found 
in the address book of the infected handset. This kind of malware propagates in the 
social graph formed by the address books, and can spread very quickly without 
geographical limitations. 
The other approach is to use the short-range wireless media such as Bluetooth to 
infect the devices in proximity as “proximity malware.” 
Recent work of Wang et al. has investigated the proximity malware propagation 
features, and finds that it spreads slowly because of the human mobility, which 
offers ample opportunities to deploy the defense system. However, the approach 
for efficiently deploying such a system is still an ongoing research problem.
DISADVANTAGES OF EXISTING SYSTEM: 
 There is a problem for optimal signature distribution to defend mobile 
networks against the propagation of both proximity and MMS-based 
malware. 
 The existing system offers only protection against only one attack at a 
time. 
PROPOSED SYSTEM: 
 To Design a defense system for both MMS and proximity malware. Our 
research problem is to deploy an efficient defense system to help infected 
nodes to recover and prevent healthy nodes from further infection. 
 We formulate the optimal signature distribution problem with the 
consideration of the heterogeneity of mobile devices and malware, and the 
limited resources of the defense system. Moreover, our formulated model is 
suitable for both the MMS and proximity malware propagation. 
 We give a centralized greedy algorithm for the signature distribution 
problem. We prove that the proposed greedy algorithm obtains the optimal
solution for the system, which provides the benchmark solution for our 
distributed algorithm design. 
 We propose an encounter-based distributed algorithm to disseminate the 
malware signatures using Metropolis sampler. It only relies on local 
information and opportunistic contacts. 
ADVANTAGES OF PROPOSED SYSTEM: 
 The system provides optimal signature distribution to defend mobile 
networks against the propagation of both proximity and MMS-based 
malware. 
 The proposed system offers protection against both MMS based attack and 
Bluetooth based attack at the same time.
SYSTEM REQUIREMENTS: 
HARDWARE REQUIREMENTS: 
 System : Pentium IV 2.4 GHz. 
 Hard Disk : 40 GB. 
 Floppy Drive : 1.44 Mb. 
 Monitor : 15 VGA Colour. 
 Mouse : Logitech. 
 Ram : 512 Mb. 
SOFTWARE REQUIREMENTS: 
 Operating system : Windows XP/7. 
 Coding Language : JAVA/J2EE 
 IDE : Netbeans 7.4 
 Database : MYSQL
REFERENCE: 
Ong Li, Member, IEEE, Pan Hui, Member, IEEE, Depeng Jin, Member, IEEE, Li 
Su, and Lieguang Zeng, Member, IEEE. ”Optimal Distributed Malware Defense 
in Mobile Networks with Heterogeneous Devices”. IEEE TRANSACTIONS ON 
MOBILE COMPUTING, VOL. 13, NO. 2, FEBRUARY 2014

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JPN1417 AASR: An Authenticated Anonymous Secure Routing Protocol for MANETs ...
 
JPN1416 Sleep Scheduling for Geographic Routing in Duty-Cycled Mobile Sensor...
JPN1416  Sleep Scheduling for Geographic Routing in Duty-Cycled Mobile Sensor...JPN1416  Sleep Scheduling for Geographic Routing in Duty-Cycled Mobile Sensor...
JPN1416 Sleep Scheduling for Geographic Routing in Duty-Cycled Mobile Sensor...
 
JPN1415 R3E: Reliable Reactive Routing Enhancement for Wireless Sensor Netw...
JPN1415   R3E: Reliable Reactive Routing Enhancement for Wireless Sensor Netw...JPN1415   R3E: Reliable Reactive Routing Enhancement for Wireless Sensor Netw...
JPN1415 R3E: Reliable Reactive Routing Enhancement for Wireless Sensor Netw...
 
JPN1411 Secure Continuous Aggregation in Wireless Sensor Networks
JPN1411   Secure Continuous Aggregation in Wireless Sensor NetworksJPN1411   Secure Continuous Aggregation in Wireless Sensor Networks
JPN1411 Secure Continuous Aggregation in Wireless Sensor Networks
 
JPN1414 Distributed Deployment Algorithms for Improved Coverage in a Networ...
JPN1414   Distributed Deployment Algorithms for Improved Coverage in a Networ...JPN1414   Distributed Deployment Algorithms for Improved Coverage in a Networ...
JPN1414 Distributed Deployment Algorithms for Improved Coverage in a Networ...
 
JPN1413 An Energy-Balanced Routing Method Based on Forward-Aware Factor for...
JPN1413   An Energy-Balanced Routing Method Based on Forward-Aware Factor for...JPN1413   An Energy-Balanced Routing Method Based on Forward-Aware Factor for...
JPN1413 An Energy-Balanced Routing Method Based on Forward-Aware Factor for...
 
JPN1412 Transmission-Efficient Clustering Method for Wireless Sensor Networ...
JPN1412   Transmission-Efficient Clustering Method for Wireless Sensor Networ...JPN1412   Transmission-Efficient Clustering Method for Wireless Sensor Networ...
JPN1412 Transmission-Efficient Clustering Method for Wireless Sensor Networ...
 
JPN1410 Secure and Efficient Data Transmission for Cluster-Based Wireless Se...
JPN1410  Secure and Efficient Data Transmission for Cluster-Based Wireless Se...JPN1410  Secure and Efficient Data Transmission for Cluster-Based Wireless Se...
JPN1410 Secure and Efficient Data Transmission for Cluster-Based Wireless Se...
 
JPN1409 Neighbor Table Based Shortcut Tree Routing in ZigBee Wireless Networks
JPN1409  Neighbor Table Based Shortcut Tree Routing in ZigBee Wireless NetworksJPN1409  Neighbor Table Based Shortcut Tree Routing in ZigBee Wireless Networks
JPN1409 Neighbor Table Based Shortcut Tree Routing in ZigBee Wireless Networks
 
JPN1408 Hop-by-Hop Message Authentication and Source Privacy in Wireless Sen...
JPN1408  Hop-by-Hop Message Authentication and Source Privacy in Wireless Sen...JPN1408  Hop-by-Hop Message Authentication and Source Privacy in Wireless Sen...
JPN1408 Hop-by-Hop Message Authentication and Source Privacy in Wireless Sen...
 
JPN1406 Snapshot and Continuous Data Collection in Probabilistic Wireless S...
JPN1406   Snapshot and Continuous Data Collection in Probabilistic Wireless S...JPN1406   Snapshot and Continuous Data Collection in Probabilistic Wireless S...
JPN1406 Snapshot and Continuous Data Collection in Probabilistic Wireless S...
 
JPN1405 RBTP: Low-Power Mobile Discovery Protocol through Recursive Binary T...
JPN1405  RBTP: Low-Power Mobile Discovery Protocol through Recursive Binary T...JPN1405  RBTP: Low-Power Mobile Discovery Protocol through Recursive Binary T...
JPN1405 RBTP: Low-Power Mobile Discovery Protocol through Recursive Binary T...
 
JPN1404 Optimal Multicast Capacity and Delay Tradeoffs in MANETs
JPN1404 Optimal Multicast Capacity and Delay Tradeoffs in MANETsJPN1404 Optimal Multicast Capacity and Delay Tradeoffs in MANETs
JPN1404 Optimal Multicast Capacity and Delay Tradeoffs in MANETs
 
JPM1410 Images as Occlusions of Textures: A Framework for Segmentation
JPM1410   Images as Occlusions of Textures: A Framework for SegmentationJPM1410   Images as Occlusions of Textures: A Framework for Segmentation
JPM1410 Images as Occlusions of Textures: A Framework for Segmentation
 
JPM1407 Exposing Digital Image Forgeries by Illumination Color Classification
JPM1407   Exposing Digital Image Forgeries by Illumination Color ClassificationJPM1407   Exposing Digital Image Forgeries by Illumination Color Classification
JPM1407 Exposing Digital Image Forgeries by Illumination Color Classification
 

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JPJ1451 Optimal Distributed Malware Defense in Mobile Networks with Heterogeneous Devices

  • 1. Optimal Distributed Malware Defense in Mobile Networks with Heterogeneous Devices ABSTRACT: As malware attacks become more frequently in mobile networks, deploying an efficient defense system to protect against infection and to help the infected nodes to recover is important to prevent serious spreading and outbreaks. The technical challenges are that mobile devices are heterogeneous in terms of operating systems, the malware infects the targeted system in any opportunistic fashion via local and global connectivity, while the to-be-deployed defense system on the other hand would be usually resource limited. In this paper, we investigate the problem of how to optimally distribute the content-based signatures of malware, which helps to detect the corresponding malware and disable further propagation, to minimize the number of infected nodes. We model the defense system with realistic assumptions addressing all the above challenges that have not been addressed in previous analytical work. Based on the framework of optimizing the system welfare utility, which is the weighted summation of individual utility depending on the final number of infected nodes through the signature allocation, we propose an encounter-based distributed algorithm based on Metropolis sampler.
  • 2. Through theoretical analysis and simulations with both synthetic and realistic mobility traces, we show that the distributed algorithm achieves the optimal solution, and performs efficiently in realistic environments. EXISTING SYSTEM: Mobile malware can propagate through two different dominant approaches. Via MMS, a malware may send a copy of itself to all devices whose numbers are found in the address book of the infected handset. This kind of malware propagates in the social graph formed by the address books, and can spread very quickly without geographical limitations. The other approach is to use the short-range wireless media such as Bluetooth to infect the devices in proximity as “proximity malware.” Recent work of Wang et al. has investigated the proximity malware propagation features, and finds that it spreads slowly because of the human mobility, which offers ample opportunities to deploy the defense system. However, the approach for efficiently deploying such a system is still an ongoing research problem.
  • 3. DISADVANTAGES OF EXISTING SYSTEM:  There is a problem for optimal signature distribution to defend mobile networks against the propagation of both proximity and MMS-based malware.  The existing system offers only protection against only one attack at a time. PROPOSED SYSTEM:  To Design a defense system for both MMS and proximity malware. Our research problem is to deploy an efficient defense system to help infected nodes to recover and prevent healthy nodes from further infection.  We formulate the optimal signature distribution problem with the consideration of the heterogeneity of mobile devices and malware, and the limited resources of the defense system. Moreover, our formulated model is suitable for both the MMS and proximity malware propagation.  We give a centralized greedy algorithm for the signature distribution problem. We prove that the proposed greedy algorithm obtains the optimal
  • 4. solution for the system, which provides the benchmark solution for our distributed algorithm design.  We propose an encounter-based distributed algorithm to disseminate the malware signatures using Metropolis sampler. It only relies on local information and opportunistic contacts. ADVANTAGES OF PROPOSED SYSTEM:  The system provides optimal signature distribution to defend mobile networks against the propagation of both proximity and MMS-based malware.  The proposed system offers protection against both MMS based attack and Bluetooth based attack at the same time.
  • 5. SYSTEM REQUIREMENTS: HARDWARE REQUIREMENTS:  System : Pentium IV 2.4 GHz.  Hard Disk : 40 GB.  Floppy Drive : 1.44 Mb.  Monitor : 15 VGA Colour.  Mouse : Logitech.  Ram : 512 Mb. SOFTWARE REQUIREMENTS:  Operating system : Windows XP/7.  Coding Language : JAVA/J2EE  IDE : Netbeans 7.4  Database : MYSQL
  • 6. REFERENCE: Ong Li, Member, IEEE, Pan Hui, Member, IEEE, Depeng Jin, Member, IEEE, Li Su, and Lieguang Zeng, Member, IEEE. ”Optimal Distributed Malware Defense in Mobile Networks with Heterogeneous Devices”. IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 13, NO. 2, FEBRUARY 2014