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Defending Against Collaborative Attacks by Malicious 
Nodes in MANETs: A Cooperative Bait Detection Approach 
ABSTRACT: 
In mobile ad hoc networks (MANETs), a primary requirement for the 
establishment of communication among nodes is that nodes should cooperate with 
each other. In the presence of malevolent nodes, this requirement may lead to 
serious security concerns; for instance, such nodes may disrupt the routing process. 
In this context, preventing or detecting malicious nodes launching grayhole or 
collaborative blackhole attacks is a challenge. This paper attempts to resolve this 
issue by designing a dynamic source routing (DSR)-based routing mechanism, 
which is referred to as the cooperative bait detection scheme (CBDS), that 
integrates the advantages of both proactive and reactive defense architectures. Our 
CBDS method implements a reverse tracing technique to help in achieving the 
stated goal. Simulation results are provided, showing that in the presence of 
malicious-node attacks, the CBDS outperforms the DSR, 2ACK, and best-effort 
fault-tolerant routing (BFTR) protocols (chosen as benchmarks) in terms of packet 
delivery ratio and routing overhead (chosen as performance metrics).
EXISTING SYSTEM: 
DSR involves two main processes: route discovery and route maintenance. To 
execute the route discovery phase, the source node broadcasts a Route Request 
(RREQ) packet through the network. If an intermediate node has routing 
information to the destination in its route cache, it will reply with a RREP to the 
source node. When the RREQ is forwarded to a node, the node adds its address 
information into the route record in the RREQ packet. When destination receives 
the RREQ, it can know each intermediary node’s address among the route.The 
destination node relies on the collected routing information among the packets in 
order to send a reply RREP message to the source node along with the whole 
routing information of the established route. 
DISADVANTAGES OF EXISTING SYSTEM: 
 The lack of any infrastructure added with the dynamic topology feature of 
MANETs make these networks highly vulnera ble to routing attacks such as 
blackhole and grayhole (known as variants of blackhole attacks). 
 In this regard, the effectiveness of these approaches becomes weak when 
multiple malicious nodes collude together to initiate a collaborative attack, 
which may result to more devastating damages to the network.
PROPOSED SYSTEM: 
In this paper, a mechanism [so-called cooperative bait detection scheme (CBDS)] 
is presented that effectively detects the malicious nodes that attempt to launch 
grayhole/collaborative blackhole attacks. In our scheme, the address of an adjacent 
node is used as bait destination address to bait malicious nodes to send a reply 
RREP message, and malicious nodes are detected using a reverse tracing 
technique. Any detected malicious node is kept in a blackhole list so that all other 
nodes that participate to the routing of the message are alerted to stop 
communicating with any node in that list. Unlike previous works, the merit of 
CBDS lies in the fact that it integrates the proactive and reactive defense 
architectures to achieve the aforementioned goal. 
ADVANTAGES OF PROPOSED SYSTEM: 
 In this setting, it is assumed that when a significant drop occurs in the packet 
delivery ratio, an alarm is sent by the destination node back to the source 
node to trigger the detection mechanism again. 
 This function assists in sending the bait address to entice the malicious 
nodes and to utilize the reverse tracing program of the CBDS to detect the 
exact addresses of malicious nodes.
SYSTEM ARCHITECTURE: 
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/LINUX. 
 Implementation : NS2 
 NS2 Version : NS2.2.28 
 Front End : OTCL (Object Oriented Tool Command 
Language) 
 Tool : Cygwin (To simulate in Windows OS) 
REFERENCE: 
Jian-Ming Chang, Po-Chun Tsou, Isaac Woungang, Han-Chieh Chao, and Chin- 
Feng Lai, Member, IEEE, “Defending Against Collaborative Attacks by 
Malicious Nodes in MANETs: A Cooperative Bait Detection Approach”, 
IEEE SYSTEMS JOURNAL, 2014.

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JPN1418 PSR: A Lightweight Proactive Source Routing Protocol For Mobile Ad H...
JPN1418  PSR: A Lightweight Proactive Source Routing Protocol For Mobile Ad H...JPN1418  PSR: A Lightweight Proactive Source Routing Protocol For Mobile Ad H...
JPN1418 PSR: A Lightweight Proactive Source Routing Protocol For Mobile Ad H...
 
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
 
JPM1417 Characterness: An Indicator of Text in the Wild
JPM1417   Characterness: An Indicator of Text in the WildJPM1417   Characterness: An Indicator of Text in the Wild
JPM1417 Characterness: An Indicator of Text in the Wild
 
JPM1416 A Unified Data Embedding and Scrambling Method
JPM1416   A Unified Data Embedding and Scrambling MethodJPM1416   A Unified Data Embedding and Scrambling Method
JPM1416 A Unified Data Embedding and Scrambling Method
 

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JPN1422 Defending Against Collaborative Attacks by Malicious Nodes in MANETs: A Cooperative Bait Detection Approach

  • 1. Defending Against Collaborative Attacks by Malicious Nodes in MANETs: A Cooperative Bait Detection Approach ABSTRACT: In mobile ad hoc networks (MANETs), a primary requirement for the establishment of communication among nodes is that nodes should cooperate with each other. In the presence of malevolent nodes, this requirement may lead to serious security concerns; for instance, such nodes may disrupt the routing process. In this context, preventing or detecting malicious nodes launching grayhole or collaborative blackhole attacks is a challenge. This paper attempts to resolve this issue by designing a dynamic source routing (DSR)-based routing mechanism, which is referred to as the cooperative bait detection scheme (CBDS), that integrates the advantages of both proactive and reactive defense architectures. Our CBDS method implements a reverse tracing technique to help in achieving the stated goal. Simulation results are provided, showing that in the presence of malicious-node attacks, the CBDS outperforms the DSR, 2ACK, and best-effort fault-tolerant routing (BFTR) protocols (chosen as benchmarks) in terms of packet delivery ratio and routing overhead (chosen as performance metrics).
  • 2. EXISTING SYSTEM: DSR involves two main processes: route discovery and route maintenance. To execute the route discovery phase, the source node broadcasts a Route Request (RREQ) packet through the network. If an intermediate node has routing information to the destination in its route cache, it will reply with a RREP to the source node. When the RREQ is forwarded to a node, the node adds its address information into the route record in the RREQ packet. When destination receives the RREQ, it can know each intermediary node’s address among the route.The destination node relies on the collected routing information among the packets in order to send a reply RREP message to the source node along with the whole routing information of the established route. DISADVANTAGES OF EXISTING SYSTEM:  The lack of any infrastructure added with the dynamic topology feature of MANETs make these networks highly vulnera ble to routing attacks such as blackhole and grayhole (known as variants of blackhole attacks).  In this regard, the effectiveness of these approaches becomes weak when multiple malicious nodes collude together to initiate a collaborative attack, which may result to more devastating damages to the network.
  • 3. PROPOSED SYSTEM: In this paper, a mechanism [so-called cooperative bait detection scheme (CBDS)] is presented that effectively detects the malicious nodes that attempt to launch grayhole/collaborative blackhole attacks. In our scheme, the address of an adjacent node is used as bait destination address to bait malicious nodes to send a reply RREP message, and malicious nodes are detected using a reverse tracing technique. Any detected malicious node is kept in a blackhole list so that all other nodes that participate to the routing of the message are alerted to stop communicating with any node in that list. Unlike previous works, the merit of CBDS lies in the fact that it integrates the proactive and reactive defense architectures to achieve the aforementioned goal. ADVANTAGES OF PROPOSED SYSTEM:  In this setting, it is assumed that when a significant drop occurs in the packet delivery ratio, an alarm is sent by the destination node back to the source node to trigger the detection mechanism again.  This function assists in sending the bait address to entice the malicious nodes and to utilize the reverse tracing program of the CBDS to detect the exact addresses of malicious nodes.
  • 4. SYSTEM ARCHITECTURE: 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.
  • 5.  Ram : 512 Mb. SOFTWARE REQUIREMENTS:  Operating system : Windows XP/7/LINUX.  Implementation : NS2  NS2 Version : NS2.2.28  Front End : OTCL (Object Oriented Tool Command Language)  Tool : Cygwin (To simulate in Windows OS) REFERENCE: Jian-Ming Chang, Po-Chun Tsou, Isaac Woungang, Han-Chieh Chao, and Chin- Feng Lai, Member, IEEE, “Defending Against Collaborative Attacks by Malicious Nodes in MANETs: A Cooperative Bait Detection Approach”, IEEE SYSTEMS JOURNAL, 2014.