Sybil Attack in NS2

Benefits of implementing Sybil attack using NS2 simulator

What is Sybil Attack?

In an Network a malicious node illegitimately claims to have multiple identities on a single physical device is known as Sybil Attack.If an entity on a network does not have physical knowledge of the other entities, it will perceive them purely as informational abstractions called identities.We have successfully delivered more than 75+ Sybil attack in ns2 Project to B.E/B.Tech/M.E/M.Tech and PhD Scholars.Sybil Attack can be prevented using various algorithm and can be simulated using ns2.To Simulate Sybil Attack in ns2 , we need to find malicious node.

Architecture-of-Sybil-Attack-in-Manet

Types of Sybil attack in Manet:

  • Simultaneous Sybil attack.
  • join and leave Sybil attack.

Detection mechanisms involved in Manet:

  • request threshold validation mechanism.
  • trusted certification.
  • hashing function.
  • incentive-based detection.
  • location/position verification.
  • random key predistribution.
  • RSSI-based scheme etc.

Malicious applications of Sybil attack:

  • Data aggregation.
  • Routing.
  • Distributed storage.
  • Fair resource allocation.
  • Tampering with voting and reputation systems .

NS2 Projects Video Output

See our Latest Video Output of Ns2 Projects on Various Domain.

Ns2 Projects

Customized NS2 Projects for B.E/B.Tech/M.E/M.Tech/Ms/PhD Scholars.

Ns2 Projects Screen Shots

Ns2 Projects Screen Shots.Regular Update of NS2 Projects Screenshots here!

Sample code for Sybil attack: This is the sample code of Sybil attack.



#create lists which contains the claimed and estimated positions of sybil nodes
 #set claimed {(,) (,)}
#set estimated {(,) (,)}
 #create neighbour list of all sybil nodes - 2d array
 set neighbour_sybil [ list {12 5 15 18 17} {14 20 5 12} {5 19} {5 12} {19 5 20} {5 15 8 19} {19 5 8} {20 5 19} {5 12 15} ] 
#Display neighbour list of all sybil nodes
 for {set i 0} {$i < [llength $neighbour_sybil]} {incr i}  { 
set l($i) [lindex $neighbour_sybil $i]
puts "Neighbour list of [lindex $mylist_sybil $i] is $l($i)"
 }
  
#malicious node detection
 proc detect {mylist} { 
set length [llength $mylist]
 for {set i 0} {$i < 21} {incr i} {
 set count($i) 0
 }
  for {set j 0} {$j < 9} {incr j} {
 set l($j) [lindex $mylist $j]
for {set i 0} {$i < [llength $l($j)]} {incr i} {
 set m($i) [lindex $l($j) $i]
set count($m($i)) [expr {$count($m($i))+1} ]
 }
 }
 for {set i 0} {$i < 21} {incr i} {
 puts "Suspicion factor of $i is $count($i)"
 }
 puts "Maximum suspicion factor is for node 5"
puts "Node 5 is the malicious node"}
 detect $neighbour_sybil
 
# Provide initial location of mobilenodes
$node_(0) set X_ -89.0
$node_(0) set Y_ 977.0
$node_(0) set Z_ 0.0
$ns at 0.0 "$node_(0) label RSU0"
 
$node_(1) set X_ 541.0
$node_(1) set Y_ 494.0
$node_(1) set Z_ 0.0
 
$node_(2) set X_ 86.0
$node_(2) set Y_ 620.0
$node_(2) set Z_ 0.0
 
$node_(17) set X_ 554.0
$node_(17) set Y_ 385.0
$node_(17) set Z_ 0.0
 
$node_(4) set X_ 365.0
$node_(4) set Y_ 727.0
$node_(4) set Z_ 0.0
 
$node_(18) set X_ 699.0
$node_(18) set Y_ 494.0
$node_(18) set Z_ 0.0
 
$node_(19) set X_ 498.0
$node_(19) set Y_ 840.0
$node_(19) set Z_ 0.0
 
$node_(20) set X_ -36.0
$node_(20) set Y_ 727.0
$node_(20) set Z_ 0.0
 
$node_(3) set X_ 1165.0
$node_(3) set Y_ 601.0
$node_(3) set Z_ 0.0
$ns at 0.0 "$node_(3) label RSU3"
 
$node_(5) set X_ 269.0
$node_(5) set Y_ 620.0
$node_(5) set Z_ 0.0
 
$node_(6) set X_ 148.0
$node_(6) set Y_ 494.0
$node_(6) set Z_ 0.0
 
$node_(7) set X_ 86.0
$node_(7) set Y_ 840.0
$node_(7) set Z_ 0.0
 
$node_(8) set X_ 723.0
$node_(8) set Y_ 727.0
$node_(8) set Z_ 0.0
 
$node_(10) set X_ 704.0
$node_(10) set Y_ 620.0
$node_(10) set Z_ 0.0
 
$node_(11) set X_ 292.0
$node_(11) set Y_ 839.0
$node_(11) set Z_ 0.0
 
$node_(12) set X_ 310.0
$node_(12) set Y_ 494.0
$node_(12) set Z_ 0.0
 
$node_(13) set X_ 156.0
$node_(13) set Y_ 727.0
$node_(13) set Z_ 0.0
 
$node_(14) set X_ -36.0
$node_(14) set Y_ 620.0
$node_(14) set Z_ 0.0
 
$node_(15) set X_ 582.0
$node_(15) set Y_ 620.0
$node_(15) set Z_ 0.0
 
$node_(16) set X_ 439.0
$node_(16) set Y_ 620.0
$node_(16) set Z_ 0.0
 


Journal Support for Research Scholars
NS2 Projects Journal support
Ns2 Projects Work Progress
  • MANET – Mobile Ad Hoc Network 95%
  • VANET – Vechicle Ad Hoc Netwok 97%
  • LTE – Long Term Evolution 78%
  • IoT – Internet of Things 90%
  • Wireless Sensor Network 89%
  • Network Security 89%
  • Ns2 Attacks 96%
  • Cognitive Radio Network 85%
  • Parallel and Distributed Computing 73%
  • SDN – Software Defined Networking 95%
  • P2P , Video Streaming , Peersim 96%
  • IPV4 , IPV6 88%
  • 4G Network , 5G Network 80%
  • Visual , Underwater Sensor Network 79%
  • Multicasting Communication 84%
  • Wimax, WiFi 90%
  • OFDMA 94%
Our Achievements – Ns2 Projects
Ns2 Projects Achievements