Analysis of Resilient Autonomous Navigation Adversarial Attacks

Analysis of Resilient Autonomous Navigation Adversarial Attacks

Performance Analysis of Resilient Autonomous Navigation under Adversarial Attacks

Implementation plan:
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Scenario-1 (Without Attack):
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Step 1: Initially, we construct a network consisting of 5 – Vehicles, 5 – gNodeB, 1 – router, 1 – Server.

Step 2: Next, we Implement the on-board unit component (Camera, Lidar, Radar, and GPS receiver) into the network without attack

Step 3: Finally, plot performance for the following metrics:

3.1: Number of Vehicles vs. Packet Delivery Ratio (%)
3.2: Number of Vehicles vs. Latency (ms)
3.3: Number of Vehicles vs. Throughput (Mbps)
3.4: Time (ms) vs. State of Sensor

Scenario-2 (With Attack):
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Step 1: Initially, we construct a network consisting of 5 – Vehicles, 5 – gNodeB, 1 – router, 1 – Server.

Step 2: Next, we Implement the on-board unit component (Camera, Lidar, Radar, and GPS receiver) into the network without attack

Step 3: Then, Implement the attack on the on-board unit component.

Step 4: Finally, plot performance for the following metrics:

4.1: Number of Vehicles vs. Packet Delivery Ratio (%)
4.2: Number of Vehicles vs. Latency (ms)
4.3: Number of Vehicles vs. Throughput (Mbps)
4.4: Time (ms) vs. State of Sensor
4.5: Successful attacks (%) vs. Attacked Sensors

Scenario-3 (After RDO):
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Step 1: Initially, we construct a network consisting of 5 – Vehicles, 5 – gNodeB, 1 – router, 1 – Server.

Step 2: Next, we Implement the on-board unit component (Camera, Lidar, Radar, and GPS receiver) into the network without attack

Step 3: Then, Implement the attack on the on-board unit component.

Step 4: Next, implement a robust Resilient Distributed Optimization (RDO) Algorithm for the V2V communication on the on-board unit component which is being attacked.

Step 5: Finally, plot performance for the following metrics:

5.1: Number of Vehicles vs. Packet Delivery Ratio (%)
5.2: Number of Vehicles vs. Latency (ms)
5.3: Number of Vehicles vs. Throughput (Mbps)
5.4: Time (ms) vs. State of Sensor
5.5: Successful attacks (%) vs. Attacked Sensors

Software requirements:
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[+] Development tool: Omnet++ 4.6 or above

[+] Development OS: Ubuntu or Windows 10

Note:-
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[1] If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.

[2] Please note that this implementation plan does not include any further steps after it is put into implementation.

[3] If the above plan satisfies your requirement please confirm with us.

[4] We make simulation type processes only, not a real-time process.

Live Tasks
Technology Ph.D MS M.Tech
NS2 75 117 95
NS3 98 119 206
OMNET++ 103 95 87
OPNET 36 64 89
QULANET 30 76 60
MININET 71 62 74
MATLAB 96 185 180
LTESIM 38 32 16
COOJA SIMULATOR 35 67 28
CONTIKI OS 42 36 29
GNS3 35 89 14
NETSIM 35 11 21
EVE-NG 4 8 9
TRANS 9 5 4
PEERSIM 8 8 12
GLOMOSIM 6 10 6
RTOOL 13 15 8
KATHARA SHADOW 9 8 9
VNX and VNUML 8 7 8
WISTAR 9 9 8
CNET 6 8 4
ESCAPE 8 7 9
NETMIRAGE 7 11 7
BOSON NETSIM 6 8 9
VIRL 9 9 8
CISCO PACKET TRACER 7 7 10
SWAN 9 19 5
JAVASIM 40 68 69
SSFNET 7 9 8
TOSSIM 5 7 4
PSIM 7 8 6
PETRI NET 4 6 4
ONESIM 5 10 5
OPTISYSTEM 32 64 24
DIVERT 4 9 8
TINY OS 19 27 17
TRANS 7 8 6
OPENPANA 8 9 9
SECURE CRT 7 8 7
EXTENDSIM 6 7 5
CONSELF 7 19 6
ARENA 5 12 9
VENSIM 8 10 7
MARIONNET 5 7 9
NETKIT 6 8 7
GEOIP 9 17 8
REAL 7 5 5
NEST 5 10 9
PTOLEMY 7 8 4

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