presentation big data analytics on Apache spark

Streaming Data Analytics For Unknown
(Attacker) Vehicle Identification In Passive
Sensing Scenario
May 13, 2023
Streaming Data Analytics For Unknown (Attacker) Vehicle Identification In Passive Sensing Scenario
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Description: What Needs to be Solved?
Goal: To determine position of attacking vehicle using indirect wireless
emissions RSS(Received Signal Strength) collected in RSU (Road Side Units)
RSUs such collect Service massages(Location, Vehicle ID) and RSS
Red color vehicle is spoofed location of the attacker vehicle reported to RSUs
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General Description: How are we Solving?
To localization vehicle position using ranges from multiple RSU.
Range is calculated from Signals emitting from vehicle.
These signals can be from TPMS, Bluetooth, or LTE usage
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Motivation
Connected Vehicles equipped with IoT technologies can receive notifications
from the road infrastructure i.e. Road side units for safety. This is useful for
enhancing Situational awareness for non-line of sight scenarios
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Flowchart of Approach
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Data Processing Approach?
At the system level, localization of the attacker vehicle is conducted using its
wireless emissions
Using the localization method, localized position estimates are obtained from
different time instances. Features such as velocity of the vehicle is extracted
from these position estimates.
These features are processed using a Streaming Analytics Pipeline where
Machine learning model classifies a vehicle as attacker or normal
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Streaming Analytics
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Streaming Analytics: FlowChart
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Streaming Analytics: Genral Data Flow
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Apache Kafka
Apache Kafka is a distributed event store which connect to
external systems for data import/export
Figure: Kafka Publisher Subscriber
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Streaming Processing Input: Producer
Figure: Create Topic
Figure: Create Producer
Figure: Sending Streams of data from a File over TCP Using Netcat
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Streaming Processing Input: Consumer
Configuring the hostname:port for Kafka brokers
Entering the topics you want to listen for from Kafka
Create our Kafka stream, which will contain (topic, message) pairs.
map() transformation is used to extract the data (value) from the message
Figure: Listening for the Topic
Figure: listening Streams of data from a File over TCP Using Netcat
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Streaming Data Processing: Classification
Simple features such as distance between service message and attacker is
calculated
Streaming logistic regression classifier is used
Figure: Classifier Predictions(left) True Label(right)
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Processing Classification Predictions
Obtain the predictions from binary attack vehicle classifier in which attack
vehicle was detected
Create a object predictiond ataobject having vehicle id of each detection
Map this into (URL, 1) tuples
Reduce the tuples to count each instance of detected vehicle in a time window
Sort print the results as batches come in
Figure: Vehicle ID(left) True Count (right)
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Time Window Based Processing
Batch, slide window interval are used as parameters for operation
Batch interval defines how often data is captured from stream
Slide interval define how often a windowed transformation is computed
Window interval is how many previous batches to be included in the window
transformation.
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Filter Streaming Data
After processing Classification Predictions vehicle id with maximum detections is
obtained
Using filter transformation we filter data belonging to this vehicle id and store
the data such as id, latitiude, longitude
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Summary
A range-based localization method using ranges from multiple
RSUs (road side units) was used estimates the position of
target vehicle
Localization was performed using a OLS (i.e., ordinary least
square)
Kalman filter was applied to the OLS estimates
Validation of the method was performed on simple simulated
scenario
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Thank you!
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presentation big data analytics on Apache spark

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