Skip to content

Latest commit

 

History

History
123 lines (90 loc) · 4.55 KB

README.md

File metadata and controls

123 lines (90 loc) · 4.55 KB
Raw

HW2-CS441

Arijus Trakymas

atraky2@uic.edu

Prerequisites

Run the project

Make sure Spark is running (if running locally)

Start Spark on your machine if running locally.

The instructions are for Windows users who have to start services manually.

In Spark home directory bin folder:

.\spark-class org.apache.spark.deploy.master.Master

Spark is very picky about IPs. If this fails, go to the terminal where you started master and grab the spark://host:port that is provided.

.\spark-class org.apache.spark.deploy.worker.Worker spark://localhost:7077

Create the jar file

sbt clean compile assembly

Run locally (Apache Spark compiled against Scala 2.13)

2.13.10 gives no issues.

~/spark-3.5.0-bin-hadoop3-scala2.13/bin/spark-submit.cmd --master local[4] --class Main --jars target/scala-2.13/HW2.jar --driver-class-path target/scala-2.13/HW2.jar -Doriginal_graph_file_name="example.ngs" target/scala-2.13/HW2.jar

Run locally (Apache Spark compiled against Scala 2.12)

2.12 gives issues with serial UUID for Scala list.

Amazon EMR 6.14.0 supports Scala 2.12.15 with Apache Spark 3.4.1

~/spark-3.4.1-bin-hadoop3/bin/spark-submit.cmd --master local[4] --class Main --jars target/scala-2.12/HW2.jar --driver-class-path target/scala-2.12/HW2.jar target/scala-2.12/HW2.jar

Run via the command line (submit to Spark cluster)

~/spark-3.5.0-bin-hadoop3-scala2.13/bin/spark-submit.cmd --master spark://192.168.50.64:7077 --executor-memory 20G --class Main --jars target/scala-2.13/HW2.jar --driver-class-path target/scala-2.13/HW2.jar target/scala-2.13/HW2.jar

or

Run via Intellij

Load the project in Intellij IDEA and click on start.

Run tests

sbt clean compile test

Design

Preprocessing

For this assignment, we are interested in nodes (servers/services) that contain valuable information. In order to have a baseline of what nodes contain valuable information we must process the original graph by performing a walk in the graph and storing information about nodes that contain valuable information.

Nodes are deemed valuable if their valuableData property is equal to true.

System

For this project we are simulating "man in the middle" (MitM) attacks on computer networks. In our case the computer network is represented in the form of a graph. We construct our graph using the output from NetGameSim and constructing a GraphX graph using the vertices and edges from NetGameSim.

The vertices used in our graph for GraphX uses the node ID as the VertexID and the node object itself as the only property of the vertex.

The edges used in our graph consist of the from node ID and to node ID both from the node objects.

The attack is defined as a random walk in the graph in which the attacker uses a stochastic process to determine what to do on each step of the walk. The possible options of the outcome of the process is to either to

  • finish the walk
  • move to a neighboring node

If the attacker decides to end the walk, then nothing else happens.

If the attacker looks at the neighbors of the current node and sees that there are available neighbors to walk to, they will pick one of the neighbors at random and walk there provided are iterations still left.

Upon arriving to the node, the attacker will then perform a SimRank on the node by comparing it to all nodes in the original network (graph). After performing the similarity analysis, the attacker will then pick at random one of the nodes that was equal to or above the similarity threshold. The SimRank algorithm has been reused from HW1.

If the attacker finds themselves stuck, the walk will end there. Additionally, if the attacker attempted to perform an attack on the current node and was unsuccessful the walk will end there as well.

This attack is performed on the perturbed graph since it represents the actual network topology including honeypots and modifications to existing machines.

The termination criteria for the walk is the number of nodes visited is equivalent to the number of nodes in the graph multiplied by a constant.

The number of iterations or in other words the number of attacks performed is also set but can be configured to adjust the number of attacks simulated.

Configuration

  • similarity threshold: the threshold for which nodes to have match in order to be considered a valid match
    • if this is too low there will be too many matches
  • number of attackers: number of starting nodes from random walkers start walking