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 # MANET-Rank
 
-A competitive evaluation framework for defense protocols in Mobile Ad Hoc Environments (MANETs). Complete code will be available soon.
+A competitive evaluation framework for defense protocols in Mobile Ad Hoc Environments (MANETs).
 
 # Content 
 
-- Full C++ NS-3.36 code for the lightweight protcol, the DST protocol and the attack protocol
-- Slurm file generation to automate NS3 simulations in High Performance Compute facilities (Like IRIDIS at Southampton, UK)
-- Python Code to extract the NS3 simulation results.
-- Python Code for bootstrapping and final results.
+- Full C++ NS-3.36 code for the lightweight protcol, the DST protocol and the Velocity-threshold protocol (Coming Soon)
+- Full C++ NS-3.36 code for each grey hole attack type (Probabilistic, Time-Based and Precusor-Based) (Coming Soon)
+- Slurm file generation to automate NS3 simulations in High Performance Compute facilities, Like IRIDIS at Southampton, UK.
+- Python Code to run MANET-Rank
+
+# Requirements
+
+# Installation
+
+## Running MANET-Rank on Pre-processed data
+
+To run MANET-Rank with the pre-processed data simple run:
+```python MANET-Rank.py "results/"```
+
+By default, this will create a DPDR/ folder in the "results/" folder which will hold 5 types of files:
+- histogram_*.png files: these graphs will show the regret distributions for all strategies, per environment.
+- first_placed_rank.csv: this will show the winning strategy for all environments.
+- ranked_list_*.csv: this will show the rankings for each environment for MANET-Rank
+- orig_first_placed_rank.csv: this will show the winning strategies for all environments (without bootstrapping).
+- orig_ranked_list_*.csv: this will show the rankings for each environment for MANET-Rank (without bootstrapping).
+
+If you ran MANET-Rank.py without editing the file, you would have created DPDR strategies, in the main section of MANET-Rank.py you can see a few commented lines of code:
+```
+#generate_manet_rank(_processed_file_dir, "Attack_DPDR/", "DPDR", inversion=True, attacker_analysis=True)
+#generate_manet_rank(_processed_file_dir, "Attack_EED/", "EED", inversion=False, attacker_analysis=True)
+#generate_manet_rank(_processed_file_dir, "Attack_FScore/", "FScore", inversion=True, attacker_analysis=True)
+#generate_manet_rank(_processed_file_dir, "FScore/", "FScore", inversion=False, attacker_analysis=False)
+```
+- _processed_file_dir is the directory where the processed file is (specified at command line level).
+- "Attack_DPDR/" is the folder name that we want to create
+- "DPDR" is the metric of interest
+- inversion=True/False mutliplies the performance metric by -1 if needed (in the case of EED, the performance metrics inside the CSV are already negative)
+- attacker_analysis=True/False switches to the attack perspective, which will produce rankings of attack strategies.
+
+# File Explanation
+
+- ConfigFileCreate.py: This python file, in conjunction with ConfigFileHelper.py and StrategyHelper.py, will take a JSON file of attacker/defender strategies in combination with environment paramters to produce configuration files for the NS3 software.
+- GenerateCommands.py: This python file generates slurm files for HPCs using hpc_template.slurm and master_template.slurm as a base.
+- PostProcess.py: This python file takes a large amount of .csv files (generated from the NS3 software) and combines them into one csv file "processed_data.csv".
+- 
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