ROUT-4-2023: RPL Based Routing Attack Dataset for IoT

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Abstract 

This dataset consists of .csv files of 4 different routing attacks (Blackhole Attack, Flooding Attack, DODAG Version Number Attack and Decreased Rank Attack) targeting the RPL protocol and these files are taken from Cooja (Contiki network simulator). It gives researchers the opportunity to develop IDS for RPL-based IoT networks using Artificial Intelligence and Machine Learning methods without simulating attacks. Simulating these attacks is an important step towards developing and testing protection mechanisms against such attacks by mimicking real-world attack scenarios. For these researchers, it may offer an alternative approach to intrusion detection systems that have limitations of traditional methods. This requires identifying appropriate attributes that include characteristics of attacks, analyzing network traffic data, and considering other relevant parameters. It is also important that your dataset is balanced and representative so that your model can accurately identify and predict different types of attacks. In conclusion, this study is an important step in the field of IoT security. By simulating four different routing attacks to IoT devices through Cooja, we present a new dataset to detect attacks for application in artificial intelligence and machine learning methods.

  

Instructions: 

Features and descriptions in the ROUT-4-2023 dataset:

Name/Abbreviation

Description

TIME

Simulation time

SOURCE

Source Node IP

DESTINATION

Destination Node IP

LENGTH

Packet Length

INFO

Packet Information

TR

Transmission Rate(per 1000_ms)

RR

Reception Rate(per 1000 ms)

TAT

Transmission Average Time

RAT

Reception Average Time

TPC

Transmitted Packet Count(per second)

RPC

Received Packet Count(per second)

TTT

Total Transmission Time

TRT

Total Reception Time

DAO

DAO Packet Count

DIS

DIS Packet Count

DIO

DIO Packet Count

CATEGORY

Attack Type or Normal

LABEL

Normal/Malicious Label