Distributed DoS Detection in IoT Networks Using Intelligent Machine Learning Algorithms
Paper in proceeding, 2021

The threat of a Distributed Denial of Service (DDoS) attack on web-based services and applications is grave. It only takes a few minutes for one of these attacks to cripple these services, making them unavailable to anyone. The problem has further persisted with the widespread adoption of insecure Internet of Things (IoT) devices across the Internet. In addition, many currently used rule-based detection systems are weak points for attackers. We conducted a comparative analysis of ML algorithms to detect and classify DDoS attacks in this paper. These classifiers compare Nave Bayes with J48 and Random Forest with ZeroR ML as well as other machine learning algorithms. It was found that using the PCA method, the optimal number of features could be found. ML has been implemented with the help of the WEKA tool.

Nave Bayes

Random Forest

ZeroR

DDoS

IoT

Author

S. Binny

Kristu Jyoti College of Management and Technology

Shamili Srimani Pendyala

Institute of Aeronautical Engineering, Hyderabad

S. John Pimo

St. Xavier's Catholic College of Engineering

Sagaya Aurelia

Christ University, Bengaluru

P. Rahul Reddy

Geethanjali Institute of Science and Technology

Damaraju Sri Sai Satyanarayana

Student at Chalmers

2021 2nd International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2021 - Proceedings


9781665482974 (ISBN)

2nd International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2021
Bengaluru, India,

Subject Categories

Other Computer and Information Science

Computer Science

Computer Systems

DOI

10.1109/ICSTCEE54422.2021.9708569

More information

Latest update

3/14/2022