# A Lightweight Model for DDoS Attack Detection Using Machine Learning Techniques
## 6 Conclusions  
The study in the paper characterizes lightweight IoT networks as being established by devices with few computer resources, such as reduced battery life, processing power, memory, and, more critically, minimal security and protection, which are easily vulnerable to DDoS attacks and propagating malware. Numerous scholars have studied the notion that protecting lightweight IoT networks urgently requires improving intrusion detection systems. This manuscript proposes a compact intrusion detection system that blends machine learning classifiers with a fresh approach to data pre-processing. The study provides a number of classifier types to build lightweight intrusion detection systems that are ideally suited for defense against DDoS attacks in IoT networks. The dataset from the TON-IOT from UNSW Network and BOT-IOT are used. The dataset is used to produce DDoS assault samples. Binary and multiple-class classifications of the experiment for the DDoS attacks and all attacks, respectively, are undertaken in TON-IOT and BOT-IOT datasets. The attack classes of the TON-IOT and the normal attacks of BOT-IOT are imbalanced. In this study, multiple iterations of the SMOTE technique are used to balance classes in the experiments using the TON-IOT and BOT-IOT dataset.  
Binary classification and multiple-class classification between DDoS and normal traffic evaluated the performance of five ML methods (RF, LR, KNN, ANN, NB) based on the accuracy, recall, precision, F1 score, training time, prediction time, and total time. The datasets used are TON-IOT network train/test dataset and BOT-IOT dataset. The performance analysis was done with 15 features of both BOT-IOT and TON-IOT datasets using ExtraTreeClassifier. A comparison between existing state-of-the-art models and the proposed model was made. It was observed that, in the TON-IOT dataset for multiple-class classification for 15 features, random forest is the best algorithm, with 100% accuracy, whereas in binary classification naive bayes is the best, with 100%. In the BOT-IOT dataset, it was observed that for both multiple-class and binary classification naive bayes obtains an accuracy of 100%. Comparison was made between the existing state-of-art models and the proposed model.  
The future work for this research is going to be as follows. Firstly, implementing the same process on a different IoT-related dataset and testing the existing method. Secondly, implementing deep learning techniques to potentially create better models to detect attacks. Thirdly, creating a front-end application to detect any attacks that would come across any IoT devices.  
Conceptualization, S.S. and B.M.; Methodology, S.S. and B.M.; Software, B.M.; Validation, R.M. and P.P.; Formal analysis, R.M.; Investigation, R.M. and P.P.; Resources, B.M.; Writing--original draft, S.S. and B.M.; Writing--review & editing, S.S., R.M. and P.P.; Supervision, R.M. and P.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.