# A Lightweight Model for DDoS Attack Detection Using Machine Learning Techniques
## 2 Literature Survey
FlowGuard is made up of two main parts: a flow handler and a flow filter. The latter analyzes suspicious flows for DDoS attack identification and classification and conducts filtration rule generation using a self-evolving machine. The former maintains the flow filtration rules generated by the handler and is responsible for DDoS attack detection. The CICDDoS2019 dataset was utilized. The proposed model using LSTM acquired an accuracy of 98.9% and with CNN it obtained 99.9%. The goal of this research was to concentrate on identifying DDoS attacks launched via IoT-hot-infected devices. First, 33 different types of scans and 60 different types of DDoS attacks were generated to create a generic scanning and DDoS attack dataset [9]. The author then suggested a two-pronged machine learning strategy to stop and identify IoT botnet attacks. In order to stop IoT botnet attacks, the author created a cutting-edge deep learning model, ResNet-18, to detect scanning activity in the early stages of an attack while training a second ResNet-18 model for DDoS attack identification to recognize IoT botnet attacks in the second fold. For the dataset CICIDS-2019, BOT-IOT was utilized. It is a logistic regression algorithm. In comparison to the existing trained models, the experimental findings show that the suggested two-fold strategy may effectively prevent and detect botnet attacks. However, if the scanning detection model is unable to stop a botnet attack, the ResNetDDoS-1 model will be used to detect a DDoS attack. In order to prevent and identify IoT botnet assaults, the suggested two-fold technique exhibits 98.89% accuracy, 99.01% precision, 98.74% recall, and 98.87% F1 score. In this study, a range of machine learning (ML) techniques with built in WEKA tools are used to investigate the detection performance for DDoS assaults [10] using the most recent CICDDoS2019 datasets. CICDDoS2019 was judged to be the model that gave the best outcomes. K-NN, SVM, NB, DT, RF, and LR are the six different kinds of machine learning (ML) algorithms that were used in this study. In the evaluation presented, the DT and RF algorithms generated the best results, with accuracy results of 99% and 99%, respectively. However, because of the DT's quicker computation time (4.53 s as opposed to 84.2 s), it outperforms the RF. Unresolved issues are then presented for potential future investigation. In order to guarantee security from DoS and DDoS attacks, a number of ideas have been put forth in this article. Algorithms for Deep Learning and Machine Learning have both been used to assess DoS and DDoS attacks [11]. For training, the UNSW Canberra Cyber Center's BOT-IOT dataset was utilized. For training and testing purposes, algorithms for machine learning used random forest, KNN, and decision trees with logistic regression as the meta-classifier. As a consequence, the best attack classification accuracies for deep learning and machine learning algorithms, respectively, are 99.5% and 99.9%. The purpose of this research is to offer a novel machine learning technique for the detection and mitigation of botnet-based DDoS attacks on IoT networks. The suggested model addresses the security concern regarding the dangers posed by bots [12]. A model was developed using a variety of machine learning methods, including KNN, NB model, and MLP ANN, with data from the BOT-IOT dataset as the training set. Based on the accuracy, ROC, and AUC scores, a reference point chose the best algorithm. SMOTE and feature engineering were integrated with ML algorithms. The performance of three algorithms was evaluated on both the class-balanced dataset and the class-imbalanced dataset. Notably, naive bayes mode achieved the highest accuracy of 99.4%. In this study, the objective is to present a feature engineering and machine learning framework for the IoT-CIDDS dataset to detect DDoS attacks [13].