# A Lightweight Model for DDoS Attack Detection Using Machine Learning Techniques
## 2 Literature Survey  
To detect and categorize hostile network traffic inside an IoT network, a cutting-edge, DL-based DDoS detection system is proposed in this research [4] to defend the IoT network against new DDoS assaults. This research suggests a multi-classification method for DDoS attacks that is effective, reliable, and scalable. The suggested approach uses a Cu-LSTM-aided framework with GPU support to detect complex multi-vector DDoS attacks in IoTs using the CICDDoS2019 dataset. Cu-enabled LSTM is the employed algorithm. The suggested model showed excellent accuracy in identifying DoS assaults. However, just a small number of folds were employed for cross-validation to achieve the highest level of multi-class classification accuracy. The classifier claimed a detection accuracy of above 99.6% for DDoS threats in the multi-class characterization. The purpose of this research is to suggest a methodology for identifying unusual DDoS attacks, using the Mirai dataset [5], and to test two well-known DDoS assaults, using the standard dataset. To normalize and clean data and extract valuable features, two methods are implemented. A framework is created for identifying and evaluating anomalous behavior using common machine learning techniques. Linear SVM, neural networks, and decision tree algorithms are employed. According to the experimental findings, a merge of random forest and decision tree was able to detect attacks with an accuracy of 99.7%. The worst-performing algorithm is linear SVM. The purpose of this research is to offer an IDS for the detection of DDoS attacks that combines deep learning with multi-objective optimization [6]. In order to minimize the dimensionality of the data and pick features, these normalized data are fed into the jumping gene-adapted nondominated sorting genetic algorithm. This method was suggested in the earlier research, which has been submitted for publication. For the NSGA II-aJG algorithm implementation, the author considered the six most crucial goals, which are covered in more depth in the subsection. The deep learning algorithm-based model receives the reduced data that were output from the preceding stage as input data, using a sigmoid function with binary cross-entropy. CISIDS2017 datasets on DDoS were utilized as the datasets. MLP, SVM, bayes, random forest, and genetic algorithm (NSGA-II-aJG) were the algorithms employed. The proposed approach (NSGA-II-aJG) has attained an F1 score value of 99.36% and an accuracy of 99.03%. The purpose of this article is to offer cutting-edge cyber security solutions to IoT devices and apps for smart cities. This research suggests a botnet detection system that uses network traffic flows and is DL-based [7]. In order to identify assaults coming from infected IoT devices, the botnet detection framework gathers network traffic flows, turns them into connection records, and then utilizes a DL model. Numerous tests are run on well-known and freshly released benchmark datasets in order to identify the best DL model. The datasets are displayed in order to comprehend their features. BASHLITE and Mirai botnet datasets were used. Logistic regression, NB, KNN, DT, RF, RSVM, and linear SVM were the algorithms employed. The SVM models, coupled with KNN and RF models, provided accuracy results of 100%. The KNN and RF models together provided an accuracy of 99%. However, it is noted that the SVM models, when compared to all other algorithms, are computationally expensive and take a long time to learn all the many patterns that exist in datasets during training. This study focuses on IoT DDoS defense strategies and proposes FlowGuard, an edge-centric IoT defensive system, for detecting, identifying, classifying, and mitigating IoT DDoS attacks [8]. In this study, a DDoS attack detection, identification, classification, and mitigation strategy called FlowGuard was suggested. It makes use of two machine learning approaches that work together: long short-term memory (LSTM) and convolutional neural network (CNN).