DDoSLSTM: Detection of Distributed Denial of Service Attacks on IoT Devices using LSTM Model

Vimal Gaur, Rajneesh Kumar

20222022 International Conference on Communication, Computing and Internet of Things (IC3IoT)18 citationsDOI

Abstract

Distributed Denial of Service (DDoS) attack is a persistent complication in the network's security. These attacks have been detected by many machine learning algorithms and feature selection methods. This paper chose the Recurrent Neural Network based long short-term memory model that works on time series data and handles long time-dependent inputs, thereby detecting DDoS attacks. In our paper, we focused primarily on increasing the classification performance of the LSTM model. Multi-layer LSTM model has been used for binary and multiclass data and maximum accuracy attained is 99.46% (1- Layer LSTM with Binary data) followed by 99.16% for 2-Layer LSTM with Multiclass Grouped data. The proposed DDoSLSTM model outperforms other state-of-the-art techniques, including deep neural network (DNN), RNN, CNN, Transformers.

Topics & Concepts

Denial-of-service attackComputer scienceLong short term memoryRecurrent neural networkArtificial intelligenceArtificial neural networkDeep learningMachine learningBinary numberFeature selectionData miningThe InternetOperating systemMathematicsArithmeticNetwork Security and Intrusion DetectionInternet Traffic Analysis and Secure E-votingAnomaly Detection Techniques and Applications