International Journal on Science and Technology

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Deep learning based network attack classification

Author(s) Nagarjuna Reddy.G, Dinesh.V, Pavan Kumar.K, Jeeva.V, Kiruba Devi.T
Country India
Abstract The most common security threads to internet service is distributed denial of service also known as DDoS attack. This DDoS can be easily be launched in pc,cloud service providers and many more that suddenly spikes in traffic or websites slowdown. There are traditional DDoS attack detection are present which uses ML based algorithms gives accuracy but if the dataset consists of large volume of data the traditional algorithms find difficult to detect. Since Deep learning is widely used in present days, which is powerful learning and has extraction capabilities. In this paper we used LSTM which is a type of Recurrent Neural Network (RNN) which are Deep Learning based algorithms. In this paper WSN-DS.cv dataset is used, the data set consists of wireless sensor network classifications. Here we train the raw data by preprocessing of data like feature extraction, identifying the null values, analysing the data and splitting them for train and test. Here we epoch the dataset for 10 times to get better accuracy
Keywords Deep learning, LSTM, RNN, DDoS, CNN, IoT
Field Engineering
Published In Volume 16, Issue 1, January-March 2025
Published On 2025-03-22
Cite This Deep learning based network attack classification - Nagarjuna Reddy.G, Dinesh.V, Pavan Kumar.K, Jeeva.V, Kiruba Devi.T - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2533
DOI https://doi.org/10.71097/IJSAT.v16.i1.2533
Short DOI https://doi.org/g892g4
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