Abstract
In this paper, a fog-based attack detection framework is proposed, where the DDoS attack is detected using multilayer perceptron deep learning model (MLPDL). The framework consists of a trained MLPDL model installed at the computation module of a fog node that predicts the end IoT device behavior. For selection of best prediction model at the fog layer, we compared the performance of MLPDL model with traditional logistic regression (LR) model using Python Anaconda platform by considering OBS_Network-DataSet_2_Aug27.arff dataset of DDoS attack. From the result, it is found that MLPDL model shows an accuracy of 93.54% and LR model shows an accuracy of 90.32%. Therefore, MLPDL model is used in this framework for predicting the behavior of the end IoT user. Afterward, in the simulation using socket programming, the performance of the proposed framework is evaluated using prediction accuracy and prediction computation time.
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Gudla, S.P.K., Bhoi, S.K. (2022). MLP Deep Learning-based DDoS Attack Detection Framework for Fog Computing. In: Rout, R.R., Ghosh, S.K., Jana, P.K., Tripathy, A.K., Sahoo, J.P., Li, KC. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 427. Springer, Singapore. https://doi.org/10.1007/978-981-19-1018-0_3
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