Dense_Deepsanet: Dense_Deep Stacked Autoencodernet-Based Intrusion Detection in Cloud Computing

V. Jaya Ramakrishna and K. Dayananda
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V. Jaya Ramakrishna: Department of Computer Science and Engineering, Kakatiya University, Warangal 506009, Telangana, India
K. Dayananda: ��Department of Computer Science, Kakatiya University, Warangal 506009, Telangana, India

Journal of Information & Knowledge Management (JIKM), 2025, vol. 24, issue 05, 1-26

Abstract: Cloud Computing (CC) has gained huge attention from both public and private organisations as it offers flexibility and pay-per-use-based services for users. The privacy and security of the users are affected due to the open and distributed nature of the cloud. The CC poses several risks due to the rise of intrusion attacks and recognising them is a crucial aspect in the internet world. Thus, the Intrusion Detection System (IDS) is the most widely used technique for detecting attacks in the cloud. Hence, this research proposes Dense_Deep Stacked AutoencoderNet (Dense_DeepSANet) for detecting intrusion. Initially, the cloud simulation is done and from the specified dataset, the acquisition of input data is made. Subsequently, the z-score normalisation is used for normalising the data in the pre-processing step. Additionally, feature fusion is performed using Radial Basis Function Networks (RBF) with Lorentzian similarity to merge similar data. After that, the oversampling method concerning Synthetic Minority Over-sampling Technique (SMOTE) is used for augmenting the data to avoid overfitting. Finally, intrusion detection is done based on Dense_DeepSANet. Moreover, Dense_DeepSANet is modelled by combining the Deep Stacked Autoencoder (DSA) and Dense Convolutional Network (DenseNet). The datasets used for testing the devised approach are the ISCX NSL-KDD dataset, KDD Cup 1999 dataset and Cloud Intrusion Detection Dataset (CIDD). The experimental result shows that Dense_DeepSANet achieved superior results in terms of offering accuracy, Precision, Recall and F 1-score with values of 90.60%, 91.40%, 92.30% and 91.90% for the KDD Cup 1999 dataset.

Keywords: Cloud computing (CC); deep stacked autoencoder (DSA); synthetic minority over-sampling technique (SMOTE); dense convolutional network (DenseNet); deep learning (DL) (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0219649225500583

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