Deep Learning Inspired Intelligent Framework to Ensure Effective Intrution Detection in Cloud
Keywords:
Cloud Forensics, Data Generation, Cloud Computing, Artificial Intelligence (AI), Principal Component Analysis (PCA), Singular Value Decomposition (SVD)Abstract
In the context of cloud forensics, the exponential growth in data generation due to the widespread use of cloud computing has created substantial issues for protecting the safety and privacy of cloud-based infrastructure. Many security solutions, such as intrusion detection systems (IDS), based on artificial intelligence (AI) have been created to deal with these threats. Intrusion detection systems (IDS) are a paradigm for assessing the safety of network traffic in the cloud. This research uses principle component analysis (PCA) and singular value decomposition (SVD) to minimize the dimensionality of the feature space, which in turn improves the accuracy of IDS in cloud forensics. In order to evaluate the efficacy of these techniques, they are applied to the UNSW-NB15 data set, which serves as a standard for IDS testing in cloud forensics. Our results show that Classifier, an algorithm based on deep learning, is superior to other approaches when it comes to detecting fraudulent data streams in cloud-based networks. This provides support for the hypothesis that integrating PCA, SVD, and deep learning techniques into cloud forensics analysis might provide fruitful results. To guard against cyber threats and guarantee the dependability and security of cloud-based systems, this work adds to the continuing efforts to enhance the accuracy and efficiency of IDS systems in cloud forensics.
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