Unlike other typical clustering analysis, which considers column only, biclustering analysis processes a matrix into sub-matrices based on rows and columns simultaneously. One method of bicluster analysis uses the probabilistic model, like the plaid model, that provides overlapping bicluster. The plaid model calculates the value of an element given from a particular sub-matrix for each cell; thus, the value can be seen as the number of contributions of a particular bicluster. The algorithm begins with preparing the input data as a matrix, then an initial model is assessed and makes a residual matrix from the model. After that, we determine bicluster candidates, which are evaluated for its effect parameters and bicluster membership parameters. Finally, the bicluster candidate is pruned to give the optimal bicluster. We implemented the algorithm on gene expression dataset of colon cancer, where the rows and columns contain observations and types of genes, respectively. We carried out in six distinct scenarios in which each scenario uses different model parameters and threshold values. We measured the results using Jaccard index and coherence variance. Our experiments show that biclustering analysis on a model with mean, row, and column effects of colon cancer data output low coherence variance.
The rise of interconnected devices through wireless networks provides two sides consequences. On one side, it helps many human tasks; on the other hand, the prone wireless medium opens the vulnerable system to be exploited by adversaries. An Intrusion Detection System (IDS) is one method to inspect the network traffic by leveraging state-of-the-art anomaly detection techniques. Deep learning models have been utilized to distinguish the benign and malicious traffic. However, projecting the tabular data into images before the image classification has been the main challenge of leveraging deep learning for IDS purposes. We propose the novel projection of tabular data into 2-coded color mapping for IDS purposes. The proposed method employs a feature selection method to ensure optimal dimensionality. We examined the different number of attribute subsets to obtain the relationship between the attributes. Furthermore, it takes advantage of the Convolutional Neural Network (CNN) model to classify the Wi-Fi attacks. We evaluate the proposed model using the most common Wi-Fi attacks dataset, Aegean Wi-Fi Intrusion Dataset (AWID2). The proposed method achieved an F1 score of 99.73% and a false positive rate of 0.24%. This study highlights the importance of addressing the mapping procedures from tabular data into grid-based data before deep learning training and validates the effectiveness of CNN to detect multiple types of wireless network attacks.
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