In the internet era, network-based services and connected devices are growing with many users, thus it became an increase in the number of cyberattacks. Distributed Denial of Service (DDoS) attacks are the type of cyberattacks increasing their strength and impact on the victim. Effective detection of such attacks through a DDoS Detection System is relatively essential research. Although machine learning techniques have grown in popularity in the field of cybersecurity over the last several years, the change in the attack patterns in recent days shows the need for developing a robust DDoS prediction model. Therefore, we suggested a DDoS prediction system using a two-stage hybrid methodology. Initially, features are extracted by the unsupervised Deep Sparse Autoencoder (DSAE) using Elastic Net regularisation with optimum hyperparameters. Further, several learning models are tuned to classify attacks based on the extracted feature sets. Finally, the models’ performance is analysed with extracted features in balanced and imbalanced data scenarios. The experimental outcomes show that the suggested model outperforms current approaches. The model was evaluated on the CICIDS-2017 and CICDDoS-2019 datasets and achieved an accuracy of 99.98% and 99.99%, respectively.
IntroductionBrain tumors arise due to abnormal growth of cells at any brain location with uneven boundaries and shapes. Usually, they proliferate rapidly, and their size increases by approximately 1.4% a day, resulting in invisible illness and psychological and behavioral changes in the human body. It is one of the leading causes of the increase in the mortality rate of adults worldwide. Therefore, early prediction of brain tumors is crucial in saving a patient’s life. In addition, selecting a suitable imaging sequence also plays a significant role in treating brain tumors. Among available techniques, the magnetic resonance (MR) imaging modality is widely used due to its noninvasive nature and ability to represent the inherent details of brain tissue. Several computer-assisted diagnosis (CAD) approaches have recently been developed based on these observations. However, there is scope for improvement due to tumor characteristics and image noise variations. Hence, it is essential to establish a new paradigm.MethodsThis paper attempts to develop a new medical decision-support system for detecting and differentiating brain tumors from MR images. In the implemented approach, initially, we improve the contrast and brightness using the tuned single-scale retinex (TSSR) approach. Then, we extract the infected tumor region(s) using maximum entropy-based thresholding and morphological operations. Furthermore, we obtain the relevant texture features based on the non-local binary pattern (NLBP) feature descriptor. Finally, the extracted features are subjected to a support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), and GentleBoost (GB).ResultsThe presented CAD model achieved 99.75% classification accuracy with 5-fold cross-validation and a 91.88% dice similarity score, which is higher than the existing models.DiscussionsBy analyzing the experimental outcomes, we conclude that our method can be used as a supportive clinical tool for physicians during the diagnosis of brain tumors.
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