The imbalance of ECG signal data and the complexity of labeling pose significant challenges for deep learning-based anomaly detection. Traditional contrastive learning approaches for ECG anomaly detection often rely o...
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The project investigates the integration of YOLO v9, the latest iteration of the 'You Only Look Once' model, into robotic fingers to enhance their precision, responsiveness, and versatility in object handling....
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Lung and colon cancer are among the most frequent cancers that claim lives around the globe. For people to heal, early detection and assistance are crucial. Physicians diagnose patients using histopathological images ...
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In this work, we address the strategic placement and optimal sizing of electric vehicle charging stations for cities as well as highway traffic to minimize overall cost. We formulate the problem as a Mixed Integer Lin...
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Human Activity Recognition (HAR) is a trading area in computer vision and deep learning. However, boosting the performance of deep learning models often necessitates increasing their size or capacity, which raises com...
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Motion retargeting from videos to 3D virtual character is a challenging task in computer vision and computer graphics. A solution is first to extract the 3D motion sequences from videos using human pose estimation alg...
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Effective task scheduling and resource allocation have become major problems as a result of the fast development of cloud computing as well as the rise of multi-cloud systems. To successfully handle these issues, we p...
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Generative adversarial networks (GANs) have gained popularity for their ability to synthesize images from random inputs in deep learning models. One of the notable applications of this technology is the creation of re...
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Glioblastoma is an aggressive type of brain cancer with a high mortality rate. Early and accurate glioblastoma detection is crucial for timely and effective treatment. Hyperspectral Imaging (HSI) has emerged as a prom...
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作者：
A.E.M.EljialyMohammed Yousuf UddinSultan AhmadDepartment of Information Systems
College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Science
College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabiaand also with University Center for Research and Development(UCRD)Department of Computer Science and EngineeringChandigarh UniversityPunjabIndia
Intrusion detection systems (IDSs) are deployed to detect anomalies in real time. They classify a network’s incoming traffic as benign or anomalous (attack). An efficient and robust IDS in software-defined networks i...
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Intrusion detection systems (IDSs) are deployed to detect anomalies in real time. They classify a network’s incoming traffic as benign or anomalous (attack). An efficient and robust IDS in software-defined networks is an inevitable component of network security. The main challenges of such an IDS are achieving zero or extremely low false positive rates and high detection rates. Internet of Things (IoT) networks run by using devices with minimal resources. This situation makes deploying traditional IDSs in IoT networks unfeasible. Machine learning (ML) techniques are extensively applied to build robust IDSs. Many researchers have utilized different ML methods and techniques to address the above challenges. The development of an efficient IDS starts with a good feature selection process to avoid overfitting the ML model. This work proposes a multiple feature selection process followed by classification. In this study, the Software-defined networking (SDN) dataset is used to train and test the proposed model. This model applies multiple feature selection techniques to select high-scoring features from a set of features. Highly relevant features for anomaly detection are selected on the basis of their scores to generate the candidate dataset. Multiple classification algorithms are applied to the candidate dataset to build models. The proposed model exhibits considerable improvement in the detection of attacks with high accuracy and low false positive rates, even with a few features selected.
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