With the fast growth and utilization of artificial intelligence (AI) models, the Industrial Internet of Things (IIoT) has remarkably progressed in locating industrial communication and enhancing industrial methods qui...
详细信息
With the fast growth and utilization of artificial intelligence (AI) models, the Industrial Internet of Things (IIoT) has remarkably progressed in locating industrial communication and enhancing industrial methods quickly. In Industry 5.0, the hyper-automation method is a technical trend that navigates manufacturing objects to intellectual devices of IIoT, smart, agile software, cloud computing, smart robotics, and embedded modules by reliability and higher intricacy. Machine learning (ML) and deep learning (DL) methods were established for identifying anomalies by understanding the usual behaviour methods of the cyber threat attack and identifying and detecting deviations. This study proposes a Feature Enhancement Model with a White Shark Optimizer-based Cyber Threat Attack Detection and Classification (FEWSO-CTADC) technique on an Imbalanced Dataset in an IIoT environment. The primary focus of the FEWSO-CTADC technique is to enhance the automatic classification and detection of cyber threats in the IIoT environment. Initially, the FEWSO-CTADC technique implements a data preprocessing model to scale the raw information into a uniform format. Next, the SMOTE technique is used to manage the imbalanced class distribution in the attack recognition database. Moreover, the WSO-based feature subset selection is accomplished to select the superior set of features. Finally, the FEWSO-CTADC method utilizes the stacked auto-encoder (SAE) method for attack classification and recognition. Extensive experiments were conducted to demonstrate the improved performance of the FEWSO-CTADC approach, and the results were compared across various methods. The performance validation of the FEWSO-CTADC approach exhibited a superior value of 99.20 % over recent techniques under diverse metrics.
This paper demonstrates methods for creating a server application with the task of generating a plausible family tree based on the user. The application does not try to be factual, merely plausible. In creating this a...
详细信息
Emotions are an omnipresent and important factor in the interaction and communication between people. Since emotions are an indispensable part of human life, it would accelerate the progress of artificial intelligence...
详细信息
The paper presents a semi-automatic method for the construction of derivational networks. The proposed approach applies a sequential pattern mining technique in order to construct useful morphological features in an u...
详细信息
In this paper, a cooperative spectrum sharing scheme is proposed for NOMA based cognitive radio networks comprising of primary and secondary networks. The primary network consists of a primary transmitter PT communica...
详细信息
As big data,its technologies,and application continue to advance,the Smart Grid(SG)has become one of the most successful pervasive and fixed computing platforms that efficiently uses a data-driven approach and employs...
详细信息
As big data,its technologies,and application continue to advance,the Smart Grid(SG)has become one of the most successful pervasive and fixed computing platforms that efficiently uses a data-driven approach and employs efficient information and communication technology(ICT)and cloud *** a result of the complicated architecture of cloud computing,the distinctive working of advanced metering infrastructures(AMI),and the use of sensitive data,it has become challenging tomake the SG *** of the SG are categorized into two main categories,Technical Losses(TLs)and Non-Technical Losses(NTLs).Hardware failure,communication issues,ohmic losses,and energy burnout during transmission and propagation of energy are ***’s are human-induced errors for malicious purposes such as attacking sensitive data and electricity theft,along with tampering with AMI for bill reduction by fraudulent *** research proposes a data-driven methodology based on principles of computational intelligence as well as big data analysis to identify fraudulent customers based on their load *** our proposed methodology,a hybrid Genetic Algorithm and Support Vector Machine(GA-SVM)model has been used to extract the relevant subset of feature data from a large and unsupervised public smart grid project dataset in London,UK,for theft detection.A subset of 26 out of 71 features is obtained with a classification accuracy of 96.6%,compared to studies conducted on small and limited datasets.
This study seeks to enhance academic integrity by providing tools to detect AI-generated content in student work using advanced technologies. The findings promote transparency and accountability, helping educators mai...
详细信息
The current work examines the theoretical aspects of a very sensitive surface plasmon resonance sensor for detecting the chikungunya virus. Silver and zinc selenide are the only material layers comprising the sensing ...
详细信息
Skim Pinjaman Buku Teks (SPBT) is a textbook loan program for elementary and secondary schools in Malaysia. This program is an initiative system by the Malaysia Ministry of Education to help students in the school by ...
详细信息
The valley Hall effect (VHE) holds great promise for valleytronic applications by leveraging the valley degree of freedom. To date, research on VHE has focused on its linear response to an applied current, leaving non...
详细信息
暂无评论