Ftdho Zfnet: Block chain based Fractional Tasmanian Devil Harris optimization enabled deep learning using attack detection and mitigation

Computer Software and Media Applications, Journal Year: 2024, Volume and Issue: 7(1), P. 5224 - 5224

Published: June 4, 2024

Block chain technology is regarded for enhancing the characteristics of security because decentralized design, safe distributed storage, and privacy. However, in recent times present situation block has experienced some crisis that may delay quick acceptance utilization real-time applications. To conquer this subdues, a blockchain based system attack detection mitigation with Deep Learning (DL) named Fractional Tasmanian Devil Harris Optimization_Zeiler Fergus network (FTDHO_ZFNet) introduced. In investigation, entities utilized are owner, chain, server, trusted authority user. Here, authentication phase done by means Ethereum Key Exchange module privacy preserved data sharing communication also done. Then, recorded log file creation executed below mentioned stages. At first, generated basis to record events. After wards, features extracted BoT-IoT database. feature fusion overlap coefficient utilizing Q-Network (DQN). Moreover, augmentation (DA) doneusing bootstrapping method. last, observed ZFNet tuned FTDHO. FTDHO unified Optimization (FTDO) Hawks (HHO). Additionally, FTDO integrated Calculus (FC) concept devil optimization (TDO). Furthermore, performed. The performance measures applied FTDHO_ZFNet accuracy, True Negative rate (TNR), supreme values 92.9%, 93.8% 92.9%.

Language: Английский

Smart Healthcare Based Cyber Physical System Modeling by Block Chain with Cloud 6G Network and Machine Learning Techniques

Wireless Personal Communications, Journal Year: 2024, Volume and Issue: unknown

Published: June 17, 2024

Language: Английский

FL-DSFA: Securing RPL-Based IoT Networks against Selective Forwarding Attacks Using Federated Learning

Sensors, Journal Year: 2024, Volume and Issue: 24(17), P. 5834 - 5834

Published: Sept. 8, 2024

The Internet of Things (IoT) is a significant technological advancement that allows for seamless device integration and data flow. development the IoT has led to emergence several solutions in various sectors. However, rapid popularization also its challenges, one most serious challenges security IoT. Security major concern, particularly routing attacks core network, which may cause severe damage due information loss. Routing Protocol Low-Power Lossy Networks (RPL), protocol used devices, faced with selective forwarding attacks. In this paper, we present federated learning-based detection technique detecting attacks, termed FL-DSFA. A lightweight model involving Attack Dataset (IRAD), comprises Hello Flood (HF), Decreased Rank (DR), Version Number (VN), increase efficiency. on threaten system since they mainly focus essential elements RPL. components include control messages, topologies, repair procedures, resources within sensor networks. Binary classification approaches have been assess training efficiency proposed model. step includes implementation machine learning algorithms, including logistic regression (LR), K-nearest neighbors (KNN), support vector (SVM), naive Bayes (NB). comparative analysis illustrates study, SVM KNN classifiers, exhibits highest accuracy during achieves efficient runtime performance. demonstrates exceptional performance, achieving prediction precision 97.50%, an 95%, recall rate 98.33%, F1 score 97.01%. It outperforms current leading research field, results, scalability, enhanced privacy.

Language: Английский