Attack Detection Using a Lightweight Blockchain Based Elliptic Curve Digital Signature Algorithm in Cyber Systems

Vatambeti, Ramesh; Divya, Nadella Sree; Jalla, Hanumantha Rao; Gopalachari, Mukkamula Venu

doi:10.18280/ijsse.120611

Cited by 9 publications

(7 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Vasa previa can be detected on ultrasound using a variety of techniques, including transvaginal scanning, placental cord insertion, color Doppler applied across the cervix, and three-dimensional (3D) ultrasonography [19,20,21]. When the placenta implants itself into the myometrium lining the uterine wall, a process called accretion occurs.…”

Section: Using Ultrasonography To Diagnose Placental Problemsmentioning

confidence: 99%

Investigating the abnormalities of deep learning with customized architecture using deep learning 4J

Madhu,

Nethra Betgeri,

Pavan

et al. 2024

MATEC Web Conf.

View full text Add to dashboard Cite

You In most cases, doctors and the use of image processing tools can assess the placenta—the actual site of physical birth—during pregnancy. Models built using Machine Learning produce performance metrics such as Accuracy, ROC, Precision, Recall, and F-Measure, which quantify this support. This research makes use of the tailored strategy built into the Weka tool, namely the DeepLearning4j package.Using common architectures such as LeNet, VGGnet, ResNet, and Alexnet yields results that are comparable. By iterating over the input layer's Loss functions and the output layer's errors, DeepLearning4j evaluates and optimizes the suggested architecture based on its quality. The experimental results show that out of seven distinct loss functions, the one with the abbreviation "MCXENT" (meaning "Multi-class Cross Entropy") produces the most accurate and least error-prone results. Additionally, a maximum accuracy of 95.7% is obtained, which is considered the best performance. These findings lend credence to an additional machine learning strategy that employs an interactive development tool for gynecologists and offer fresh assurance in its efficacy.

show abstract

Section: Using Ultrasonography To Diagnose Placental Problemsmentioning

confidence: 99%

Investigating the abnormalities of deep learning with customized architecture using deep learning 4J

Madhu,

Nethra Betgeri,

Pavan

et al. 2024

MATEC Web Conf.

View full text Add to dashboard Cite

show abstract

“…The design and development of lightweight blockchain technology to secure data communication in IoT networks requires various factors to ensure scalability, security, and data privacy. To design a lightweight blockchain to handle many IoT devices and transactions, in addition to lessening the burden on IoT devices, we propose a lightweight cryptographic algorithm to secure data transmission and implement a robust key management process [27,28].…”

Section: Design Considerationmentioning

confidence: 99%

A Lightweight Blockchain to Secure Data Communication in IoT Network on Healthcare System

Rangappa,

Manu,

Kariyappa

et al. 2023

IJSSE

View full text Add to dashboard Cite

The burgeoning domain of the Internet of Things (IoT) encompasses a myriad of interconnected devices tasked with the automated collection of sensitive data. A paramount challenge within this realm is the establishment of stringent security protocols to safeguard sensitive information and thwart unauthorized access. Although various strategies have been conceived and implemented to fortify data protection in IoT environments, the unique resource limitations intrinsic to IoT devices necessitate further exploration. The criticality of efficient time and memory management for the optimization of IoT application performance cannot be overstated. This paper elucidates the efficacy of employing lightweight blockchain technology as a bulwark to secure numerous IoT applications. It introduces a symmetric cryptographic algorithm, known as Blowfish, tailored for the secure transmission of data within IoT networks. A novel key generation phase has been developed, demonstrating an adept utilization of time and memory resources on IoT devices for the encryption of transaction data. Furthermore, these transactions are recorded within a blockchain database, capitalizing on its inherent immutability. Comparative analysis reveals that the proposed scheme surpasses contemporary algorithms, including AES and 3DES, with regard to encryption time and memory overhead for key generation. This advancement heralds a significant stride in the quest to bolster IoT security.

show abstract

“…Due to variables such as the devices' limited resources, the network's dynamic topology, and the variety of attack vectors, detecting assaults on the routing scheme of IIoT strategies can be challenging [6]. Recent years have seen the development of a number of methods for dealing with this problem, machine learning-based approaches.…”

Section: Introductionmentioning

confidence: 99%

Routing Attack Detection Using Ensemble Deep Learning Model for IIoT

Vatambeti¹,

Mamidisetti²

2023

IDA

View full text Add to dashboard Cite

Smart cities, ITS, supply chains, and smart industries may all be developed with minimal human interaction thanks to the increasing prevalence of automation enabled by machine-type communication (MTC). Yet, MTC has substantial security difficulties because of diverse data, public network access, and an insufficient security mechanism. In this study, we develop a novel IIOT attack detection basis by joining the following four main steps: (a) data collection, (b) pre-processing, (c) attack detection, and (d) optimisation for high classification accuracy. At the initial stage of processing, known as "pre-processing," the collected raw data (input) is normalised. Attack detection requires the creation of an intelligent security architecture for IIoT networks. In this work, we present a learning model that can recognise previously unrecognised attacks on an IIoT network without the use of a labelled training set. An IoT network intrusion detection system-generated labelled dataset. The study also introduces a hybrid optimisation algorithm for pinpointing the optimal LSTM weight when it comes to intrusion detection. When trained on the labelled dataset provided by the proposed method, the improved LSTM outperforms the other models with a finding accuracy of 95%, as exposed in the research.

show abstract

Attack Detection Using a Lightweight Blockchain Based Elliptic Curve Digital Signature Algorithm in Cyber Systems

Cited by 9 publications

References 35 publications

Investigating the abnormalities of deep learning with customized architecture using deep learning 4J

Investigating the abnormalities of deep learning with customized architecture using deep learning 4J

A Lightweight Blockchain to Secure Data Communication in IoT Network on Healthcare System

Routing Attack Detection Using Ensemble Deep Learning Model for IIoT

Contact Info

Product

Resources

About