7 research outputs found
TRNG Based Key Generation for Certificateless Signcryption
Abstract -Signcryption is a cryptographic primitive that fulfills both the functions of digital signature and public key encryption simultaneously in low cost compare with the traditional signature-then-encryption approach. In this paper, we propose a new method for providing more secure key generation against the outer and insider attack, which is based on the mouse movements. Tag KEM process is implemented using this True random number generator (TRNG) and it generates most secure and nondeterministic secret keys for data encryption which held in the Data encapsulation mechanism (DEM). We compare the statistical reports of the proposed system with the previous methods which is used to implements TKEM based on pseudo random number generators, and it works better in manner of security
Leveraging Semi-Supervised Graph Learning for Enhanced Diabetic Retinopathy Detection
Diabetic Retinopathy (DR) is a significant cause of blindness globally,
highlighting the urgent need for early detection and effective treatment.
Recent advancements in Machine Learning (ML) techniques have shown promise in
DR detection, but the availability of labeled data often limits their
performance. This research proposes a novel Semi-Supervised Graph Learning SSGL
algorithm tailored for DR detection, which capitalizes on the relationships
between labelled and unlabeled data to enhance accuracy. The work begins by
investigating data augmentation and preprocessing techniques to address the
challenges of image quality and feature variations. Techniques such as image
cropping, resizing, contrast adjustment, normalization, and data augmentation
are explored to optimize feature extraction and improve the overall quality of
retinal images. Moreover, apart from detection and diagnosis, this work delves
into applying ML algorithms for predicting the risk of developing DR or the
likelihood of disease progression. Personalized risk scores for individual
patients are generated using comprehensive patient data encompassing
demographic information, medical history, and retinal images. The proposed
Semi-Supervised Graph learning algorithm is rigorously evaluated on two
publicly available datasets and is benchmarked against existing methods.
Results indicate significant improvements in classification accuracy,
specificity, and sensitivity while demonstrating robustness against noise and
outlie rs.Notably, the proposed algorithm addresses the challenge of imbalanced
datasets, common in medical image analysis, further enhancing its practical
applicability.Comment: 13 pages, 6 figure
Safe Routing Approach by Identifying and Subsequently Eliminating the Attacks in MANET
Wireless networks that are decentralized and communicate without using
existing infrastructure are known as mobile ad-hoc networks. The most common
sorts of threats and attacks can affect MANETs. Therefore, it is advised to
utilize intrusion detection, which controls the system to detect additional
security issues. Monitoring is essential to avoid attacks and provide extra
protection against unauthorized access. Although the current solutions have
been designed to defeat the attack nodes, they still require additional
hardware, have considerable delivery delays, do not offer high throughput or
packet delivery ratios, or do not do so without using more energy. The
capability of a mobile node to forward packets, which is dependent on the
platform's life quality, may be impacted by the absence of the network node
power source. We developed the Safe Routing Approach (SRA), which uses
behaviour analysis to track and monitor attackers who discard packets during
the route discovery process. The attacking node recognition system is made for
irregular routing node detection to protect the controller network's usual
properties from becoming recognized as an attack node. The suggested method
examines the nearby attack nodes and conceals the trusted node in the routing
pathway. The path is instantly assigned after the initial discovery of trust
nodes based on each node's strength value. It extends the network's life span
and reduces packet loss. In terms of Packet Delivery Ratio (PDR), energy
consumption, network performance, and detection of attack nodes, the suggested
approach is contrasted with AIS, ZIDS, and Improved AODV. The findings
demonstrate that the recommended strategy performs superior in terms of PDR,
residual energy, and network throughput
A Neural Radiance Field-Based Architecture for Intelligent Multilayered View Synthesis
A mobile ad hoc network is made up of a number of wireless portable nodes that spontaneously come together en route for establish a transitory network with no need for any central management. A mobile ad hoc network (MANET) is made up of a sizable and reasonably dense community of mobile nodes that travel across any terrain and rely solely on wireless interfaces for communication, not on any well before centralized management. Furthermore, routing be supposed to offer a method for instantly delivering data across a network between any two nodes. Finding the best packet routing from across infrastructure is the major issue, though. The proposed protocol's major goal is to identify the least-expensive nominal capacity acquisition that assures the transportation of realistic transport that ensures its durability in the event of any node failure. This study suggests the Optimized Route Selection via Red Imported Fire Ants (RIFA) Strategy as a way to improve on-demand source routing systems. Predicting Route Failure and energy Utilization is used to pick the path during the routing phase. Proposed work assess the results of the comparisons based on performance parameters like as energy usage, packet delivery rate (PDR), and end-to-end (E2E) delay. The outcome demonstrates that the proposed strategy is preferable and increases network lifetime while lowering node energy consumption and typical E2E delay under the majority of network performance measures and factors
Outsourced Analysis of Encrypted Graphs in the Cloud with Privacy Protection
Huge diagrams have unique properties for organizations and research, such as
client linkages in informal organizations and customer evaluation lattices in
social channels. They necessitate a lot of financial assets to maintain because
they are large and frequently continue to expand. Owners of large diagrams may
need to use cloud resources due to the extensive arrangement of open cloud
resources to increase capacity and computation flexibility. However, the
cloud's accountability and protection of schematics have become a significant
issue. In this study, we consider calculations for security savings for
essential graph examination practices: schematic extraterrestrial examination
for outsourcing graphs in the cloud server. We create the security-protecting
variants of the two proposed Eigen decay computations. They are using two
cryptographic algorithms: additional substance homomorphic encryption (ASHE)
strategies and some degree homomorphic encryption (SDHE) methods. Inadequate
networks also feature a distinctively confidential info adaptation convention
to allow the trade-off between secrecy and data sparseness. Both dense and
sparse structures are investigated. According to test results, calculations
with sparse encoding can drastically reduce information. SDHE-based strategies
have reduced computing time, while ASHE-based methods have reduced stockpiling
expenses