2 research outputs found
Prevention of cyberattacks in WSN and packet drop by CI framework and information processing protocol using AI and Big Data
As the reliance on wireless sensor networks (WSNs) rises in numerous sectors,
cyberattack prevention and data transmission integrity become essential
problems. This study provides a complete framework to handle these difficulties
by integrating a cognitive intelligence (CI) framework, an information
processing protocol, and sophisticated artificial intelligence (AI) and big
data analytics approaches. The CI architecture is intended to improve WSN
security by dynamically reacting to an evolving threat scenario. It employs
artificial intelligence algorithms to continuously monitor and analyze network
behavior, identifying and mitigating any intrusions in real time. Anomaly
detection algorithms are also included in the framework to identify packet drop
instances caused by attacks or network congestion. To support the CI
architecture, an information processing protocol focusing on efficient and
secure data transfer within the WSN is introduced. To protect data integrity
and prevent unwanted access, this protocol includes encryption and
authentication techniques. Furthermore, it enhances the routing process with
the use of AI and big data approaches, providing reliable and timely packet
delivery. Extensive simulations and tests are carried out to assess the
efficiency of the suggested framework. The findings show that it is capable of
detecting and preventing several forms of assaults, including as
denial-of-service (DoS) attacks, node compromise, and data tampering.
Furthermore, the framework is highly resilient to packet drop occurrences,
which improves the WSN's overall reliability and performanc
Undecimated Wavelet Transform for Word Embedded Semantic Marginal Autoencoder in Security improvement and Denoising different Languages
By combining the undecimated wavelet transform within a Word Embedded
Semantic Marginal Autoencoder (WESMA), this research study provides a novel
strategy for improving security measures and denoising multiple languages. The
incorporation of these strategies is intended to address the issues of
robustness, privacy, and multilingualism in data processing applications. The
undecimated wavelet transform is used as a feature extraction tool to identify
prominent language patterns and structural qualities in the input data. The
proposed system may successfully capture significant information while
preserving the temporal and geographical links within the data by employing
this transform. This improves security measures by increasing the system's
ability to detect abnormalities, discover hidden patterns, and distinguish
between legitimate content and dangerous threats. The Word Embedded Semantic
Marginal Autoencoder also functions as an intelligent framework for
dimensionality and noise reduction. The autoencoder effectively learns the
underlying semantics of the data and reduces noise components by exploiting
word embeddings and semantic context. As a result, data quality and accuracy
are increased in following processing stages. The suggested methodology is
tested using a diversified dataset that includes several languages and security
scenarios. The experimental results show that the proposed approach is
effective in attaining security enhancement and denoising capabilities across
multiple languages. The system is strong in dealing with linguistic variances,
producing consistent outcomes regardless of the language used. Furthermore,
incorporating the undecimated wavelet transform considerably improves the
system's ability to efficiently address complex security concern