Intrusion Detection and Prevention Using Artificial Neural Network in Wireless Sensor Networks

Chandre, Pankaj R.; Mahalle, Parikshit N.; Shinde, Gitanjali Rahul

doi:10.1007/978-981-33-4073-2_12

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…It is predicated on fundamental aspects of quantum physics like the no-cloning theorem and the Heisenberg uncertainty principle. A major idea in quantum cryptography is Quantum major Distribution (QKD), which enables two remote parties to safely share a secret key that is generated randomly and is protected by the laws of quantum physics [4]. Due to the inherent characteristics of quantum states, quantum cryptography is secure and impervious to eavesdropping attempts.…”

Section: Quantum Cryptography Fundamentalsmentioning

confidence: 99%

Machine Learning-Enhanced Advancements in Quantum Cryptography: A Comprehensive Review and Future Prospects

Chandre,

Shendkar,

Deshmukh

et al. 2023

IJRITCC

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Quantum cryptography has emerged as a promising paradigm for secure communication, leveraging the fundamental principles of quantum mechanics to guarantee information confidentiality and integrity. In recent years, the field of quantum cryptography has witnessed remarkable advancements, and the integration of machine learning techniques has further accelerated its progress. This research paper presents a comprehensive review of the latest developments in quantum cryptography, with a specific focus on the utilization of machine learning algorithms to enhance its capabilities. The paper begins by providing an overview of the principles underlying quantum cryptography, such as quantum key distribution (QKD) and quantum secure direct communication (QSDC). Subsequently, it highlights the limitations of traditional quantum cryptographic schemes and introduces how machine learning approaches address these challenges, leading to improved performance and security. To illustrate the synergy between quantum cryptography and machine learning, several case studies are presented, showcasing successful applications of machine learning in optimizing key aspects of quantum cryptographic protocols. These applicatiocns encompass various tasks, including error correction, key rate optimization, protocol efficiency enhancement, and adaptive protocol selection. Furthermore, the paper delves into the potential risks and vulnerabilities introduced by integrating machine learning with quantum cryptography. The discussion revolves around adversarial attacks, model vulnerabilities, and potential countermeasures to bolster the robustness of machine learning-based quantum cryptographic systems. The future prospects of this combined field are also examined, highlighting potential avenues for further research and development. These include exploring novel machine learning architectures tailored for quantum cryptographic applications, investigating the interplay between quantum computing and machine learning in cryptographic protocols, and devising hybrid approaches that synergistically harness the strengths of both fields. In conclusion, this research paper emphasizes the significance of machine learning-enhanced advancements in quantum cryptography as a transformative force in securing future communication systems. The paper serves as a valuable resource for researchers, practitioners, and policymakers interested in understanding the state-of-the-art in this multidisciplinary domain and charting the course for its future advancements.

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Section: Quantum Cryptography Fundamentalsmentioning

confidence: 99%

Machine Learning-Enhanced Advancements in Quantum Cryptography: A Comprehensive Review and Future Prospects

Chandre,

Shendkar,

Deshmukh

et al. 2023

IJRITCC

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“…Where p & q are two real or complex functions. They convolute into another function (p*q) which is normally transformed version of one of the initial functions [27].…”

mentioning

confidence: 99%

ATiTHi: A Deep Learning Approach for Tourist Destination Classification using Hybrid Parametric Optimization

Tejaswini Bhosale

2023

IJRITCC

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A picture is best way to explore the tourist destination by visual content. The content-based image classification of tourist destinations makes it possible to understand the tourism liking by providing a more satisfactory tour. It also provides an important reference for tourist destination marketing. To enhance the competitiveness of the tourism market in India, this research proposes an innovative tourist spot identification mechanism by identifying the content of significant numbers of tourist photos using convolutional neural network (CNN) approach. It overcomes the limitations of manual approaches by recognizing visual information in photos. In this study, six thousand photos from different tourist destinations of India were identified and categorized into six major categories to form a new dataset of Indian Trajectory. This research employed Transfer learning (TF) strategies which help to obtain a good performance measure with very small dataset for image classification.VGG-16, VGG-19, MobileNetV2, InceptionV3, ResNet-50 and AlexNet CNN model with pretrained weight from ImageNet dataset was used for initialization and then an adapted classifier was used to classify tourist destination images from the newly prepared dataset. Hybrid hyperparameter optimization employ to find out hyperparameter for proposed Atithi model which lead to more efficient model in classification. To analyse and compare the performance of the models, known performance indicators were selected. As compared to the AlexNet model (0.83), MobileNetV2(0.93), VGG-19(0.918), InceptionV3(0.89), ResNet-50(0.852) the VGG16 model has performed the best in terms of accuracy (0.95). These results show the effectiveness of the current model in tourist destination image classification.

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Pick up the Best Router Signature to Detect Intrusion by Artificial Intelligence Techniques

Alodat

2023

2023 IEEE International Conference on Contemporary Computing and Communications (InC4)

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Intrusion Detection and Prevention Using Artificial Neural Network in Wireless Sensor Networks

Cited by 3 publications

References 8 publications

Machine Learning-Enhanced Advancements in Quantum Cryptography: A Comprehensive Review and Future Prospects

Machine Learning-Enhanced Advancements in Quantum Cryptography: A Comprehensive Review and Future Prospects

ATiTHi: A Deep Learning Approach for Tourist Destination Classification using Hybrid Parametric Optimization

Pick up the Best Router Signature to Detect Intrusion by Artificial Intelligence Techniques

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