International Journal of Interpreting Enigma Engineers (IJIEE)

Optimized Cloud Security Ecc-enhanced Homomorphic Paillier Re-encryption

Veeresh Dachepalli — 2024-06-18

In the dynamic domain of cloud computing, ensuring data security is of utmost importance. Conventional encryption techniques, while providing a high level of security, introduce substantial computational burdens, rendering them impractical for environments with limited resources. In response to this predicament, our study introduces a novel lightweight encryption framework that amalgamates Elliptic Curve Cryptography (ECC) with Homomorphic Paillier Re-Encryption, thereby reinforcing the security of data within cloud infrastructures.

Our methodology exploits the inherent advantages of ECC, notably its ability to maintain stringent security measures with relatively smaller key dimensions, thereby optimizing efficiency without sacrificing the level of security. The integration of ECC with Homomorphic Paillier Encryption facilitates the execution of secure computations on ciphered data, maintaining user privacy while permitting the cloud to perform meaningful data operations. The re-encryption feature of our scheme ensures the secure mobility and modification of data sans decryption, thus augmenting security measures and operational flexibility.

The proposed encryption paradigm has been validated through rigorous theoretical scrutiny and empirical implementation, revealing marked enhancements in both computational efficiency and security measures when juxtaposed with established encryption techniques. The empirical evidence suggests that the streamlined nature of our encryption scheme renders it exceptionally compatible with real-world cloud applications, particularly in scenarios where the optimization of resources is imperative.

This work contributes to the field of cloud data security by providing a scalable, efficient, and secure encryption solution, paving the way for more secure and practical cloud computing applications.

Smart Security Solutions for Cloud Infrastructure Using Machine Learning

Sindhooja Abbagalla — 2024-06-18

Cloud computing has become a ubiquitous storage, processing, and data management tool. However, providing strong security measures inside cloud infrastructure remains a primary priority.The purpose of this study is to give an overview of the process of integrating cloud infrastructure with machine learning. The main objective of this work is to leverage machine learning approaches and models for threat identification, anomaly detection, and access control methods in order to protect sensitive data and reduce growing risks in the cloud infrastructure. Ultimately, this research endeavours to enhance the overall security posture of cloud computing, enabling organisations to harness the full potential of the cloud while safeguarding their critical assets and sensitive information.

Atm Transaction Without a Card Via Biometric and Hand Recognitionmetric

Manisha Bandari — 2024-06-18

Study and Analysis of Market Basket Analysis Using Apirori Algorithm

Tanusha Gorak — 2024-06-18

One data mining technique that has been increasingly popular across several industries, especially in retail and e-commerce, is market basket analysis, or MBA. This technique examines transactional data to find patterns and relationships between the products that are frequently purchased. An MBA is essential for understanding consumer behavior, which helps companies place products more effectively, develop more effective marketing campaigns, and run their operations more efficiently overall. The MBA is still a vital tool for organizations to stay abreast of changing customer preferences, stimulate rapid decision-making, and extract relevant information from the massive amounts of transactional data they are dealing with. MBA is used in various industries outside of retail, including supply chain management, healthcare, and internet platforms.

A Roadmap to Success: Strategies and Challenges in Adopting Aiops for it Operations

Anil Kumar Mondru — 2024-06-18

The evolution of information technology (IT) has introduced significant complexities that far exceed the capabilities of traditional IT management approaches. Artificial Intelligence for IT Operations (AIOps) offers a transformative prowess by leveraging AI and machine learning to enhance IT operations using some of the concepts such as anomaly detection and predictive analysis and many more. Regardless of the benefits, integrating AIOps with existing IT operations poses significant challenges, including data integration, model interpretability, and overcoming organizational resistance to change. This article aims to explore the technological advancements, practical applications and the challenges of AIOps. The findings highlight that while AIOps can significantly improve operational efficiency and accuracy, addressing integration challenges is crucial for its successful adoption. This article emphasises the significance and crucial role of data management, and strategic change management can help pave a future for resilient AIOps infrastructure. Effective implementation strategies, such as developing robust data pipelines and employing interpretable AI models are also discussed. These insights will be invaluable for those looking to stay ahead in the rapidly advancing landscape of IT operations so follow along.