Visible to the public Biblio

Filters: Author is Sharma, D.  [Clear All Filters]
2019-08-05
Chavan, N. S., Sharma, D..  2018.  Secure Proof of Retrievability System in Cloud for Data Integrity. 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). :1-5.

Due to expansion of Internet and huge dataset, many organizations started to use cloud. Cloud Computing moves the application software and databases to the centralized large data centers, where the management of the data and services may not be fully trustworthy. Due to this cloud faces many threats. In this work, we study the problem of ensuring the integrity of data storage in Cloud Computing. To reduce the computational cost at user side during the integrity verification of their data, the notion of public verifiability has been proposed. Our approach is to create a new entity names Cloud Service Controller (CSC) which will help us to reduce the trust on the Third Party Auditor (TPA). We have strengthened the security model by using AES Encryption with SHA-S12 & tag generation. In this paper we get a brief introduction about the file upload phase, integrity of the file & Proof of Retrievability of the file.

2018-02-21
Elsaeidy, A., Elgendi, I., Munasinghe, K. S., Sharma, D., Jamalipour, A..  2017.  A smart city cyber security platform for narrowband networks. 2017 27th International Telecommunication Networks and Applications Conference (ITNAC). :1–6.

Smart city is gaining a significant attention all around the world. Narrowband technologies would have strong impact on achieving the smart city promises to its citizens with its powerful and efficient spectrum. The expected diversity of applications, different data structures and high volume of connecting devices for smart cities increase the persistent need to apply narrowband technologies. However, narrowband technologies have recognized limitations regarding security which make them an attractive target to cyber-attacks. In this paper, a novel platform architecture to secure smart city against cyber attackers is presented. The framework is providing a threat deep learning-based model to detect attackers based on users data behavior. The proposed architecture could be considered as an attempt toward developing a universal model to identify and block Denial of Service (DoS) attackers in a real time for smart city applications.