Event‐driven data alteration detection using block‐chain. Issue 2 (16th February 2021)

• Record Type: Journal Article
• Authors: Srivastava, Shubhi; Mohit, ; Kumar, Arun; Jha, Sudhanshu Kumar; Dixit, Pratibha; Prakash, Shiv
• Is Part Of: Security and privacy. Volume 4:Issue 2(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-02-16
• Subjects: block‐chain -- data integrity -- insider attack -- large databases -- machine learning techniques; Computer security -- Periodicals; Data protection -- Periodicals; Cyberterrorism -- Periodicals; 005.8
• DOI: 10.1002/spy2.146
• Languages: English
• ISSNs: 2475-6725

Abstract

Abstract: With the rapid development of Internet and Internet‐based applications, accessing of the file become easy from various locations at the same time around the globe, however, maintaining of data integrity turn into a challenging issue. In order to maintain a data integrity in a large database(s), database file is being kept to secure with the presence of control policies and firewalls, which basically limit the outside users with more strict control policies. Despite these control policies, there still lies a threat to the security of the data through an insider attack. An insider attack refers to someone who is a part of the organization and can have access to the resources (network, system, etc.) with inside knowledge of the system or knows the administrative rights, and thus interferes with the data, now become one of the most critical challenging issues for the organization as such attacks are hard to notice. An insider attacker is more crucial to the organization as they can perform espionage, embezzlement, theft of the intellectual property, disclosing of the research, and development being carried out in the organization, and sabotage. Therefore, to highlight this, an Event‐Driven Data Alteration Detection technique using the tamper resistance property of Block‐chain technology is being presented in this paper. The model has been tested and found that if any unauthorized alteration in the database is being made then it can be detected using the Block‐chain Database API. To make it robust to insider attacks we have implemented and tested the model on a web‐based application. A machine learning based technique will be considered for detection in the future scope of this paper. The results obtained after experiments show the performance of the proposed architecture and the comparison outcome shows that the proposed architecture is better than related models.