Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data 2006
DOI: 10.1145/1142473.1142487
|View full text |Cite
|
Sign up to set email alerts
|

Forensic analysis of database tampering

Abstract: Mechanisms now exist that detect tampering of a database, through the use of cryptographically-strong hash functions. This paper addresses the next problem, that of determining who, when, and what, by providing a systematic means of performing forensic analysis after such tampering has been uncovered. We introduce a schematic representation termed a "corruption diagram" that aids in intrusion investigation. We use these diagrams to fully analyze the original proposal, that of a linked sequence of hash values. … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

2010
2010
2021
2021

Publication Types

Select...
4
2

Relationship

0
6

Authors

Journals

citations
Cited by 24 publications
(3 citation statements)
references
References 9 publications
(7 reference statements)
0
3
0
Order By: Relevance
“…Accuracy is the percentage of number of tuples correctly classified by rule and the number of tuples covered by rule. The coverage for given rule R, is given by Equation (1) and accuracy is given by (2). [38] accuracy(R) = ncorrect ncovers (2) [38] Where, ncovers = The number of tuples covered by rule |D| = Total number of tuples present in transaction ncorrect = The number of tuples correctly classified by rule The coverage and accuracy for insert and update tampering are given in Table 5.…”
Section: • Identify Suspicious Tamperingmentioning
confidence: 99%
See 1 more Smart Citation
“…Accuracy is the percentage of number of tuples correctly classified by rule and the number of tuples covered by rule. The coverage for given rule R, is given by Equation (1) and accuracy is given by (2). [38] accuracy(R) = ncorrect ncovers (2) [38] Where, ncovers = The number of tuples covered by rule |D| = Total number of tuples present in transaction ncorrect = The number of tuples correctly classified by rule The coverage and accuracy for insert and update tampering are given in Table 5.…”
Section: • Identify Suspicious Tamperingmentioning
confidence: 99%
“…Any alteration in the database is called as database tampering. Database tampering, addresses the problem of determining who, when and what data has been tampered [2]. This article addresses for when and what data has tampered, which may be useful for database forensic studies.…”
Section: Introductionmentioning
confidence: 99%
“…Kyriacos E. Pavlou said about how to investigate data manipulated in the database system, especially suggesting an algorithm likely to find out information about who manipulated the data as well as the fact that the data was manipulated. Such information will be quite useful for cases when work data is lost or leaked out [4].…”
Section: Related Workmentioning
confidence: 99%