The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
DBMS Log Analytics for Detecting Insider Threats in Contemporary Organizations
|
|
Author(s): Muhammad Imran Khan (Insight Centre for Data Analytics, Ireland), Simon N. Foley (IMT Atlantique, France)and Barry O'Sullivan (University College Cork, Ireland)
Copyright: 2019
Pages: 28
Source title:
Security Frameworks in Contemporary Electronic Government
Source Author(s)/Editor(s): Ryma Abassi (Carthage University, Tunisia)and Aida Ben Chehida Douss (Carthage University, Tunisia)
DOI: 10.4018/978-1-5225-5984-9.ch010
Purchase
|
Abstract
Insiders are legitimate users of a system; however, they pose a threat because of their granted access privileges. Anomaly-based intrusion detection approaches have been shown to be effective in the detection of insiders' malicious behavior. Database management systems (DBMS) are the core of any contemporary organization enabling them to store and manage their data. Yet insiders may misuse their privileges to access stored data via a DBMS with malicious intentions. In this chapter, a taxonomy of anomalous DBMS access detection systems is presented. Secondly, an anomaly-based mechanism that detects insider attacks within a DBMS framework is proposed whereby a model of normative behavior of insiders n-grams are used to capture normal query patterns in a log of SQL queries generated from a synthetic banking application system. It is demonstrated that n-grams do capture the short-term correlations inherent in the application. This chapter also outlines challenges pertaining to the design of more effective anomaly-based intrusion detection systems to detect insider attacks.
Related Content
|
Azeem Khan, Noor Zaman Jhanjhi, Dayang Hajah Tiawa Binti Awang Haji Hamid, Haji Abdul Hafidz bin Haji Omar.
© 2024.
30 pages.
|
|
Siva Raja Sindiramutty, Chong Eng Tan, Sei Ping Lau, Rajan Thangaveloo, Abdalla Hassan Gharib, Amaranadha Reddy Manchuri, Navid Ali Khan, Wee Jing Tee, Lalitha Muniandy.
© 2024.
67 pages.
|
|
Ruchi Doshi, Kamal Kant Hiran.
© 2024.
16 pages.
|
|
N. Ambika.
© 2024.
9 pages.
|
|
Siva Raja Sindiramutty, Wee Jing Tee, Sumathi Balakrishnan, Sukhminder Kaur, Rajan Thangaveloo, Husin Jazri, Navid Ali Khan, Abdalla Gharib, Amaranadha Reddy Manchuri.
© 2024.
54 pages.
|
|
Azeem Khan, NZ Jhanjhi, Dayang Hajah Tiawa Binti Awang Haji Hamid, Haji Abdul Hafidz bin Haji Omar.
© 2024.
22 pages.
|
|
Azeem Khan, Noor Zaman Jhanjhi, Dayang Hajah Tiawa Binti Awang Haji Hamid, Haji Abdul Hafidz bin Haji Omar.
© 2024.
36 pages.
|
|
|