On Intelligence
Detecting Attacks That Exploit Application-Logic Errors Through Application-Level Auditing
ACSAC '04 Proceedings of the 20th Annual Computer Security Applications Conference
The Software Vulnerability Guide (Programming Series) (Programming Series)
The Software Vulnerability Guide (Programming Series) (Programming Series)
The Art of Software Security Assessment: Identifying and Preventing Software Vulnerabilities
The Art of Software Security Assessment: Identifying and Preventing Software Vulnerabilities
IEEE Intelligent Systems
Random testing for security: blackbox vs. whitebox fuzzing
Proceedings of the 2nd international workshop on Random testing: co-located with the 22nd IEEE/ACM International Conference on Automated Software Engineering (ASE 2007)
PRESENCE: A Human-Inspired Architecture for Speech-Based Human-Machine Interaction
IEEE Transactions on Computers
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 01
A Game Theoretical Attack-Defense Model Oriented to Network Security Risk Assessment
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 06
A Network Security Risk Assessment Framework Based on Game Theory
FGCN '08 Proceedings of the 2008 Second International Conference on Future Generation Communication and Networking - Volume 02
An Attack Scenario Based Approach for Software Security Testing at Design Stage
ISCSCT '08 Proceedings of the 2008 International Symposium on Computer Science and Computational Technology - Volume 01
AIKED'11 Proceedings of the 10th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
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Risk assessment models attempt to predict the probability of threats on systems in order to deploy countermeasures that will ensure system security and reliability. In recent years, risk models have become dynamic in nature, which resulted in a significant improvement over their static counterpart by taking into consideration that risk and its components vary over time. However, the evident complexity of the models and the rigorous mathematical approaches suggest significant domain constraints and lack of true human-like reasoning. This lack of higher cognitive skills in automated risk assessments stems from the gap that exists between neuroscience and artificial intelligence (AI). This paper discusses the potential of using hierarchical temporal memory models for improving human-like reasoning in automated risk assessments.