A framework for using insurance for cyber-risk management
Communications of the ACM
Applications of Hidden Markov Models to Detecting Multi-Stage Network Attacks
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 9 - Volume 9
Detecting Network Attacks in the Internet via Statistical Network Traffic Normality Prediction
Journal of Network and Systems Management
Coordinated internet attacks: responding to attack complexity
Journal of Computer Security
The one-minute risk assessment tool
Communications of the ACM - Bioinformatics
CITC5 '04 Proceedings of the 5th conference on Information technology education
An incentive system for reducing malware attacks
Communications of the ACM - 3d hard copy
Spyware: a little knowledge is a wonderful thing
Communications of the ACM - Spyware
Active learning for Hidden Markov Models: objective functions and algorithms
ICML '05 Proceedings of the 22nd international conference on Machine learning
Short Paper: Dynamic Risk Mitigation for 'Self-defending' Network Security
SECURECOMM '05 Proceedings of the First International Conference on Security and Privacy for Emerging Areas in Communications Networks
Assessing COTS integration risk using cost estimation inputs
Proceedings of the 28th international conference on Software engineering
System approach to intrusion detection using hidden Markov model
Proceedings of the 2006 international conference on Wireless communications and mobile computing
Risk and information in the estimation of hidden Markov models
WSC '04 Proceedings of the 36th conference on Winter simulation
Information security models and metrics
Proceedings of the 43rd annual Southeast regional conference - Volume 2
Investigating hidden Markov models capabilities in anomaly detection
Proceedings of the 43rd annual Southeast regional conference - Volume 1
Communications of the ACM - Emergency response information systems: emerging trends and technologies
Techniques to incorporate the benefits of a hierarchy in a modified hidden Markov model
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Cyber Threat Trend Analysis Model Using HMM
IAS '07 Proceedings of the Third International Symposium on Information Assurance and Security
IAS '07 Proceedings of the Third International Symposium on Information Assurance and Security
Dynamic Risk Mitigation in Computing Infrastructures
IAS '07 Proceedings of the Third International Symposium on Information Assurance and Security
The near real time statistical asset priority driven (nrtsapd) risk assessment methodology
SIGITE '08 Proceedings of the 9th ACM SIGITE conference on Information technology education
A large-scale hidden semi-Markov model for anomaly detection on user browsing behaviors
IEEE/ACM Transactions on Networking (TON)
Modeling learning patterns of students with a tutoring system using Hidden Markov Models
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Real-time risk assessment with network sensors and intrusion detection systems
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
Using hidden markov models to evaluate the risks of intrusions
RAID'06 Proceedings of the 9th international conference on Recent Advances in Intrusion Detection
In defense of the realm: understanding the threats to information security
International Journal of Information Management: The Journal for Information Professionals
Risk forecast using hidden Markov models
ACM SIGITE Research in IT
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Conducting risk assessment on organizational assets can be time consuming, burdensome, and misleading in many cases because of the dynamically changing security states of assets. Risk assessments may present inaccurate or false data if the organizational assets change in their security postures. Each asset can change its security status from secure, mitigated, vulnerable, or compromised states. The secure state is only temporary and imaginary; it may never exist. Therefore, it is accurate to say that each asset changes its security state within its mitigated, vulnerable, or compromised, state. If we can predict each asset's security state prior to its actual state, we would have a good risk indicator for the organization's mission-critical assets. In this paper, we explore possible security states from the insider's perspective, as there are more security incidents initiated from inside than outside an organization. However, we are in a continuous loop of mitigating dynamically changing assets caused by both internal and external threats.