Technical Note: \cal Q-Learning
Machine Learning
Elements of machine learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Formal Eavesdropping and Its Computational Interpretation
TACS '01 Proceedings of the 4th International Symposium on Theoretical Aspects of Computer Software
Energy-efficient surveillance system using wireless sensor networks
Proceedings of the 2nd international conference on Mobile systems, applications, and services
Algorithm Design
Approximate Coverage in Wireless Sensor Networks
LCN '05 Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary
Countermeasures Against Traffic Analysis Attacks in Wireless Sensor Networks
SECURECOMM '05 Proceedings of the First International Conference on Security and Privacy for Emerging Areas in Communications Networks
Delay of intrusion detection in wireless sensor networks
Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing
Security for the Mythical Air-Dropped Sensor Network
ISCC '06 Proceedings of the 11th IEEE Symposium on Computers and Communications
Sensor coverage in wireless ad hoc sensor networks
International Journal of Sensor Networks
Reinforcement learning for vulnerability assessment in peer-to-peer networks
IAAI'08 Proceedings of the 20th national conference on Innovative applications of artificial intelligence - Volume 3
A reinforcement learning approach for host-based intrusion detection using sequences of system calls
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
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A priority task for homeland security is the coverage of large spans of open border that cannot be continuously physically monitored for intrusion. Low-cost monitoring solutions based on wireless sensor networks have been identified as an effective means to perform perimeter monitoring. An ad-hoc wireless sensor network scattered near a border could be used to perform surveillance over a large area with relatively little human intervention. Determining the effectiveness of such an autonomous network in detecting and thwarting an intelligent intruder is a difficult task. We propose a model for an intelligent attacker that attempts to find a detection-free path in a region with sparse sensing coverage. In particular, we apply reinforcement learning (RL) - a machine learning approach - for our model. RL algorithms are well suited for scenarios in which specifying and finding an optimal solution is difficult. By using RL, our attacker can easily adapt to new scenarios by translating constraints into rewards. We compare our RL-based technique to a reasonable heuristic in simulation. Our results suggest that our RL-based attacker model is significantly more effective, and therefore more realistic, than the heuristic approach.