Technical Note: \cal Q-Learning
Machine Learning
Next century challenges: scalable coordination in sensor networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
SPINS: security protocols for sensor networks
Wireless Networks
A key-management scheme for distributed sensor networks
Proceedings of the 9th ACM conference on Computer and communications security
Secure Aggregation for Wireless Networks
SAINT-W '03 Proceedings of the 2003 Symposium on Applications and the Internet Workshops (SAINT'03 Workshops)
Random Key Predistribution Schemes for Sensor Networks
SP '03 Proceedings of the 2003 IEEE Symposium on Security and Privacy
TAG: a Tiny AGgregation service for ad-hoc sensor networks
ACM SIGOPS Operating Systems Review - OSDI '02: Proceedings of the 5th symposium on Operating systems design and implementation
Impact of Network Density on Data Aggregation in Wireless Sensor Networks
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
Cache-and-query for wide area sensor databases
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
SIA: secure information aggregation in sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
AIDA: Adaptive application-independent data aggregation in wireless sensor networks
ACM Transactions on Embedded Computing Systems (TECS)
TinyPK: securing sensor networks with public key technology
Proceedings of the 2nd ACM workshop on Security of ad hoc and sensor networks
Supporting spatial aggregation in sensor network databases
Proceedings of the 12th annual ACM international workshop on Geographic information systems
SDAP: a secure hop-by-Hop data aggregation protocol for sensor networks
Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Dynamic Hierarchical Distributed Intrusion Detection System Based on Multi-Agent System
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Unleashing public-key cryptography in wireless sensor networks
Journal of Computer Security - On IWAP'05
An energy-efficient, multi-agent sensor network for detecting diffuse events
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A probabilistic model for trust and reputation
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Comprehensive trust management
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
A probabilistic approach for maintaining trust based on evidence
Journal of Artificial Intelligence Research
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Sensor nodes are often used to collect data from locations inaccessible or hazardous for humans. As they are not under normal supervision, these nodes are particularly susceptible to physical damage or remote tampering. Generally, a hierarchical data collection scheme is used by the sensors to report data to the base station. It is difficult to precisely identify and eliminate a tampered node in such a data collecting hierarchy. Most security schemes for sensor networks focuses on developing mechanism for nodes located higher in the hierarchy to monitor those located at lower levels. We propose a complementary mechanism with which the nodes at lower levels can monitor their parents in the hierarchy to detect malicious behavior. Every node maintains a reputation value of its parent and updates this at the end of every data reporting cycle. We propose a novel combination of statistical testing techniques and existing reputation management and reinforcement learning schemes to manage the reputation of a parent node. The probability that the parent node is malicious is calculated using various combination of the Q-learning algorithm and the β-Reputation scheme. The input to the β-Reputation scheme is a history of boolean events consisting of correct or erroneous data reporting events by the parent node. The boolean events are generated at each data reporting period using statistical tests. Our approach can be viewed as a mechanism composed of different modules for the detection of a malicious event, interpretation of the malicious event and updating node reputation value based on the interpretation. We have created different versions of our system by varying these components. We compared the effectiveness of these alternative designs in detecting different types of malicious behavior in sensor networks.