Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
SPINS: security protocols for sensor networks
Wireless Networks
TinyPK: securing sensor networks with public key technology
Proceedings of the 2nd ACM workshop on Security of ad hoc and sensor networks
Reputation-based framework for high integrity sensor networks
Proceedings of the 2nd ACM workshop on Security of ad hoc and sensor networks
TinySec: a link layer security architecture for wireless sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Security in Sensor Networks
Reputation-based Trust in Wireless Sensor Networks
MUE '07 Proceedings of the 2007 International Conference on Multimedia and Ubiquitous Engineering
An efficient intruder detection algorithm against sinkhole attacks in wireless sensor networks
Computer Communications
CHEMAS: Identify suspect nodes in selective forwarding attacks
Journal of Parallel and Distributed Computing
Featuring trust and reputation management systems for constrained hardware devices
Proceedings of the 1st international conference on Autonomic computing and communication systems
Intrusion detection of sinkhole attacks in wireless sensor networks
ALGOSENSORS'07 Proceedings of the 3rd international conference on Algorithmic aspects of wireless sensor networks
Trust, security and privacy for pervasive applications
The Journal of Supercomputing
| Hi-index | 0.00 |
Compromised sensor nodes may collude to segregate a specific region of the sensor network preventing event reporting packets in this region from reaching the basestation. Additionally, they can cause skepticism over all data collected. Identifying and segregating such compromised nodes while identifying the type of attack with a certain confidence level is critical to the smooth functioning of a sensor network. Existing work specializes in preventing or identifying a specific type of attack and lacks a unified architecture to identify multiple attack types. Dynamic Camouflage Event-Based Malicious Node Detection Architecture (D-CENDA) is a proactive architecture that uses camouflage events generated by mobile-nodes to detect malicious nodes while identifying the type of attack. We exploit the spatial and temporal information of camouflage event while analyzing the packets to identify malicious activity. We have simulated D-CENDA to compare its performance with other techniques that provide protection against individual attack types and the results show marked improvement in malicious node detection while having significantly less false positive rate. Moreover, D-CENDA can identify the type of attack and is flexible to be configured to include other attack types in future.