Secure Data Aggregation Protocol with Byzantine Robustness for Wireless Sensor Networks

• Main Author: Khalifa, Tarek
• Language: en
• Published: 2007
• Subjects: security; byzantine; secure aggregation; sensor networks; Electrical and Computer Engineering
• Online Access: http://hdl.handle.net/10012/3333

Sensor networks are dense wireless networks constituting of small and low-cost sensors that collect and disseminate sensory data. They have gained great attention in recent years due to their ability to offer economical and effective solutions in a variety of fields; and their profound suitability to address mission critical problems that are common in health, transportation, and military applications. “Sensor networks” is a technology that is seen to change the world, and as such their deployment is expected to see a rapid growth. Effective security strategy is essential for any sensor network in order to maintain trustful and reliable functionality, protect sensory information, and ensure network component authenticity. Security models and protocols that are typically used in other types of networks, such as wired networks, are not suitable for sensor networks due to their specific hardware specifications. This thesis highlights some of the research done so far in the area of security of wireless sensor networks and proposes a solution to detect Byzantine behaviour - a challenging security threat that many sensor networks face. The proposed solution’s use of cryptography is kept at a minimum to ensure maximum secure bandwidth. Under this solution, a sensor network continues to work normally until an attack is suspected. Once an attack is suspected, a cryptography scheme is enabled to authenticate suspected nodes and to allow the identification of potential external attacks. If an attack seems to persist after the cryptography scheme has been enabled, the same mechanism is used to identify and isolate potentially compromised nodes. The goal is to introduce a degree of intelligence into such networks and consequently improve reliability of data collection, accuracy of aggregated data, and prolong network lifetime.