Research On Secure Techniques For Data Processing In Wireless Sensor Networks

In recent years, with the rapid development of wireless communication tech-niques, microelectronics and embedded computing techniques, Wireless Sensor Networks(WSNs) are being widely used in many fields, such as environment monitoring, healthcare and military defense. To avoid a large amount of transmission of raw sensing datafrom the sensor network to the base station, in-network data processing becomes an essen-tial approach for users to extract information from wireless sensor networks and has beenwidely studied. It greatly reduces the communication cost and improves the efficiency ofnetwork communication and information processing. However, the existing in-networkdata processing schemes suffers various serious security threats. During the in-networkdata processing, the adversaries can obtain confidential information through interceptingthe wireless channels and compromising sensor nodes, can manipulate the results of dataprocessing by compromised nodes, can disrupt the data availability and enlarge attackresults. Considering the in-network data processing involves routing, topology controland distributed query processing and so on, It is a crucial problem in sensor network se-curity to design security mechanisms from various aspects of in-network processing todefend above threats and achieve the confidentiality, authenticity, integrity and availabil-ity of data and computation results. Regarding the features of WSNs, the study of securein-network data processing was focused on by this dissertation from aspects of topologycontrol, false data en-route filtering, secure aggregation and intrusion detection. The maincontributions of this dissertation are as follows:First, a distributed secure clustering protocol is proposed. To reduce energy cost,improve the communication efficiency and scalability, WSNs are usually organized intoclusters to carry out the in-network data processing task such as event detection and queryprocessing. Since the adversaries can disrupt and misuse clustering protocols to effec-tively attack the in-network data processing. As a result, the security of the clusteringprotocols is a basic requirement for the secure in-network data processing. In the pro-posed secure clustering protocol, the secure network initialization, with the random num-ber broadcast from the trusted base station, the randomness and verifiability of clusterhead selection are ensured while achieving better scalability comparing the centralized secure clustering protocols. The protocol defends malicious cluster-member recruitingand multiple cluster-membership attacks by establishing the d-hop neighbor list and ap-proximate hops of shortest paths to nodes in d-hop neighborhood for every node. Basedon the one-way hash chain technique, the protocol can verify the authenticity of the clus-ter head identity. The security and cost of the proposed protocol are evaluated and theresults show the resiliency and efficiency of the protocol.Second, a probabilistic false data en-route filtering scheme is proposed, referred to asGRPEF. During the in-network data processing for event detection and report task, the ad-versaries can inject false reports to exhaust network energy or trigger false alarms throughcompromised nodes. Thus, resilient report authentication and efficient en-route filteringare required to protect the report authenticity and prevent malicious energy consumptionon the routing path. Several existing schemes for filtering false reports either suffer athreshold limitation problem, which may easily lead to complete breakdown of the secu-rity protection, or are designed within the scenarios of static sinks and specific routingprotocols, which cannot work with mobile sinks and other kinds of protocols. In responseto these, a scheme referred to as Grouping-enhanced Resilient Probabilistic En-route Fil-tering (GRPEF) is proposed. In GRPEF, a multi-coordinate system based location-awarekey derivation approach is used to overcome the threshold problem and removes the de-pendence on the sink stationarity and routing protocols, thus GRPEF can be applicable tothe sensor networks with mobile sinks while reserving the resiliency. Besides, GRSEF di-vides sensor nodes into groups through an efficient distributed algorithm without incurringextra groups and reducing the filtering effectiveness as the existing schemes. Compared tothe existing schemes, GRPEF significantly improves the en-route filtering effectivenesswhile being able to achieve the same T-authentication coverage degree as the existingschemes.Third, a secure continuous aggregation scheme is proposed to verify the correct-ness of the temporal variation patterns of aggregation results. in-network aggregationprovides an energy-efficient way to extract summarization information from sensor net-works. Considering that WSNs are usually used to monitor physical environments for along time, continuous aggregation is needed to obtain the temporal variation informationof some interesting aggregates by users. However, for the continuous in-network aggre-gation in a hostile environment, the adversary could manipulate a series of aggregation results through compromised nodes to fabricate false temporal variation patterns of theaggregates. Existing secure aggregation schemes conduct one individual verification foreach aggregation result and would incur significant communication cost if they are di-rectly applied to detect false temporal variation pattern. The proposed scheme checksonly a small part of aggregation results to verify the correctness of the temporal variationpatterns in a time window. The checking of the aggregation results uses a sampling-basedapproach, which involves only a small part of sensor nodes and enables the proposedscheme independent of any particular in-network aggregation protocol. Besides, a seriesof security mechanisms are proposed to protect the sampling process, in which the identitylegitimacy of sampled nodes are protected by verifiable random sample and the integrityof samples are protected by spatial- correlation based local sample authentication.Fourth, an intrusion detection system framework SpyMon is proposed and two in-trusion detection schemes C-SpyMon and D-SpyMon based on different strategies areproposed under this framework. On one hand, SpyMon protects the monitor nodes fromidentity exposure during the monitor selection and prevent them becoming the explicittargets of adversaries. On the other hand, SpyMon randomly selects a subset of sensornodes as monitors to achieve energy-efficiency, and ensure each sensor node being mon-itored by at least k nodes in deterministic or probabilistic ways to achieve reliability. Acollective monitoring triggering scheme is also proposed to further improve the capabilityand reliability of monitoring. Our analysis shows that SpyMon is resilient against nodecompromise while attaining energy efficiency.