Sensor networks may be used in many monitoring applications where the locations of the monitored objects are quite sensitive and need to be protected. Previous research mainly focuses on protecting source location against mote-class attackers who only have a local view of the network traffic. In this paper, we focus on how to protect the source location against laptop-class attackers who have a global view of the network traffic. This paper proposes four schemes---naive, global, greedy, and probabilistic---to deal with laptop-class attacks. The naive solution uses maintenance messages sent periodically to hide real event reports. The global and greedy solutions improve the naive solution by reducing the latency of event delivery without increasing communication overhead. The probabilistic solution further improves the performance by reducing communication overhead without sacrificing location privacy. Experiments show that the probabilistic solution is practical for providing source location privacy against a laptop-class attacker.

Localization is a fundamental problem in wireless sensor networks. Currently, most localization protocols assume a benign environment. This paper studies how a localization protocol can be affected by attacks, especially those like wormhole attacks that don't need to capture the keys used in the network, and how such an attack can be detected and defended in distributed scheme. By applying several localization algorithms, this paper first shows that wormhole attacks are a serious threat to all these algorithms, based on simulation in NS-2. The paper first evaluates the effects of wormhole attacks on several Range-Free Anchor-Free (RFAF) localization algorithms based on hop-counting and connectivity techniques. Then, the paper presents an RFAF localization algorithm called Wormhole-resilient Geographic Distributed Localization (WGDL), which embeds a wormhole detecting/recovering mechanism: if this mechanism detects wormhole attacks in location computation, then it will restore the localization by freezing the wormhole affected area. Simulations show that the proposed detection method is effective on different network placements, and that the mechanism has both a low False Toleration Rate (FTR) and a low False Detection Rate (FDR) in detecting wormhole attacks.

Sensor networks are used in a variety of applications such as battlefield reconnaissance, environmental monitoring, and traffic monitoring. Security and privacy become important concerns when people are participants in sensor network applications [1]. In this research, we focus on providing a framework in which the privacy of people participating in sensor networks applications is protected.

Collaborative filtering techniques have become popular in the past decade as an effective way to help people deal with information overload. Recent research has identified significant vulnerabilities in collaborative filtering techniques. Shilling attacks, in which attackers introduce biased ratings to influence recommendation systems, have been shown to be effective against memory-based collaborative filtering algorithms. We examine the effectiveness of two popular shilling attacks (the random attack and the average attack) on a model-based algorithm that uses Singular Value Decomposition (SVD) to learn a low-dimensional linear model. Our results show that the SVD-based algorithm is much more resistant to shilling attacks than memory-based algorithms. Furthermore, we develop an attack detection method directly built on the SVD-based algorithm and show that this method detects random shilling attacks with high detection rates and very low false alarm rates.

Sensor networks are used in a variety of application areas for diverse problems from habitat monitoring to military tracking. Whenever they are used to monitor sensitive objects, the privacy of monitored objects' locations becomes an important concern. When a sensor reports a monitored object by sending a series of messages through the sensor network, the route these messages take in theory creates a trail leading back to their source. By eavesdropping on communications, an attacker may be able to move from node to node to follow this trail. Several approaches aimed at discouraging this kind of eavesdropping have been proposed, including mechanisms for constructing "phantom" routes and approaches that insert fake sources as background noise. A problem with existing approaches is that message latencies become larger and energy costs become higher as a result of introducing protections for the privacy of a source location. This paper proposes a new cyclic entrapment method (CEM) that protects source locations in sensor networks while adding a comparatively low cost in terms of additional message latency and energy.