With the growing use of underwater acoustic communications (UWAC) for both industrial and military operations, there is a need to ensure communication security. A particular challenge is represented by underwater acoustic networks (UWANs), which are often left unattended over long periods of time. Currently, due to the physical and performance limitations, the UWAC packets rarely include encryption, leaving the UWAN exposed to external attacks faking legitimate messages. In this paper, we propose a new algorithm for message authentication in a UWAN setting. We begin by observing that, due to the strong spatial dependency of the underwater acoustic channel, an attacker can attempt to mimic the channel associated with the legitimate transmitter only for a small set of receivers, typically just for a single one. Taking this into account, our scheme relies on trusted nodes that independently help a sink node in the authentication process. For each incoming packet, the sink fuses beliefs evaluated by the trusted nodes to reach an authentication decision. These beliefs are based on the estimated statistics of the channel parameters, which are chosen to be the most sensitive to the transmitter-receiver displacement. Our simulation results show accurate identification of an attacker’s packet. We also report results from a sea experiment demonstrating the effectiveness of our approach.
We consider a cellular Internet of things (CIoT) network where many source nodes aim at exchanging messages with a single concentrator node. To this end, they are assisted by anchor nodes that are trusted and securely connected with the concentrator node. In this context, we aim at providing a message authentication scheme based on the characteristics of the channel between the source nodes and the anchor nodes. According to this approach, the anchor nodes estimate the channel to source nodes in an initially externally authenticated fashion, while forthcoming messages are authenticated by comparing the current channel estimate with the initial estimate. Moreover, assuming that the anchor nodes have a limited energy availability, we derive suitable scheduling policies for the activation of the anchor nodes for authentication purposes. In particular, we aim at maximizing the anchors lifespan while guaranteeing given false alarm and missed detection probabilities of the authentication process. The performance of the proposed authentication protocols is evaluated in a typical CIoT scenario. In this preliminary work, we study the problem of distributed authentication in wireless networks. Specifically, we consider a system where multiple Bob (sensor) nodes listen to a channel and report their correlated measurements to a Fusion Center (FC) which makes the ultimate authentication decision. For the feature- based authentication at the FC, channel impulse response has been utilized as the device fingerprint. Additionally, the correlated measurements by the Bob nodes allow us to invoke Compressed sensing to significantly reduce the reporting overhead to the FC. Numerical results show that: i) the detection performance of the FC is superior to that of a single Bob-node, ii) compressed sensing leads to at least @math overhead reduction on the reporting channel at the expense of a small ( @math dB) SNR margin to achieve the same detection performance. Physical (PHY)-layer authentication systems can exploit channel state information of radio transmitters to detect spoofing attacks in wireless networks. The use of multiple landmarks each with multiple antennas enhances the spatial resolution of radio transmitters, and thus improves the spoofing detection accuracy of PHY-layer authentication. Unlike most existing PHY-layer authentication schemes that apply hypothesis tests and rely on the known radio channel model, we propose a logistic regression-based authentication to remove the assumption on the known channel model, and thus be applicable to more generic wireless networks. The Frank–Wolfe algorithm is used to estimate the parameters of the logistic regression model, in which the convex problem under a @math -norm constraint is solved for weight sparsity to avoid over-fitting in the learning process. We design a distributed Frank–Wolfe-based PHY-layer authentication to further reduce the communication overhead between the landmarks and the security agent. Then, we construct an incremental aggregated gradient-based scheme to provide online authentication with a higher accuracy and lower computation overhead. Simulation and experimental results validate the accuracy of the proposed authentication schemes, and show the reduced communication and computation overheads.