The owners of channels in the Bitcoin’s Lightning Network do not disclose their balances in order to protect their privacy, and conceal payment transfers through their channels. Nevertheless, recent studies have shown that channel balances can be discovered using simple probing techniques. These techniques are, however, slow, often rely on specific control messages, and require to open a new channel for every single channel balance discovery or have limited power in discovering balances of remote channels. In this work, we present a powerful balance discovery method called Torrent that overcomes these limitations of existing methods. Unlike the existing techniques, Torrent uses multi-path payments instead of single-path payments. This—together with a novel max flow algorithm designed for the Lightning Network— allows a single probing node to push a large flow of payments through any target channel, making it more likely to discover its balance. Moreover, Torrent can speed-up balance discovery through parallel payment transfers, and pre-computation of payment paths. In addition, Torrent can operate in the absence of routing control messages that are often relied on by the existing channel discovery methods.

We study the destination advertisement object (DAO) induction attack, a new attack against Internet Protocol version 6 (IPv6) routing protocol for low-power and lossy networks (RPL), the standard routing protocol for the Internet of Things (IoT). In the DAO induction attack, a compromised node in the network periodically transmits a special control message. Each of these crafted control messages induces many nodes in the network to transmit in response. We show that transmitting these unnecessary messages can significantly increase the power consumption of nodes, hence reduce the lifetime of battery-operated IoT devices. In addition, we show that the attack severely impacts end-to-end latency and packet delivery ratio, two important network performance metrics. For instance, in a network with 50 nodes, our simulation results show that the attack increases the average end-to-end latency and packet loss ratio by 410% and 260%, respectively. To counter the attack, we propose a lightweight solution. We show that our solution imposes no overhead when the network is in its normal operation (i.e., it is not under attack) and can quickly detect the attack even when the network experiences high packet loss rates.

In a payment network, like the Lightning Network, Alice can transfer a payment to Bob by splitting the payment into partial payments and transferring these partial payments through multiple paths. The transfer, however, delays if any of the partial payments fails or delays. To handle this, one can add redundant payment paths. The challenge in doing so is that Bob may now overdraw funds from the redundant paths. To address this, Bagaria, Neu, and Tse introduced Boomerang, a mechanism based on secret sharing and homomorphic one-way functions, which allows Alice to revert the transfer if Bob overdraws.