This work studies communication over diffusion- based molecular timing (DBMT) channels. The transmitter simultaneously releases multiple small information particles, where the information is encoded in the time of release. The receiver decodes the transmitted information based on the random time of arrival of the information particles, which is represented as an additive noise channel. For a DBMT channel, without flow, this noise follows the Lévy distribution. Under this channel model, the maximum- likelihood (ML) detector is derived and shown to have high computational complexity. It is further shown that for any additive noise channel with α-stable noise, α < 1, such as the DBMT channel, a linear receiver is not able to take advantage of the release of multiple information particles. Thus, instead of the common low-complexity linear approach, a new detector, which is based on the first arrival (FA) among all the transmitted particles, is derived. Numerical simulations indicate that for a small to medium number of released particles, the performance of the FA detector is very close to the performance of the ML detector.