The problem of identifying and detecting Botnets Command and Control (C&C) channels is considered. A Botnet is a logical network of compromised machines (Bots) which are remotely controlled by an attacker (Botmaster) using a C&C infrastructure in order to perform malicious activities. Accordingly, a key objective is to identify and block the C&C before any real harm is caused. We propose an anomaly detection algorithm and apply it to timing data, which can be collected without deep inspection, from open as well as encrypted flows. The suggested algorithm utilizes the Lempel Ziv universal compression algorithm in order to optimally give a probability assignment for normal traffic (during learning), then estimate the likelihood of new sequences (during operation) and classify them accordingly. Furthermore, the algorithm is generic and can be applied to any sequence of events, not necessarily traffic-related. We evaluate the detection algorithm on real-world network traces, showing how a universal, low complexity C&C identifi- cation system can be built, with high detection rates for a given false-alarm probability.