Bots are still a serious threat to Internet security. Although a lot of approaches have been proposed to detect bots at host or network level, they still have shortcomings. Host-level approaches can detect bots with high accuracy. However they usually pose too much overhead on the host. While network-level approaches can detect bots with less overhead, they have problems in detecting bots with encrypted, evasive communication CC channels. In this paper, we propose EFFORT, a new host-network cooperated detection framework attempting to overcome shortcomings of both approaches while still keeping both advantages, i.e., effectiveness and efficiency. Based on intrinsic characteristics of bots, we propose a multi-module approach to correlate information from different host- and network-level aspects and design a multi-layered architecture to efficiently coordinate modules to perform heavy monitoring only when necessary. We have implemented our proposed system and evaluated on real-world benign and malicious programs running on several diverse real-life office and home machines for several days. The final results show that our system can detect all 17 real-world bots (e.g., Waledac, Storm) with low false positives (0.68%) and with minimal overhead. We believe EFFORT raises a higher bar and this host-network cooperated design represents a timely effort and a right direction in the malware battle.