With the widespread use of Internet of Things (IoT) devices, malware detection has become a hot spot for both academic and industrial communities. Existing approaches can be roughly categorized into network-side and host-side. However, existing network-side methods are difficult to capture contextual semantics from cross-source traffic, and previous host-side methods could be adversary-perceived and expose risks for tampering. More importantly, a single perspective cannot comprehensively track the multi-stage lifecycle of IoT malware. In this paper, we present CMD, a co-analyzed IoT malware detection and forensics system by combining hardware and network domains. For the network part, CMD proposes a tailored capsule neural network to capture the contextual semantics from cross-source traffic. For the hardware part, CMD designs an entire file operation recovery process in a side-channel manner by leveraging the Serial Peripheral Interface (SPI) signals from on-chip traces. These traffic provenance and operating logs information could benefit the anti-virus countermeasures for security practitioners. By practical evaluation, we demonstrate that CMD realizes outstanding detection effects (e.g., ∼∼99.88% F1-score) compared with seven state-of-the-art methods, and recovers 96.88%∼∼99.75% operation commands even if against adaptive adversaries (that could kill processes or tamper with operation log files). A by-product benefit of such an external monitor is CMD introduces zero latency on the IoT device, and incurs negligible IoT CPU utilization. Also, since SPI focuses on file operations, the proposed hardware trace forensics does not have the data explosion problem like previous work, e.g., recovered logs of CMD only take up limited extra space overhead (e.g., ∼∼0.2 MB per malware). Furthermore, we provide the model interpretability for the capsule network and develop a case study (Hajime) of the operation logs recovery.