Full description
ECU-MALNETT (ECU MALware NETwork Traffic) is a real world, reproducible dataset of labeled benign and malicious network flows built from the Peekaboo execution corpus. Peekaboo runs evasive malware with dynamic binary instrumentation and records raw host-level PCAPs while granting full Internet access, yielding noisy, real-world captures with background OS activity and concurrent processes. To derive trustworthy labels from these traces, we apply Construct, a baseline aware, zero-trust labeling framework. Construct first ingests a baseline capture to establish reference sets (DNS qnames, HTTP hosts, TLS SNIs, and socket endpoints) and grows a conservative benign IP pool only via whitelisted DNS resolutions. During per-sample analysis, flows are marked benign only if they match the baseline or an explicit whitelist; all others are treated as suspicious. Malicious evidence then propagates: DNS resolutions outside the benign pool label dependent flows as malicious, while beacon-like timing and anomalous HTTP/port usage extend labels across related endpoints. The result is a corpus of automatically inferred, reproducible, and explainable flow labels that preserves real-world noise and avoids synthetic ground-truth assumptions, enabling rigorous, comparable benchmarking for AI-based malware-traffic analytics. Both Construct and the ECU-MALNETT labels are released to support transparent evaluation and accelerate research on network-based detection of evasive malware.
Notes
From Peekaboo’s 20,500 executed samples, ECU-MALNETT comprises a stratified random subset capped at 20 samples per family, covering 58 families across worms, ransomware, trojans, spyware, botnets, post-exploitation tools, APTs, and benign software. The result pairs realistic, noisy captures with automatically inferred labels, enabling rigorous benchmarking of malware traffic analytics without unrealistic ground-truth assumptions.
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- DOI : 10.25958/F4SC-7402
