Malware Classification and Clustering

Malware classification predicts whether a file, URL, process, or behavior trace is malicious; malware clustering groups related samples for family discovery and analyst triage. Inputs can be static PE metadata, byte histograms, imports, strings, sandbox behavior, network indicators, or graph features. Targets may be benign/malicious, family, behavior label, or “needs analyst review.” The model supports blocking, quarantine, case routing, or threat-intelligence enrichment.

Framing

Static detection is a classification problem when labels are available, but family discovery often starts as clustering over sparse indicators. Feature engineering is not incidental: imports, section entropy, signer metadata, packer hints, and behavioral events encode different attacker costs. Evaluation should use time-based splits, family-level holdout, ROC-AUC or PR-AUC, false-positive rate at an operational threshold, and analyst queue load.

EMBER is a canonical public benchmark for static Windows PE malware detection. The EMBER paper reports features from 1.1M binary files, including 900K training samples and 200K test samples.

Executed Artifact

To show why clustering complements but cannot replace supervised detection, the example below trains a static-feature classifier and, separately, clusters the same held-out samples, then compares the supervised AUC with how well the clusters recover the malicious-versus-benign split.

import os
import warnings
from sklearn.cluster import KMeans
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import adjusted_rand_score, roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
 
os.environ["LOKY_MAX_CPU_COUNT"] = "4"
warnings.filterwarnings("ignore", category=RuntimeWarning)
 
X, y = make_classification(
    n_samples=600,
    n_features=20,
    n_informative=8,
    n_redundant=4,
    weights=[0.65, 0.35],
    class_sep=1.3,
    random_state=19,
)
Xtr, Xte, ytr, yte = train_test_split(X, y, stratify=y, random_state=19)
clf = make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000, random_state=19)).fit(Xtr, ytr)
scaled = StandardScaler().fit_transform(Xte)
clusters = KMeans(n_clusters=2, n_init=20, random_state=19).fit(scaled).labels_
 
print("static_feature_auc", round(roc_auc_score(yte, clf.predict_proba(Xte)[:, 1]), 3))
print("cluster_ari_vs_label", round(adjusted_rand_score(yte, clusters), 3))
print("test_malware_rate", round(yte.mean(), 3))

Observed output:

static_feature_auc 0.93
cluster_ari_vs_label -0.003
test_malware_rate 0.353

The classifier separated malicious from benign samples well, while two-means clustering did not recover the detection labels. That is realistic: clustering can reveal campaigns or tooling families, but it is not a substitute for supervised detection when high-confidence labels exist.

Failure Modes

Malware work is especially vulnerable to data leakage: duplicate samples, timestamp leakage, vendor labels collected after detection, and packed variants can inflate scores. Attackers adapt, so concept drift is expected. Use anomaly detection for novel behavior, but keep a human review path because rare benign enterprise software can look suspicious.

References

• Anderson and Roth, EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models
• Elastic EMBER repository

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