Cybersecurity + AI Research Hub

A concise, practical hub to organize research and experiments applying AI to cybersecurity problems such as phishing, malware, network anomalies, fraud, and threat intelligence. This page bundles domain guidance, model recommendations, baselines, reproducibility best practices, starter experiments, and collaboration pointers.

Explore Datasets Models & Methods Learning Roadmap

Open research Reproducible artifacts Responsible use

Quick actions for contributors

Download the sample CSV and run the starter Colab.
Fork a baseline, run experiments, and register results here.
Share reproducibility artifacts: code commit, seed, env, and data hash.

Domains & Why they suit AI research

Below are cybersecurity domains where AI methods typically add value, the common data types and typical tasks for each domain.

Phishing & URL analysis

Data: URLs, HTML, WHOIS, TLS, landing page screenshots. Tasks: binary classification, clustering of campaigns, token attribution. Good for NLP/sequence and tabular ML; abundant labeled datasets.

Malware detection & attribution

Data: static features (imports, strings), dynamic telemetry, assembly traces. Tasks: malicious/benign classification, family attribution, clustering. Suited to multimodal models combining static + dynamic inputs.

Network & host anomaly detection

Data: NetFlow, PCAP, logs, host telemetry. Tasks: unsupervised anomaly detection, time-series forecasting. Large-scale time-series fits deep sequence models and unsupervised methods.

Fraud detection

Data: transactions, device fingerprints, session logs. Tasks: classification, sequence scoring, risk ranking. Operationally important; class imbalance and cost-sensitive learning required.

Threat intelligence & TTP extraction

Data: reports, feeds, passive DNS, indicator graphs. Tasks: entity extraction, link prediction, knowledge graph population. Well-suited to NLP and graph ML.

Insider threat & user analytics

Data: activity logs, access patterns, email metadata. Tasks: anomaly detection, sequence modeling, risk scoring—requires privacy-aware methods and concept-drift handling.

Darknet / Darkweb

Data: forum posts, marketplace listings, crypto traces. Tasks: illicit vs benign classification, seller clustering, link prediction. (Legally sensitive)

Deepfakes & multimedia

Data: images, video, audio, metadata. Tasks: deepfake detection, provenance, localization. (High compute, privacy constraints)

IoT & Smart City

Data: sensor telemetry, device logs, SCADA traces. Tasks: device compromise detection, sensor anomaly, predictive maintenance.

Models & Methods — from basics to advanced

A practical primer listing model families, when to use them, common tasks, and evaluation guidance.

Problem types & formulations

Supervised classification — phishing vs safe, malware vs benign.
Regression / scoring — risk score prediction.
Unsupervised anomaly detection — find novel/unknown attacks.
Semi‑supervised learning — few labels, many unlabeled examples.
Clustering — group campaigns or malware variants.
Sequence & time-series modeling — session/log/trace analysis.
Graph learning — link prediction and threat graph reasoning.
Information extraction / NLP — parse reports and feeds.

Baseline & classical models (start here)

Logistic Regression — interpretable baseline for tabular features.
Decision Trees & Random Forests — robust, good default.
XGBoost / LightGBM / CatBoost — strong tabular performance.
SVM — useful for smaller, cleaner datasets.

Neural & sequence models

MLP for fused tabular/dense inputs.
1D/2D CNNs — URL token convs or page screenshot analysis.
RNNs/LSTM/GRU — system-call or session sequences.
Transformers — state-of-the-art for text and long sequences.

Text models & NLP

Transformers (BERT, RoBERTa, DeBERTa) — state-of-the-art for classification, NER, relation extraction.
DistilBERT / TinyBERT — distilled/lightweight Transformers for low-latency inference and edge use.
Named Entity Recognition (NER) — extract IOCs, CVEs, malware family names from text.
Embeddings & semantic search — sentence/document embeddings for similarity, clustering, and threat hunting.
Semantic search & retrieval — vector search (FAISS) for fast threat intel lookup.
Robustness / adversarial tests — obfuscation, token-level perturbations and defenses.

Image models & Vision

YOLO family — real-time object detection for screenshots, UI element detection and spoof/UI-injection checks.
ViT (Vision Transformer) — powerful image representations for screenshot classification and deepfake detection.
Faster R-CNN / SSD — higher-accuracy detectors for forensic image analysis.
OCR & layout analysis — extract text and form fields from screenshots (Tesseract, transformer-OCR).
Visual embeddings & similarity — nearest-neighbor / clustering for near-duplicate detection and campaign linking.
Deepfake detection & provenance — multimodal checks, temporal-consistency and model-fingerprint methods.

Graph, unsupervised & advanced techniques

GNNs (GCN, GAT, GraphSAGE) for indicator graphs and link prediction.
Isolation Forest, LOF, Autoencoders for anomaly detection.
Contrastive & self-supervised learning to leverage unlabeled data.
Federated learning & differential privacy for cross-org collaboration.
Few-shot/meta-learning for new malware families.

Explainability & robustness

SHAP, LIME, permutation importance for model explanations.
Calibration, uncertainty estimates and probabilistic outputs for triage.
Adversarial testing (red-teaming) to assess evasion risk.

Tooling

Data: pandas, numpy, Dask
Classical ML: scikit-learn
Boosting: XGBoost, LightGBM, CatBoost
DL: PyTorch, TensorFlow; Transformers: Hugging Face
Graphs: PyTorch Geometric, DGL
Explainability: SHAP, LIME
Tracking: MLflow, WandB

Mapping problem → recommended models & metrics (quick reference)

Problem	Models	Key metrics
Phishing (URL / page)	LogReg, RandomForest, XGBoost / LightGBM; Transformers (BERT/DistilBERT) for surrounding text; 1D CNNs or token-conv for URL tokens; CNN/ViT for page screenshots.	Precision, Recall, F1, PR-AUC; Precision@k; operational FP burden (alerts/hour).
Malware (static / dynamic)	RF / XGBoost on engineered features; CNN on byte n‑grams; dynamic telemetry: RNNs / Temporal CNNs; hybrid ensembles (static + dynamic).	Precision/Recall, per-family accuracy, PR‑AUC / ROC‑AUC; confusion matrix per family.
Network anomaly	Isolation Forest, LOF, Autoencoders / LSTM‑AE, Temporal CNNs, Transformers for sequences.	Detection rate, False Positive Rate (FPR), precision@k, time‑to‑detection.
Threat intelligence (text / graph)	Transformers (BERT/RoBERTa) for extraction; GNNs (GraphSAGE, GAT) for linking and link prediction; knowledge-graph pipelines.	Precision/Recall for extraction; link‑pred AUC; entity resolution F1.
Deepfakes & multimedia abuse	Vision Transformer (ViT), CNN ensembles (ResNet/EfficientNet), 3D‑CNN / I3D / TimeSformer for video; wav2vec / audio models; multimodal fusion (audio+video+text) and model‑fingerprint detectors.	AUC / mAP; Equal Error Rate (EER); frame‑level precision/recall; temporal consistency metrics; robustness to compression/transcoding.
IoT / Smart City (edge)	Lightweight models: Tiny CNNs, DistilBERT for logs, LSTM/Temporal CNNs for sensor series; Autoencoders for anomaly detection; federated learning setups for cross-site training.	Detection rate, FPR, latency & resource usage (RAM/CPU), energy consumption; real-time constraints.
Darknet / illicit marketplace analysis	DistilBERT/Transformers for post classification & NER; topic models; clustering for vendor ecosystems; GNNs for seller/wallet linking.	Precision/Recall for classification; clustering purity / silhouette; link‑pred AUC; legal/privacy constraints — publish metadata only.

Notes: choose evaluation metrics aligned with operational objectives (e.g., prioritize low FP burden for SOC). For multimedia/deepfake detection, include robustness tests (compression, re-encoding, adversarial perturbations). Consider model cards and dataset privacy notices for sensitive sources (darknet, leaked data).

Datasets, features & practical tips

Common features used in security tasks

URL tokens, host/path n-grams, URL length, token counts, presence of '@', hyphens
TLS certificate fields, WHOIS age, domain age, registrar
Static malware features: imports, strings, file size, entropy
Dynamic telemetry: API calls sequence, network endpoints, process trees
Session aggregates: rates, time-of-day patterns, device fingerprints

Practical data notes

Label quality: expect noise — consider label-cleaning or robust losses.
Class imbalance: use stratified splits, resampling, or cost-sensitive losses.
Privacy & PII: redact or synthesize sensitive fields before sharing.
Reproducibility: publish data checksums, environment spec, and random seeds.

Baselines & reproducibility

Starter Colab / Jupyter snippet

Copy the snippet and run in Colab after uploading a CSV (or use the sample CSV provided).

# Colab starter: load CSV -> train RandomForest -> evaluate
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix
import seaborn as sns, matplotlib.pyplot as plt

df = pd.read_csv('phishing_sample.csv')  # replace with actual dataset
df = df.dropna()
X = df.drop(columns=['label'])
y = df['label']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)
clf = RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print(classification_report(y_test, y_pred, digits=4))
cm = confusion_matrix(y_test, y_pred)
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues'); plt.show()

Open Colab (placeholder)

Baseline registry (recommended)

For each dataset, record: baseline model, hyperparameters, seed, environment (requirements.txt or conda), split used, and metrics.

Example baselines: RandomForest, XGBoost, simple CNN for screenshots
Include interpretability notebook (SHAP) for analyst validation

Reproducibility checklist

Data provenance & checksum
Code commit hash and notebook
Environment spec (pip/conda), random seeds
Evaluation script and train/test split

Recommended learning & experiment roadmap

Foundations (Week 0)

Python, pandas, basic ML (scikit-learn), probability & statistics
Hands-on: run a simple classifier on small tabular data

Supervised & tabular (Week 1–2)

Logistic Regression, RandomForest, XGBoost; hyperparameter tuning
Hands-on: RandomForest baseline on phishing dataset

Deep learning & sequences (Week 3–4)

MLP, CNN, RNN, Transformer basics; embeddings for tokens
Hands-on: token embeddings for URLs or LSTM autoencoder for sequences

Unsupervised & anomaly (Week 5)

Isolation Forest, Autoencoders, clustering
Hands-on: anomaly detection on NetFlow samples

Explainability & robustness (Week 6)

SHAP, LIME, calibration, adversarial testing
Hands-on: SHAP explanations for top false positives/negatives

Operationalization

Experiment tracking, containerization, docs, evaluation pipeline
Deploy prototypes: simple API + alerting playbook

Starter experiments & project ideas

Phishing baseline — end-to-end

RandomForest baseline on the Kaggle Phishing Websites dataset. Includes: data prep, training, evaluation (confusion matrix, precision/recall/F1), feature importance, and a SHAP explainability notebook.

phishing

Lead: Your Team

Run the full experiment, reproduce results, and explore error cases. Recommended for a single 90–120 min session.

Repo (GitHub) Notebook (Colab)

Get started (guide)

URL token embeddings

Train token embeddings from a large URL corpus; use embeddings in a downstream classifier.

Malware micro-benchmark

Compare static-feature RandomForest vs small CNN on byte n-grams for detection and family classification.

Network anomaly prototype

LSTM autoencoder on NetFlow; evaluate detection rate and false positives.

Filter: Add / Submit

Featured

All research items

Ethics, privacy & responsible research

When using security data, minimize PII, document provenance and licensing, and consider adversarial misuse. Use privacy-preserving techniques when collaborating across organizations.

Data anonymization checklist
Responsible disclosure and sharing agreements
Adversarial robustness and model abuse considerations

Collaborate & contact

To contribute: fork the hub repo, add dataset or notebook, register reproducibility artifacts and open a PR. For research collaboration email: research@onesecurity.tech.

Email the research team