HACKING PROJECT · mobile · Advanced
Mobile Malware YARA Rule Generator
Deliver a tool that automatically generates and validates discriminative YARA rules from mobile malware samples.
Writing effective YARA rules for mobile malware is manual and expertise-heavy, and hand-crafted rules are often too broad (false positives) or too narrow (miss variants). As Android malware families mutate rapidly, detection teams cannot keep pace by hand. There is a gap in tooling that ingests malicious APK samples and automatically proposes high-quality, family-generalizing YARA rules.
**What you'll build**
Build a generator that ingests a labeled set of malicious (and benign) APKs, extracts candidate features (DEX opcode n-grams, unique strings, method signatures, embedded certs, native-lib patterns, manifest fingerprints), and uses frequency/entropy analysis plus optional ML clustering to select discriminative indicators. It then emits ready-to-use YARA rules with ATT&CK-tagged metadata and validates each rule against benign and malicious corpora to report precision/recall. Provide a CLI and a review UI so analysts can tune before deployment.
**Objective** — Deliver a tool that automatically generates and validates discriminative YARA rules from mobile malware samples.
**Expected outcome** — A rule-generator that outputs validated, ATT&CK-tagged YARA rules with precision/recall metrics against a test corpus.
**Difficulty** — Advanced — feature engineering, clustering, and rule validation over noisy mobile samples is research-adjacent.
**Primary category** — Detection Engineering; Secondary = Malware Analysis, AI/ML Security
**Security domains** — Primary = Threat Intelligence; Secondary = Malware Analysis, Blue Team, AI Security
**Technical focus** — Automated YARA generation, feature extraction, DEX opcode n-grams, string/entropy analysis, ML clustering, rule validation (precision/recall), detection-as-code, malware family fingerprinting, IOC generation
**Core technologies** — Languages: Python, YARA; Frameworks/Libraries: yara-python, androguard, scikit-learn, numpy/pandas; Security Tools: YARA, apktool, jadx; Cloud/Infra: Docker, batch compute; AI/Data: AndroZoo/MalwareBazaar corpora, clustering/feature-selection models; Databases: PostgreSQL/Elasticsearch for features and rule metrics
**Where it's valued** — AV/EDR vendors, threat-intel providers, MSSPs, national CERTs, mobile security teams
**Careers it builds toward** — Detection Engineer, Threat Intelligence Analyst, Malware Researcher, Security Data Scientist
**Framework alignment**
• MITRE ATT&CK: ATT&CK for Mobile — rule metadata tags techniques such as Obfuscated Files (T1406), Download New Code at Runtime (T1407), Input Capture (T1417); MITRE ATLAS relevant if adversarial evasion of the ML component is modeled
• NIST CSF 2.0: Identify (IOC/feature cataloging), Detect (automated detection authoring), Respond (rapid variant coverage)
• OWASP: MASVS-CODE, MASVS-RESILIENCE (context for obfuscation features)
• CIS Controls v8: 10 (Malware Defenses), 8 (Audit Log Management)
**Innovation** — Research-Level Project — automated, validated, ML-assisted YARA generation for mobile is at the edge of current tooling and publishable/productizable. Scores: Innovation 8/10, Startup 6/10, Enterprise-Readiness 5/10, Patent = Possible.