HACKING PROJECT · forensics · Advanced
Ransomware Detection System
Detect ransomware activity through behavioral and anomaly-based monitoring before widespread encryption completes.
Ransomware causes some of the most damaging and fast-moving incidents, and signature-only defenses miss novel or fileless variants until encryption is underway. Defenders need behavior-based detection that catches mass file modification, shadow-copy deletion, and encryption patterns early enough to interrupt. The gap between infection and detection directly determines recoverability.
**What you'll build**
Build a host-based behavioral detection system that monitors filesystem activity, entropy shifts, rapid mass rename/encrypt patterns, and known precursor actions (e.g., volume shadow-copy deletion), scoring behavior to raise high-fidelity alerts and optionally trigger containment. Combine heuristic and ML-based anomaly detection, validated against ransomware behavior in an isolated lab. Findings map to ATT&CK and feed response playbooks.
**Objective** — Detect ransomware activity through behavioral and anomaly-based monitoring before widespread encryption completes.
**Expected outcome** — A behavioral ransomware-detection agent with alerting, ATT&CK mappings, and a tested response/containment workflow.
**Difficulty** — Advanced — real-time filesystem monitoring, low-false-positive tuning, and ML anomaly detection require significant defensive and data skill.
**Primary category** — SOC / Detection Engineering; Secondary = DFIR, Endpoint Security, Threat Hunting
**Security domains** — Primary = Blue Team; Secondary = Incident Response, Malware Analysis, Security Research, Threat Intelligence
**Technical focus** — Behavioral detection, File-entropy analysis, Filesystem monitoring, Anomaly/ML detection, Endpoint telemetry, Alerting/scoring, Automated containment, ATT&CK mapping, Response playbooks
**Core technologies** — Languages: Python, PowerShell/C for host hooks; Frameworks/Libraries: watchdog, scikit-learn, pandas, Sysmon config, ETW/inotify; Security Tools: Sysmon, Sigma, Velociraptor/OSQuery, YARA, isolated ransomware-behavior samples/simulators; Cloud/Infra: isolated detonation VM, optional SIEM (Elastic/Wazuh); AI/Data: labeled behavioral datasets, feature engineering; Databases: Elasticsearch/SQLite for event storage
**Where it's valued** — Enterprises across all sectors, MDR/MSSP, healthcare, finance, critical infrastructure, EDR vendors
**Careers it builds toward** — SOC Analyst, Detection Engineer, Incident Responder, Security Researcher, Endpoint Security Engineer
**Framework alignment**
• MITRE ATT&CK: Impact — Data Encrypted for Impact (T1486), Inhibit System Recovery (T1490); Defense Evasion — File Deletion (T1070.004); Discovery — File and Directory Discovery (T1083)
• NIST CSF 2.0: Detect (DE.AE, DE.CM), Respond (RS.AN, RS.MI), Recover (RC.RP)
• OWASP: N/A
• CIS Controls v8: 8 (Audit Log Management), 10 (Malware Defenses), 11 (Data Recovery), 13 (Network Monitoring and Defense)
**Innovation** — Improved Existing Solution — behavioral ransomware detection exists in EDR, but a transparent, tunable, ML-augmented agent with response hooks has both learning and product value. Scores: Innovation 7/10, Startup 7/10, Enterprise-Readiness 7/10, Patent = Possible.