Java with spring boot
Initilaised springboot with dependecncies :
Spring Web → REST endpoints
Spring Data JPA → ORM for PostgreSQL
PostgreSQL Driver → Connect to Postgres
Spring Security → JWT auth for admin APIs
Spring Boot DevTools → hot reload for dev
Lombok → reduces boilerplate for entities/controllers
Spring Data Redis → for threshold rules & counters
Validation → @Valid annotations for request payloads
Spring Boot Actuator → health endpoints, metrics
Configured application.yml including:
database
jpa
redis config
jwt security
Using docker:
installed docker
setup postgres for docker
docker run --name security-postgres \ -e POSTGRES_PASSWORD=ENTERNAME \ -e POSTGRES_DB=ENTERDB \ -p 5432:5432 \ -d postgres:15
setup redis
docker run --name security-redis -p 6379:6379 -d redis:7
test: docker exec -it security-redis redis-cli ping
Add docker compose to project root to persist data, stop and run container
1 — High-level goal
A backend service that accepts security events (failed logins, privilege escalations, anomalous activity),
stores them, runs detection rules, and raises alerts (email/webhook/console). It should support configurable rules, audit trails,
and an analyst interface (API) to triage alerts.
2 — Core components
Ingest API (Spring Boot REST) — receive events from apps/agents.
Event Store — persistent storage for raw events & metadata (Postgres / Elasticsearch).
Rule Engine — evaluates events against rules (thresholds, anomaly detectors).
Alert Service — creates alerts, deduplicates, escalates (email/webhook).
Analytics / Reporting — aggregation endpoints (failed logins per user/IP).
Admin API / UI — manage rules, view alerts, mark triage status.
Worker Queue — processes events/rules asynchronously (Kafka/RabbitMQ or Spring TaskExecutor).
Cache — Redis for counts / temporary state (sliding windows, rate limits).
3 — Tech stack
Language/Framework: Java 17 + Spring Boot
Persistence: PostgreSQL for relational data, optionally Elasticsearch for log search/analytics
Queue: RabbitMQ or Apache Kafka (Kafka if high throughput)
Cache: Redis (sliding windows, rate counters)
Auth: JWT + role-based access for admin APIs
Docs: OpenAPI/Swagger
CI/CD: GitHub Actions (build, test, docker image)
Deployment: Docker + ECS / EKS / Heroku / GCP Cloud Run
Optional: Prometheus + Grafana for metrics
4 — Example REST endpoints
Authentication assumed via JWT.
Authentication/Admin:
POST /auth/login — returns JWT
GET /users — admin list users
Event ingestion (public/internal tokens):
POST /events — ingest one or batch of events (accepts json array)
body: { "source":"webapp","events":[{ "type":"LOGIN_FAIL","user":"alice","ip":"1.2.3.4","timestamp":"..." , "details":{...}}] }
Alerts & triage:
GET /alerts — list alerts with filters (status, severity, time range)
GET /alerts/{id} — alert details (events attached)
POST /alerts/{id}/comment — analyst comment
POST /alerts/{id}/close — close/resolve alert
Rules:
GET /rules — list
POST /rules — create (json config)
PUT /rules/{id} — update/enable/disable
Reporting:
GET /reports/failed-logins?from=...&to=...&groupBy=ip,user
GET /reports/top-suspicious-ips?limit=50
6 — Detection rule examples
A. Simple threshold rule
Rule config (JSON):
{
"type": "RATE",
"description": "Lockout after 5 failed logins in 10 minutes",
"window_seconds": 600,
"threshold": 5,
"group_by": ["user","ip"],
"action": "CREATE_ALERT"
}
Implementation outline:
On each LOGIN_FAIL event, increment counter in Redis key fail:{group} with TTL 600s.
If counter > threshold → emit alert (include recent event ids).
B. Geo anomaly (impossible travel)
Idea: detect two successful logins for same user from geographically distant IPs within a small time window.
Use GeoIP to map IP → lat/lon.
For each LOGIN_SUCCESS, check last success for user: compute distance and time delta.
If distance/time implies travel > plausible speed (e.g., > 500 km/h), flag as anomaly.
C. Unusual privilege change
If an account receives a role elevation (USER → ADMIN) and dev or service account performed it, create HIGH severity alert
and require manual review.
D. Behavioral baseline (advanced)
Build per-user baseline of typical API call volumes or endpoints accessed (rolling window average).
Use z-score: z = (current_count - mean)/std. If z > 3, alert.
Could implement with simple statistical windowing initially; later swap to ML.
7 — Alerting & deduplication
Deduplication strategy: alerts tied to a rule + group (e.g., rule "5 fails" + user=alice). If identical alert exists OPEN,
increment a counter and append events instead of creating a new alert.
Escalation: if an alert remains OPEN for > X minutes and severity >= HIGH, send SMS/email to on-call.
Delivery: SMTP for email, webhooks for integrations (Slack, PagerDuty).
8 — Storage & querying choices
Postgres works for relational storage and moderate query loads; use jsonb for flexible event details and indexing (GIN).
If you want powerful search and aggregation (text search over logs), add Elasticsearch to index events;
use it for GET /events/search.
Use Redis for ephemeral counters/sliding window algorithms.
9 — Scaling & resilience
Use asynchronous ingestion: POST /events writes to queue; workers consume and process rules.
This decouples spikes.
Partition events by time (DB partitioning) for long-term retention.
Add backpressure and rate-limiting on ingestion endpoint.
Implement retention policies: raw events older than X months archived to S3 or removed.
10 — Security considerations (you must be able to discuss these)
Secure ingestion: require API keys + TLS. Authenticate agents.
Input validation & deny-listing for event payloads to avoid injection.
Protect stored sensitive data: if events include PII, encrypt details column at rest
(Transparent DB encryption or application-level encryption).
RBAC: only certain roles can view sensitive fields or create rules.
Audit all admin actions in audit_log.
Secrets management: use environment variables or a secret manager; do not commit keys.
11 — Testing strategy
Unit tests for rule logic (JUnit + Mockito). Example: simulate N failed logins and assert alert created.
Integration tests: start Spring context + embedded DB, simulate ingestion → rule evaluation → alert.
E2E tests: dry-run with a small dataset and assert reports endpoints produce expected aggregates.
12 — Observability & metrics
Expose Prometheus metrics: events_ingested_total, alerts_created_total, rule_eval_latency_seconds.
Log structured messages (JSON) for pipeline debugging.
Add health endpoints and readiness/liveness probes.
13 — Minimal viable implementation (MVP) — what to build first
Spring Boot app with JWT auth and a simple admin user.
POST /events to store events in Postgres.
One rule (failed logins threshold) implemented synchronously (no queue) using Redis counters.
GET /alerts to view alerts.
Basic unit/integration tests and Dockerfile.
14 — Progressive enhancements Outline
Week 1: Project scaffolding, auth, events table, ingest endpoint, simple UI.
Week 2: Implement threshold rule (Redis), create alerts storing, list alerts endpoint, unit tests.
Week 3: Add rule management APIs, deduplication, basic email alerts (SMTP).
Week 4: Add GeoIP anomaly detection, integrate free GeoIP DB, build report endpoints.
Week 5: Introduce queue (RabbitMQ) to async rule processing; add Redis metrics.
Week 6: Add audit logs, RBAC, more rule types; Dockerize; add CI pipeline.
Week 7–8: Add Elasticsearch , advanced behavioral detectors, deploy to cloud, prepare demo & README.