Shell Scripting and Automation Questions

I’d parse into two layers: a stable normalized object and a raw-extensions map for everything unknown. That keeps forward compatibility while still enforcing the fields I care about.

from dataclasses import dataclass, field

@dataclass
class SecurityEvent:
    host: str
    event_type: str
    timestamp: str
    raw_fields: dict = field(default_factory=dict)


def parse_event(doc):
    if not isinstance(doc, dict) or "host" not in doc or "event_type" not in doc:
        raise ValueError("invalid record")
    stable = SecurityEvent(
        host=doc["host"],
        event_type=doc["event_type"],
        timestamp=doc.get("timestamp", ""),
        raw_fields={k: v for k, v in doc.items() if k not in {"host", "event_type", "timestamp"}}
    )
    return stable

The key design choice is to validate only the required subset, not reject new fields. Unknown nested content is preserved in raw_fields so downstream systems can reprocess it later.

I’d reject records only if the stable subset is missing or malformed. For compatibility, I’d version the parser behavior through tests and keep fixtures for old and new schemas. A good safety net is round-trip testing: parse, serialize, and confirm unknown fields survive unchanged.

That approach makes the code resilient to schema drift, which is common in security telemetry pipelines.

I’d separate the solution into parsing, normalization, deduplication, and counting so each part is easy to test.

from collections import Counter
from datetime import datetime, timedelta, timezone


def top_failed_ips(lines):
    cutoff = datetime.now(timezone.utc) - timedelta(days=1)
    counts = Counter()
    seen = set()

    for line in lines:
        parsed = parse_line(line)
        if not parsed:
            continue
        key = (parsed["ip"], parsed["ts"], parsed["user"])
        if key in seen:
            continue
        seen.add(key)
        if parsed["event"] == "failed_login" and parsed["ts"] >= cutoff:
            counts[parsed["ip"]] += 1

    return counts.most_common(3)

I’d keep parse_line() isolated and tolerant of multiple timestamp formats, returning None for malformed lines instead of throwing. That keeps the pipeline resilient in real log data.

For testing, I’d cover:

• Mixed timestamp formats and timezone offsets.
• Duplicate events with the same IP/user/timestamp.
• Malformed lines that should be skipped.
• Boundary cases around the 24-hour cutoff.

In a cybersecurity context, maintainability means predictable parsing rules, small functions, and clear failure handling so the script can be trusted during an incident.

I’d model the baseline and current state as recursive dictionaries and compute a structured diff tree. That makes nested sections easy to extend and supports both human and machine output.

from dataclasses import dataclass, asdict

@dataclass
class DiffItem:
    path: str
    status: str   # added, removed, changed, same
    baseline: object = None
    current: object = None


def compare(path, base, cur, out):
    if isinstance(base, dict) and isinstance(cur, dict):
        keys = set(base) | set(cur)
        for k in sorted(keys):
            compare(f"{path}.{k}" if path else k, base.get(k), cur.get(k), out)
    elif base != cur:
        status = "added" if base is None else "removed" if cur is None else "changed"
        out.append(DiffItem(path, status, base, cur))

Why this model works

• Recursive traversal handles nested sections naturally
• A DiffItem schema keeps reporting consistent
• Default values can be normalized before compare, so expected defaults don’t show as drift

Output

• Human-readable summary: grouped by path, with severity labels
• JSON report: [{path, status, baseline, current}] for CI ingestion or SIEM forwarding

Extendability

• Add per-field comparators for CIDRs, lists, or version strings
• Add ignore rules for approved exceptions
• Add severity mapping for security-critical settings like MFA, logging, or shell timeout

This gives a clean drift engine that can grow with the baseline schema without rewriting the diff logic.

I’d build the pipeline as a streaming transform over the raw log file so memory stays flat even for millions of lines.

Structure

1. Read one event at a time.
2. Parse JSON and validate the stable schema.
3. Enrich from in-memory lookup tables: asset owner, geo, threat intel.
4. Write the enriched record immediately to the output sink.
5. Attach missing-lookups metadata instead of dropping the event.

def enrich_event(event, owners, geos, ti):
    return {
        **event,
        "asset_owner": owners.get(event.get("host_ip"), "unknown"),
        "geolocation": geos.get(event.get("src_ip"), {}),
        "threat_labels": ti.get(event.get("indicator"), []),
        "enrichment_status": "partial" if "unknown" in event.values() else "ok",
    }

For partial failures, I’d never fail the whole batch for one missing lookup. Instead, mark fields as unknown, track error counts, and keep processing.

Correctness checks:

• Sample-based golden tests comparing input to expected output.
• Coverage for missing keys, malformed lines, and duplicate records.
• Metrics for parse failures, enrichment misses, and output row count matching input row count.

This design is simple, memory-efficient, and resilient to imperfect reference data.

I’d treat each alert as a node in an evolving incident graph. Two alerts belong to the same incident if they share enough entities, occur close in time, and have compatible severity or tactic patterns.

Algorithm

• Normalize entities like host, user, IP, process hash, and rule name.
• For each new alert, compare it against recent incident candidates in a time window.
• Score overlap on shared entities and time proximity.
• Merge into the highest-scoring incident if the score exceeds a threshold; otherwise create a new one.
• Assign a stable incident ID using the first alert’s ID plus a deterministic dedup key.

Heuristics

• High precision: require strong entity overlap and short time gaps.
• Higher recall: allow weaker matches when severity is high or the alerts come from trusted correlated tools.
• Penalize generic entities like common IPs or shared service accounts.
• Use decay so old context matters less than recent activity.

To balance recall vs precision, I’d tune thresholds with analyst feedback and measure merge error rates. In security operations, over-grouping hides incidents, while under-grouping floods analysts, so I’d start conservatively and only relax rules where we have evidence.