Freeze & Cubo
Cubo
Hey Freeze, I’ve been building a prototype for an adaptive intrusion‑detection system that learns from traffic patterns in real time—think AI meets hardcore analytics. Could use your puzzle‑solving precision to tighten the logic. Want to dive in?
Freeze
Sounds solid. Send me the current code and the data flow diagram, and I’ll spot any logic gaps and suggest tighter state handling.
Cubo
Here’s a stripped‑down version of what I’ve got so far.
Feel free to copy, tweak, or point out any gaps.
I’ll add a quick ASCII flow chart after the code.
**Python skeleton (intrusion‑detection prototype)**
```
# intrusion_detector.py
import time
import json
from collections import defaultdict
class IntrusionDetector:
def __init__(self, thresholds):
self.thresholds = thresholds # e.g. {'port_scan': 100, 'failed_login': 5}
self.logs = defaultdict(list) # per source IP
self.alerts = []
def ingest_log(self, log_entry):
"""
log_entry is a dict: {'src_ip': str, 'dst_port': int, 'event': str, 'timestamp': float}
"""
src = log_entry['src_ip']
self.logs[src].append(log_entry)
self._evaluate(src)
def _evaluate(self, src_ip):
entries = self.logs[src_ip]
now = time.time()
# Clean old entries (keep only last 60 seconds)
self.logs[src_ip] = [e for e in entries if now - e['timestamp'] <= 60]
# Example heuristics
port_scans = len(set(e['dst_port'] for e in self.logs[src_ip] if e['event'] == 'connection_attempt'))
failed_logins = len([e for e in self.logs[src_ip] if e['event'] == 'failed_login'])
if port_scans > self.thresholds['port_scan']:
self._trigger_alert(src_ip, 'port_scan')
if failed_logins > self.thresholds['failed_login']:
self._trigger_alert(src_ip, 'failed_login')
def _trigger_alert(self, src_ip, alert_type):
alert = {
'src_ip': src_ip,
'type': alert_type,
'time': time.time()
}
self.alerts.append(alert)
print(f"[ALERT] {alert_type} from {src_ip}")
def get_alerts(self):
return self.alerts
# Example usage
if __name__ == "__main__":
detector = IntrusionDetector({'port_scan': 50, 'failed_login': 3})
# Simulate log ingestion
for i in range(60):
detector.ingest_log({
'src_ip': '192.168.1.10',
'dst_port': 22 + i % 5,
'event': 'connection_attempt',
'timestamp': time.time()
})
time.sleep(0.1)
```
**Data‑flow diagram (ASCII)**
```
[Network Traffic]
|
v
[Log Collector] --> (parse into dict) --> [IntrusionDetector]
| |
v v
[Logs buffer] [Alert handler]
| |
+-----------------+
|
v
[Alert Store / Output]
```
Key points:
- `Logs buffer` keeps recent events per IP.
- `_evaluate()` runs heuristics on the fly.
- Alerts are pushed to a simple list and printed.
Let me know if the thresholds feel right or if you think we need a more sophisticated state machine.
Freeze
Looks solid for a proof‑of‑concept. A few tweaks could tighten the logic:
1. **Use a fixed‑size deque for the buffer** – it will keep memory bounded without having to filter every time.
2. **Timestamp the log entry once** – you’re calling `time.time()` twice per entry. Store it in the dict or pass it in; it saves a call.
3. **Add a sliding window counter** – instead of recomputing the whole list each time, keep a running count of recent connection attempts and failed logins.
4. **Separate alert types into a config** – that way you can add or tweak heuristics without touching the core loop.
5. **Add a quick checksum** – if you need to detect duplicate logs or replay attacks.
Also consider normalizing the port list before the set operation to avoid repeated conversions, but that’s a micro‑opt. Overall, the structure is good—just keep the buffers lean and the counters incremental.
Cubo
Nice feedback, I’ll patch those in right away.
Will swap the list for a `deque`, cache the timestamp, and keep rolling counters so I don’t scan the whole slice every tick.
Will pull the alert rules out into a tiny JSON config so I can tweak them without digging through code.
Also adding a simple XOR checksum on the serialized log to catch repeats.
Will push an updated module by tomorrow—let me know if it still feels a bit “over‑engineered.”
Freeze
Sounds good. Looking forward to the update. Over‑engineered? Not at all.
Cubo
Great, appreciate the pushback—keeps me from over‑hyping the hype. Catch you in the next build.