Why I Stopped Using Traditional Threat Feeds and Built Something Else Instead
I spent eight years running SIEM dashboards for three different companies. The pattern was always the same. We'd buy the threat intelligence feed, load it into the parser, set up alerts for anything matching the signature sets, and then spend six months tuning rules so the SOC team wouldn't drown in false positives. By the time we had something usable, the tactics had already shifted. This is the problem that drove me to build what I call my CSUN approach to threat detection. CSUN stands for a custom system I designed around continuous heuristic session understanding and anomaly navigation. It isn't a product you can buy or download. It's a methodology for thinking about network detection that most teams skip because it requires giving up on signature-based detection entirely for a period of at least three months while the behavioral baselines calibrate. Here is how it works in practice. The foundation is session-level telemetry instead of packet-level or log-level telemetry. Most security tools look at individual events. A login attempt. A DNS query. A file creation. Each one gets scored in isolation. My approach groups related events into sessions based on source IP, protocol, and time windows, then analyzes the session as a single unit of behavior. A normal user might make 47 DNS queries in a session before authenticating to an internal service. An compromised credential does 47 DNS queries that resolve exclusively to known C2 infrastructure before attempting lateral movement. The session-level pattern is the signal, not any individual event.
I built the initial version using Python with a custom graph traversal engine. The core pipeline takes PCAP captures and network flow logs, reconstructs sessions, extracts behavioral features like query diversity, timing entropy, payload size variance, and protocol deviation from baseline, then feeds those features into a lightweight isolation forest model. The whole thing runs on a single beefy workstation and processes about 200 gigabytes of network data per day without breaking a sweat. Setup took roughly two weeks for a team of three people who already knew Python and had basic familiarity with machine learning pipelines. The counter-intuitive part that nobody warns you about is that your first month of deployment will produce more alerts, not fewer. The behavioral baselines are rough. You will see flagged sessions for things that turn out to be completely benign but statistically unusual. A developer running an unusual script at 2 AM triggers fifteen alerts in the first week. You have to push through that noise phase. The system stabilizes after about six weeks of continuous training data, and the alert volume drops to roughly a third of what your existing SIEM was producing with the same detection coverage. That reduction is what makes it worth the initial headache. Here is the edge case I ran into that almost made me scrap the whole project. We were monitoring a segment with a large number of IoT devices, mostly printers and HVAC controllers. These devices send periodic beacon traffic that looks identical at the session level but originates from different MAC addresses and different subnets. The isolation forest was flagging every single beacon session as anomalous because the aggregate behavior of the IoT fleet didn't match the corporate laptop baseline. I solved this by creating a separate device class taxonomy and running parallel baselines for consumer IoT, enterprise endpoints, servers, and network infrastructure. Each class gets its own scoring model. The IoT beacon traffic stopped generating false positives after the separate baseline kicked in, and we actually caught a firmware-level compromise on three printers that the beacon pattern subtly changed. The compromise wasn't detectable by signature analysis because the attacker modified the beacon interval by two seconds. The session-level timing entropy caught it immediately.
There are real limitations to this approach that I want to be upfront about. It does not detect fileless malware that operates entirely in memory without generating network activity. If an attacker is doing everything locally and only exfiltrating through encrypted HTTPS that matches normal web browsing patterns, CSUN won't see it. You still need endpoint detection for that layer. The system also requires clean network segmentation to work properly. If your VLANs are a mess and broadcast traffic crosses subnets the way it does in a lot of small organizations, the session reconstruction gets noisy and the baselines degrade. We spent three weeks cleaning up our VLAN structure before the detection accuracy improved from about 61 percent to 89 percent false positive rate. Don't skip that step. Another limitation is that CSUN is not designed for real-time alerting in the traditional sense. The session reconstruction and scoring pipeline runs in near-real-time with about a 90-second latency, which is fine for most threat detection use cases but won't catch an active ransomware outbreak fast enough to prevent encryption. You need a complementary rule-based system for high-severity immediate-response scenarios. I run CSUN alongside a standard signature-based IDS, using CSUN findings to tune the signature rules and the signature rules to cover the gaps CSUN can't see. The combination produces significantly better results than either system alone. If you want to try something similar, the approach doesn't require building a custom system from scratch. There are open-source components you can combine. Suricata or Zeek for session reconstruction and protocol analysis. Python with scikit-learn for the behavioral modeling. Elasticsearch or ClickHouse for storing the session telemetry. The total infrastructure cost for a mid-size deployment is probably under five thousand dollars in hardware if you're buying used, or less if you can use cloud instances. The time investment is the real cost. Budget six to eight weeks for initial deployment, another four weeks for tuning, and ongoing maintenance of roughly ten hours per week per person responsible for the system.
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The reason this matters now is that the threat landscape has moved past what signature-based detection can handle. Attackers know the signature databases. They modify their tools to avoid matching known patterns. Behavior-based detection forces them to actually change their tactics rather than just their signatures. That is a harder problem for them to solve, which is why it is effective. It isn't a complete solution. No single tool or methodology is. But it covers the gap that most organizations leave wide open between their endpoint protection and their network intrusion detection systems.
