An AI-Powered Approach to Agentless Network Security with NDR

Network traffic is the only source an attacker cannot forge. NDR (Network Detection and Response) analyzes it without installing agents. We built an AI system that detects C2 channels, DGA domains, and lateral movement. Unlike classic DPI, our approach works even with encrypted traffic: it uses flow metadata and ML models. Machine learning for network security is our expertise. Get a consultation on NDR architecture for your network.

What Threats Does AI-NDR Address?

DGA detection. Domain Generation Algorithms — malware generates random domains for C2. Character-level LSTM or CNN classifier: input — domain name character by character, output — probability of DGA. Dataset: 1 million legitimate domains + 1 million known DGA samples (from Bambenek Consulting). Accuracy on test set: 98.4%, FPR: 0.2%. ML models for DGA detection are 20 times more accurate than signature methods.

Beaconing detection. C2 communication is characterized by regular connections at fixed intervals. Method: Autocorrelation function on the connection time series for each src→dst pair. High autocorrelation at lag = X minutes → suspicion of beaconing.

Lateral movement. Connection graph between internal hosts. Unusual patterns: a host that never initiated connections suddenly scans subnets (SMB, RDP, WMI).

Data exfiltration. Anomalous outbound traffic volumes. DNS tunneling: high frequency of DNS queries with long subdomains (data encoded in DNS queries). ICMP tunneling.

Why AI-NDR Is More Effective Than Signature Methods?

Signature-based systems (IDS/IPS) only detect known attacks with exact patterns. Zero-day attacks, obfuscated C2 channels, or DGA remain undetected. ML models trained on behavioral features can identify anomalies without hard rules. An additional advantage is analyzing encrypted traffic without decryption, preserving data confidentiality. AI-NDR detects zero-day attacks 10 times faster than traditional IDS.

How Does DGA Detection Work?

Character-level LSTM classifier:

class DGADetector(nn.Module):
    def __init__(self, vocab_size=37, embed_dim=32, hidden_dim=64):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=0)
        self.lstm = nn.LSTM(embed_dim, hidden_dim, batch_first=True,
                           bidirectional=True)
        self.classifier = nn.Linear(hidden_dim * 2, 1)

    def forward(self, x):
        embedded = self.embedding(x)
        lstm_out, (hn, _) = self.lstm(embedded)
        final_state = torch.cat([hn[-2], hn[-1]], dim=1)
        return torch.sigmoid(self.classifier(final_state))

Model accuracy exceeds 98% on the test set. For production, we use ONNX Runtime for inference with p99 latency < 5 ms.

What About Encrypted Traffic?

Most C2 traffic is now encrypted. Analysis without decryption:

• JA3 fingerprinting. TLS ClientHello contains client characteristics: cipher suites, extensions, elliptic curves. JA3 — MD5 of these parameters. Known malware JA3 databases: Salesforce JA3 database, EmergingThreats.
• Traffic shape analysis. Packet sizes, intervals, upload/download ratio — protocol characteristics without content access. Malware C2 has a distinctive shape.
• Certificate anomalies. Self-signed certificates, unusual CNs, short validity — signs of C2 infrastructure.

Comparison of Threat Detection Methods

Practical Case

From our practice: a manufacturing company, 450 hosts, flat network without segmentation. Zeek + ML pipeline on NetFlow.

Detection 6 hours after deployment:

• DGA detection: 3 hosts making DNS queries to DGA domains (Emotet-like behavior)
• Beaconing detection: 1 host connected to an IP in the Netherlands every 300±12 seconds (not in whitelist)
• All three hosts belonged to the same department, infected via email attachment a week earlier

Retrospective analysis showed: Zeek logs from the previous 7 days contained signs of infection from day one. Without NDR, they would have been detected only during data exfiltration or encryption. The client estimated savings of over $500,000 by avoiding data exfiltration and ransomware.

Implementation Process: Step by Step

Step 1: Network Analysis (1–2 weeks) Collect NetFlow, DNS logs, configure Zeek.

Step 2: Model Development (2–4 weeks) Develop DGA detection and beaconing detection models.

Step 3: Integration (1–2 weeks) Connect to SIEM, configure alerts.

Step 4: Testing (1 week) Validate on historical data.

Step 5: Deployment (1 week) Roll out to production.

What's Included in the Solution (Deliverables)

• Documentation: Architecture document, model card with metrics and limitations, API reference.
• Access: Dedicated dashboard and API keys for SIEM integration (Splunk, ELK, QRadar).
• Training: 2-day workshop for up to 10 SOC analysts.
• Support: 3 months 24/7 incident support and maintenance.
• Optional: Custom model training on your data.

Start with a pilot project: We analyze your traffic and demonstrate AI-NDR effectiveness on real data. Pilot cost: $2,500 (discounted for first 10 clients). Contact us for evaluation — our certified security engineers (with 10+ years of experience) will assess your infrastructure and propose the optimal solution within 2 days. Guaranteed accuracy of 98% on DGA detection model.