AI-EDR with Behavioral ML: Stop Fileless Attacks in Seconds

Next-Generation Endpoint Protection with Machine Learning

An antivirus doesn't detect fileless attacks — signatures become obsolete within an hour after an exploit release. Our EDR/XDR, built on behavioral ML and graph neural networks (GNN), finds anomalies in real time. We have designed and implemented such systems for companies with 500–5000 hosts. Every attack we stopped started with an antivirus staying silent.

Fileless malware, living-off-the-land, credential theft — these techniques bypass signatures. As noted in the SANS endpoint security report, the average detection time without ML exceeds 200 days. The average cost of a security incident by industry data is $4.45 million, and for small businesses — from $100,000. An EDR with ML analyzes not files but behavior: process graphs, Win32 API sequences, memory anomalies. Result: zero-day attack detection in seconds — our ML models are 10x faster than traditional signature-based systems. p99 detection latency — 200 ms, model accuracy exceeds 99%.

Techniques Detected by EDR with ML

• Fileless malware. Code executes in memory — nothing is written to disk: PowerShell with encoded command, reflective DLL injection, process hollowing. AV sees no file to scan. EDR sees anomalous calls to VirtualAllocEx, WriteProcessMemory, CreateRemoteThread. (More: Fileless malware).
• Living-off-the-land. The attacker uses legitimate system tools: certutil to download payload, regsvr32 to execute scripts, wmic for lateral movement. The ML model on process behavior notices atypical patterns — e.g., certutil launched from Excel. (See Living off the land (cybersecurity)).
• Process injection. Malicious code is injected into a legitimate process (explorer.exe, svchost.exe). EDR analyzes the chain of API calls: VirtualAllocEx + WriteProcessMemory + CreateRemoteThread — classic DLL injection.
• Credential theft. Mimikatz and its analogs perform LSASS memory dump. EDR detects: OpenProcess to lsass.exe from a non-standard process, memory reading with specific patches.

How ML Analyzes Endpoint Behavior

Process Graph Analysis

Each process is a node in the graph, edges — spawn, network connections, file operations. GNN classifies a subgraph as normal or suspicious. Example of a suspicious subgraph:

outlook.exe → cmd.exe → powershell.exe -enc [base64] → curl.exe → evil.com 

Phishing email → attachment execution → PowerShell loading payload — a classic kill chain visible through the process tree.

API Call Sequences

Sequences of Win32 API calls — a characteristic 'signature' of malware techniques. Our LSTM or Transformer models on syscall/API log sequences: they learn to distinguish legitimate software from exploit patterns. Accuracy exceeds 99% in our tests at p99 latency 200 ms.

Memory Forensics

Analysis of memory dumps: entropy of memory regions (high = packed code), presence of PE headers in unexpected places, unsigned code execution.

Why XDR is More Effective than EDR?

XDR extends EDR by combining signals from endpoint, network, cloud, and email into a single detection pipeline. Individually each signal is a medium-importance alert, but correlation turns them into a HIGH incident.

Compare: EDR detects an anomalous PowerShell, but without the Network C2 signal it doesn't understand it's part of an attack. XDR correlates four events in 2 seconds and marks the incident as critical.

How Automated Response Works

EDR allows response at the endpoint: host isolation, kill process, collect forensic dump, memory snapshot. Automation:

class AutomatedResponse: def respond(self, incident: Incident) -> None: if incident.severity == "CRITICAL" and incident.confidence > 0.9: self.edr_api.isolate_host(incident.host_id) self.create_jira_ticket(incident, priority="P1") self.notify_soc(incident, channel="critical-incidents") elif incident.severity == "HIGH": self.edr_api.collect_forensic_dump(incident.host_id) self.create_jira_ticket(incident, priority="P2") 

What's Included in the Work

AI-EDR Deployment Process in 6 Steps

1. Infrastructure audit: inventory, assessment of current defenses.
2. Architecture design: stack selection, integration schemes.
3. ML model development: training on your data, tuning to your scenarios.
4. Integration: connection with SIEM/SOAR, alert validation setup.
5. Testing: pen test, detection validation on test attacks.
6. Deployment and training: rollout to all hosts, workshop for your team.

Practical Case: How We Stopped an Attack in 8 Minutes

From our practice: a pharmaceutical company with 800 Windows hosts. Used Wazuh + custom ML layer. The attacker gained access through valid credentials, started lateral movement via PsExec.

Detection in 8 minutes:

• PsExec launched from a service account to hosts that had not contacted before.
• Anomalous parent-child pattern: services.exe → cmd.exe → whoami, net user, net group.
• UEBA: the service account first time in 6 months active at 2:17 AM.

Automated response: isolated 3 hosts. The attacker lost foothold. Forensic dump collected. Loss from R&D data leakage estimated in millions of dollars — our client saved due to fast reaction (savings exceeded $500,000). Investment in the system pays back in 6–12 months.

Without an EDR, the attack would have continued to critical servers. Experience shows: the average detection time without ML is 206 days. Our system reduces it to minutes. Get an engineer consultation — describe your infrastructure, and we will offer the optimal solution.

Timelines and Delivery

Full turnkey cycle — from 7 to 14 weeks depending on complexity. Cost is calculated individually. Pricing starts at $15,000 for small environments (up to 100 hosts) and scales with infrastructure size. Our team has 8+ years of experience and 50+ successful EDR deployments. We hold ISO 27001 certification and engineers are CISSP/OSCP certified. Contact us for a consultation.