Initializing Security Systems
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Choose the deployment model that fits your infrastructure needs
Traffic Processing Capacity
Than competing hardware appliances
Optimized for parallel packet processing
Guaranteed delivery under full load
AI-powered rule optimization built-in
Standard 1U/2U form factors available
Ideal For:
Enterprise Networks, Data Centers, High-Traffic Environments
Traffic Processing Capacity
Than competing software solutions
Efficient CPU utilization across cores
Reliable processing without data loss
AI-powered rule optimization included
Deploy on AWS, Azure, GCP, or private cloud
Ideal For:
Cloud Environments, Virtual Infrastructure, Hybrid Deployments
Deploy NeuroSmash in the configuration that best fits your infrastructure
Standalone appliance deployment
Data center integration
Virtual and cloud-native
Enterprise-grade protection combining artificial intelligence with proven security principles.
Deep learning models trained on millions of attack patterns identify sophisticated threats that signature-based systems miss. Continuous learning adapts to evolving attack techniques without manual rule updates.
Process 10+ Gbps of network traffic per instance with sub-millisecond latency. Distributed architecture scales horizontally to support enterprise-wide deployments without bottlenecks.
Behavioral context engine reduces false positives by 87% compared to traditional IDS. Smart correlation eliminates alert fatigue while ensuring zero tolerance for genuine threats.
Inline deployment blocks threats before they reach critical systems. Stream processing architecture analyzes traffic in real-time without packet loss or degradation.
Interactive dashboards surface actionable insights from terabytes of security data. Automated threat hunting identifies attack campaigns across your entire infrastructure.
Combines signature matching, behavioral analysis, protocol validation, and ML-based anomaly detection. Defense-in-depth architecture ensures threats don't slip through single-layer gaps.
NeuroSmash IDPS vs Typical IDS/IPS Solutions
| Feature | NeuroSmash IDPS | Typical IDS/IPS |
|---|---|---|
Threat Detection Breadth Multi-layered detection using AI, behavioral analysis, and signature matching vs basic signature-only detection | ★★★★★ | ★★★★★ |
Network Throughput & Scalability 10+ Gbps per instance with horizontal scaling vs limited throughput requiring hardware upgrades | ★★★★★ | ★★★★★ |
Zero-day Threat Protection Neural network-based anomaly detection identifies unknown threats vs reliance on signature updates | ★★★★★ | ★★★★★ |
False Positive/Negative Reduction Behavioral context and ML reduce false alarms by 87% vs high false positive rates requiring manual tuning | ★★★★★ | ★★★★★ |
Threat Intelligence Integration Real-time feed correlation with 50+ threat intel sources vs limited or manual threat feed integration | ★★★★★ | ★★★★★ |
Reporting & Dashboards Interactive dashboards with customizable views and automated compliance reporting vs basic logging | ★★★★★ | ★★★★★ |
Custom Rule Creation (GUI) Intuitive graphical interface for creating custom detection rules vs command-line configuration | ★★★★★ | ★★★★★ |
Cloud & Hybrid Deployments Native cloud support with flexible deployment across on-premises, cloud, and hybrid environments vs limited cloud compatibility | ★★★★★ | ★★★★★ |
Pre-Kernel Packet Filtering Pre-kernel filtering blocks threats before reaching the operating system vs kernel-level processing | ★★★★★ | ★★★★★ |
IP-to-Geolocation Mapping Built-in geolocation intelligence for threat source identification vs manual lookup or third-party integration | ★★★★★ | ★★★★★ |
Front-End Packet Filtering (NIC/ring-buffer level) Hardware-accelerated packet filtering at NIC level for maximum performance vs software-only processing | ★★★★★ | ★★★★★ |
Enterprise-grade performance designed for modern infrastructures
Versatile protection for diverse infrastructure scenarios
Technical answers to common questions
Traditional systems rely heavily on signature-based detection, which misses zero-day attacks and generates high false positive rates. NeuroSmash combines signatures with behavioral analysis and neural networks trained on millions of attack patterns. This multi-layered approach detects unknown threats while reducing false positives by 87%.
Each NeuroSmash instance processes 10+ Gbps with sub-millisecond latency in inline mode. The distributed architecture scales horizontally, allowing you to deploy multiple instances behind load balancers for 100+ Gbps deployments. We've tested installations handling peak loads exceeding 50 Gbps sustained traffic.
Yes. While signature-based detection catches known threats, our neural network models identify anomalous behavior patterns characteristic of exploit attempts. The behavioral analysis engine establishes baselines for normal traffic and flags deviations indicative of zero-day attacks, even without specific signatures.
NeuroSmash supports Syslog, SNMP, and REST API for alert forwarding to SIEMs. We integrate with 50+ threat intelligence feeds and can consume custom indicators. Deployment options include inline (active blocking), passive TAP monitoring, or hybrid modes. Configuration via CLI, API, or web interface.
NeuroSmash analyzes 100+ protocols including HTTP/S, DNS, SSH, RDP, SMB, database protocols, and custom applications. Detection covers OWASP Top 10, network-based attacks (DDoS, port scanning, ARP poisoning), malware C2 communications, lateral movement, data exfiltration, and protocol-specific exploits.
NeuroSmash ships with pre-trained models and signature sets that work out-of-the-box. The behavioral analysis engine auto-learns your network baseline during a 7-day training period. Most deployments require minimal tuning—typically just allowing known legitimate traffic that triggers initial alerts. The ML models continuously adapt without manual intervention.