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PRODUCT CODE : NSF-3100A-I
Protection against new malware and zero-day attacks is by far the most critical, as these threats have not been included in any signature database. Moreover, these advanced threats are typically in the possession of highly sophisticated and well-resourced threat actors, who are more capable of causing significant damage. Sangfor NSF-3100A-I effectively addresses these threats by implementing artificial intelligence in all of its security innovations, including Engine Zero, the Web Intelligent & Semantic Engine for NGWAF, Botnet Detection, and more. For example, Engine Zero on Sangfor NSF-3100A-I is continuously trained on tens of millions of malware samples using advanced machine-learning algorithms to learn the evolving characteristics of malware. This has enabled Sangfor NSF-3100A-I to recognize potential new malware and zero-day attacks with a significant degree of accuracy.
When Sangfor NSF-3100A-I detects an unusual outbound connection from a server connected to the internet, it sends the suspicious DNS address to Neural-X for verification. If threat intelligence has classified this particular DNS as a known command & control (C2) server, it’s likely the server has been compromised. Sangfor NSF-3100A-I can be programmed to block these C2 communications so that no further damage is caused and alert security operators for further investigation and processing.
Traditional detection technologies mainly include signature-based detection (hashes, virus signatures, etc.), rule matching, virtual execution, and sandboxing. The threat detection capability of these technologies improves from signature-based detection to sandboxing. However, performance generally decreases and costs increase the more advanced the detection technology. Sangfor NSF-3100A-I Engine Zero's AI model can automatically learn and extract features without human intervention. The model evolves in the cloud, with continuously improving detection and automation capabilities. However, traditional detection technologies require experts to manually extract virus fingerprints and signatures, which is not only costly but may also result in missed detections. “New” viruses may have been around for a long time before traditional anti-virus vendors update the virus database.
