How can you make your IDPS architecture more resilient against zero-day exploits?

Automation is the key for updates. Now with an AI/LLM tools, you can automate what was not possible before, including automated virtual patch creation, RegEx, signatures, etc.

From decades in cybersecurity, I've witnessed how rapid the evolution of cyber threats can be. Comparing IDPS updates to a flu shot is apt; just as new strains emerge, so do new malware variations. Regular updates are essential, but the timeliness is critical. I've seen incidents where mere hours made a difference in vulnerability exposure. Beyond signatures, re-evaluating configurations in line with organizational shifts ensures the IDPS evolves with the threat landscape and internal changes. An outdated IDPS, like an old antivirus, becomes more of a placebo than a protection.

Focus on data protocols and encodings. You need new layer all the time, when the new protocol is coming to your attack surface. For instance, Wallarm started in 2016 with the first nested JSON encodings and then added gRPC, GraphQL and other protocols.

Layering defenses has been a keystone of cybersecurity since its inception. Think of it as the age-old strategy of castle fortification - moats, walls, and guards, each offering a unique line of protection. By diversifying the IDPS landscape, one doesn't just improve detection rates but also redundancy. Each layer, when strategically positioned, complements the other. Anomaly in one layer can be cross-verified with data from another, reducing false positives and increasing the reliability of detections.

Use the MITRE framework to study attack patterns and then create and apply rulesets of attacks that can be detected/prevented by IDS/IPS.

Anomaly is a great term, however, defining what's NORMAL is crucial. I recommend to start focusing on normal behavior first to avoid false positives and get much better security at the end.

In my experience, relying solely on signature-based detection is like navigating the vast seas with an old map; you're bound to miss new islands (threats). Anomaly-based detection, in contrast, acts like a radar, scanning for anything unusual. It's an essential tool in the arsenal, especially when dealing with polymorphic malware or advanced persistent threats that evolve to evade traditional detections. However, like any radar, fine-tuning is crucial. Too sensitive, and it cries wolf; too lax, and threats slip by.

Integrating AI and ML into IDPS products, organizations can benefit improving detection, enhanced threat detection, faster incident response, and improved overall security posture. Several key applications of AI and ML in IDPS features includes Behavioral Analysis, Anomaly Detection, Threat Intelligence Integration, Predictive Analytics, User and Entity Behavior, Real-Time Threat Detection and Response and Adaptive Learning. These features can help evolve the detection/response and identification of the unknown.

AI and ML in IDPS is akin to having a seasoned detective with an eidetic memory at the helm 24/7. Leveraging these technologies enables an IDPS to discern patterns and threats that would be imperceptible to human analysts in real-time. From my "experience", the predictive capabilities of AI-backed systems can preemptively flag potentially malicious activities, offering a proactive defense stance. Yet, like any tool, its effectiveness hinges on the quality and diversity of data it's trained on

Imagine having a network of informants across the globe, feeding you intelligence; that's what an integrated threat intelligence system feels like. Coupled with sandboxing, it's as if you're allowing potential threats into a controlled environment just to study them, akin to controlled detonations. These tools not only offer insight into active threats but also offer foresight, adapting defenses based on emerging threat vectors even before they target your organization.

To enhance your #IDPS architecture's resilience against #ZeroDay exploits: 1. Use behavioral analysis to spot abnormal behavior. 2. Incorporate machine learning to detect new threat patterns. 3. Implement sandboxing for safe testing of suspicious code. 4. Stay updated with threat intelligence. 5. Monitor user and entity behavior with #UEBA tools. These measures will help your #IntrusionDetection system better defend against unknown threats. #Cybersecurity