Cybersecurity

How can we counter increasingly sophisticated attacks ?

Attackers are perpetually evolving by utilizing advanced techniques and refining their tools and tactics, targeting both internal and external access points of organizations. The primary challenge is achieving reliable and robust detection, with high accuracy and low false positives.

Solution

Through advanced AI and machine learning methods, we can identify correlations between suspicious information flows, predict attackers’ strategies, and break their kill chain early on. ExtremeXP offers an early multistage real-time attack detection system that evolves in line with adversary Tactics, Techniques, and Procedures (TTP).

How can we bridge the semantic gap between low-level alerts and a high-level view of attacks ?

The key challenge lies in the semantic gap between low-level alerts and the high-level view of the attacker’s behavior. Security teams often face an overwhelming amount of data without a clear way to identify meaningful patterns.

Solution

We leverage the MITRE ATT&CK framework to align attacker behaviors with specific patterns, transforming technical alerts into a clear strategic view. This allows for effective identification of TTPs (Tactics, Techniques, and Procedures) and reduces false positives. This approach enhances response time, relying on rigorous behavioral analysis.

How can we reduce false positives while maintaining real-time detection?

Attack detection needs to be precise enough to avoid generating false alerts, which can overwhelm cybersecurity teams. A solution must be fast while also reducing the noise of false positives.

Solution

By leveraging a predictive analysis architecture and explainable AI, ExtremeXP provides a multistage detection system that adapts to evolving attacker tactics. This enables real-time responses to attacks and filters out false positives, while maintaining optimal security levels.

ExtremeXP’s architecture for advanced cybersecurity detection

ExtremeXP adopts an innovative approach, combining advanced technologies such as behavioral analysis, machine learning, and explainable AI. This architecture intelligently correlates low-level alerts with patterns identified in the MITRE ATT&CK framework, enabling more accurate and refined attack detection.