Kernel Anomaly Detection Engine for Zero-Day Attack Mitigation
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Solution Overview
Problem
Conventional cyber-security systems are inadequate in detecting zero-day attacks, which are unknown and rapidly evolving, leading to high false negatives and false positives, and often rely on manual rule updates and remote server communication, resulting in delayed detection and increased risk due to latency.
Innovation Solution
An anomaly detection engine operating in the kernel of an operating system, utilizing machine learning or AI to recognize normal behavior and identify deviations, allowing for near-zero latency detection and termination of suspicious processes, independent of threat characteristics, similar to a biological immune system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional reactive cyber protection systems use malware signatures and attack patterns to identify threats, then known viruses and malware can be blocked, but zero-day attacks and unknown threats cannot be detected
Solution Approach 1:
Instead of trying to identify what is malicious by matching known threat signatures, the system inverts the approach by learning what normal behavior looks like and identifying anything that deviates from this baseline as potentially malicious. This allows detection of zero-day attacks and unknown threats without requiring prior knowledge of their signatures or patterns.
Solution Approach 2:
The system performs self-learning by automatically observing and establishing a baseline of normal device behavior without requiring manual rule updates or external threat intelligence feeds. The machine learning model continuously adapts to the specific device's behavior patterns, enabling autonomous detection of anomalies that may indicate attacks.
2Reliability
If EDR systems rely on manual rule updates and remote server communication to identify threats, then detection capabilities can be maintained, but response time increases due to latency
Solution Approach 1:
The system eliminates dependency on remote servers for detection by performing all analysis locally on the device. The machine learning model runs autonomously, continuously monitoring device behavior and detecting threats in real-time without requiring external communication, thereby eliminating latency associated with cloud-based EDR systems.
Solution Approach 2:
The system performs preliminary learning of normal behavior patterns continuously in the background, so when an anomaly occurs, detection can happen immediately without requiring time to analyze or update rules. The baseline is already established and ready for real-time comparison against actual device behavior.
3Adaptability or versatility
If machine learning approaches only utilize information from attacker space to predict next attacks, then attack prediction can be attempted, but accuracy is impossible due to infinite possibilities
Solution Approach 1:
Instead of trying to predict future attacks by analyzing attacker behavior patterns (which has infinite possibilities), the system inverts the approach by analyzing the victim's normal behavior and detecting deviations. This transforms an impossible prediction problem into a feasible anomaly detection problem with measurable accuracy.
Data Source
AI summary
One example method includes data protection operations including cyber security operations, threat detection operations, and other security operations. Normal device behavior is learned based on data collected by an anomaly detection engine operating in a kernel. The normal data is used to train a machine learning model. Threats are detected when the machine learning model indicates that new data points deviate from normal device behavior. Associated processes are stopped. This allows threats to be detected based on normal behavior rather than on unknown threat behavior.