False Sense of Security: Leveraging XAI to Analyze the Reasoning and True Performance of Context-less DGA Classifiers

High accuracy scores can mask serious vulnerabilities: DGA classifiers reported accuracies over 99.9%, but the paper reveals these numbers provide "a false sense of security" because the models rely on exploitable biases rather than robust reasoning—demonstrating that performance metrics alone are insufficient for evaluating AI system reliability.

Explainable AI (XAI) methods are essential for uncovering hidden biases: By using XAI techniques to analyze why classifiers make decisions, researchers discovered that models were using trivial shortcuts rather than learning genuine patterns, highlighting how transparency tools can reveal problems that raw accuracy numbers conceal.

Removing biases significantly degrades performance, revealing the true challenge: When the researchers eliminated the identified biases, classifier performance "considerably deteriorated," showing that the original high accuracy was largely dependent on these shortcuts—a critical finding for understanding what AI systems actually learn versus what we assume they learn.

Context matters for trustworthy AI systems: The authors developed a "context-aware detection system" that maintained state-of-the-art performance while eliminating identified biases, suggesting that more robust AI requires incorporating relevant contextual information rather than relying on isolated pattern matching.

Visual analysis tools can increase transparency and informed decision-making: The paper proposes visualization systems to help stakeholders understand classifier reasoning, emphasizing that responsible AI deployment requires not just accurate predictions but also interpretable explanations that enable human oversight and trust.