Inteligence Artificial implementation

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Roberto Blandino IBW

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Mar 17, 2022
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Some one has any idea to implement IA for checking headers and learn what is spam or what's not?

I believe there is any way to get the incoming mails and analize them through a local IA database and machine learning.
 
CRCinAU said:
You mean like Bayes filtering?
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Good afternoon.

No, Bayes is not AI. Bayes is a classification system that uses statistical techniques for email filtering. It typically relies on a set of characteristic words to identify spam emails, which is a common approach in text classification.

Bayes does not go beyond what it was designed for. However, integrating AI with Bayes could enhance its capabilities, allowing it to detect and correlate even new emails with similar spam-like characteristics, even spot suspicious bejavior in real time.

For example, Bayes is useful, but if the content of a spam email is completely changed, these messages may bypass the spam filter. An AI system could help by flagging such suspicious emails—sending them to quarantine or taking another desired action—while also linking them to previously identified spam patterns recognized by the Bayes algorithm.
 
CRCinAU said:
So.... Bayes with more inefficiency?
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Greetings!

I believe that AI-enhanced spam filtering isn't just "Bayes with more inefficiency"; it’s about addressing the limitations of static statistical models. While Bayes is effective for known patterns, modern spam evolves (e.g., adversarial attacks, polymorphic content).

Then knowing that AI can:
  • Detect novel threats: ML models (e.g., NLP transformers) analyze semantic patterns, not just keywords—catching impersonation, phishing contexts, or obfuscated links that Bayes might miss.
  • Adapt continuously: Unlike rule-based systems, AI retrains on new data, improving accuracy over time without manual updates.
  • Prioritize precision: Yes, AI introduces latency, but strategic deployment (e.g., pre-filtering with Bayes, then AI for suspicious cases) balances speed and efficacy.
Tools like Spam Filter AI or GuardianAI focus on this hybrid approach. The "lag" is a trade-off for blocking attacks that would otherwise bypass traditional filters. For enterprises, that’s a net gain in security.

I am trying to do a self lab for test it, but is not simple, i am considering to start with a small-scale AI model (e.g., Scikit-learn or TensorFlow) to analyze headers/body text, then compare false-positive rates vs. Bayes alone. The data often speaks for itself.

Inefficiency is a matter of perspective. The latency introduced by AI analysis is subjective—it depends on priorities. For users who prioritize speed above all, any delay might seem inefficient. But for those who demand zero spam and ironclad security, the trade-off is justified.

Think of it like airport security: You could skip baggage checks to board faster, but few would risk a bomb slipping through. Similarly, AI-driven filtering acts as a precision checkpoint—adding milliseconds to scan for sophisticated threats (impersonation, zero-day phishing) that slip past traditional filters.

The real question isn’t speed vs. inefficiency; it’s speed vs. risk tolerance. And in enterprise environments, where a single breach costs far more than a slight delay, ‘efficiency’ means stopping threats before they reach the inbox.

Define Your Goal, and "that becomes your Security Compass".

The "inefficiency" debate vanishes when you align tools with priorities. Want zero spam? AI’s lag is a feature, not a bug. Need instant delivery? Simpler filters suffice... but know the risks.

So I'm just wondering if anyone has dabbled in this topic.
 
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