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Deep Learning for Intrusion Detection

📑 5 slides 👁 65 views 📅 4/28/2026
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Introduction to Cybersecurity

Cybersecurity protects systems from digital attacks like malware and hacking.

Introduction to Cybersecurity
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What is an IDS?

  • An Intrusion Detection System (IDS) monitors networks for malicious activity.
  • Examples include detecting DDoS attacks, malware infections, and unauthorized logins.
  • Traditional IDS rely on known attack patterns, limiting their effectiveness.
What is an IDS?
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Proposed Deep Learning Solution

  • Uses CNN for spatial pattern detection in network traffic data.
  • LSTM analyzes temporal patterns to detect evolving threats over time.
  • Combining CNN and LSTM improves accuracy and reduces false alarms.
Proposed Deep Learning Solution
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System Architecture

  • Network traffic data is collected and preprocessed for analysis.
  • Deep learning model processes data to detect anomalies in real-time.
  • Alerts are generated for suspicious activities, enabling quick response.
System Architecture
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Conclusion & Future Scope

  • Deep learning enhances IDS with real-time detection and high accuracy.
  • Future improvements include cloud integration and explainable AI models.
  • This system reduces manual effort and improves cybersecurity defenses.
Conclusion & Future Scope
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