Intrusion Detection System using Machine Learning
An Intelligent Network Intrusion Detection System Based on Multi-Modal Support Vector Machines presents a statistical machine learning approach for network intrusion detection using Support Vector Machines (SVM). The network traffic is modeled into connections based on protocols at various layers and fed as input to the SVM model.
Network intrusion detection is an increasingly critical component of cybersecurity defense systems. With growing cyber threats, organizations need robust intrusion detection to identify anomalous traffic, block malicious attacks in real-time, and prevent security breaches. This report demonstrates applying machine learning techniques to classify network traffic data for accurate and automated intrusion detection.
Intrusion detection systems (IDS) monitor network activity to detect violations and threats. Traditional IDS relied on rule-based approaches with signatures to detect known attacks. However, these fail to generalize for new threats. Modern IDS leverage machine learning to train models on network data that can detect both existing and zero-day attacks. This data-driven approach builds intelligent systems that continually improve as they see more data.
Network Intrusion Detection System using ML
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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