The rapid expansion of the internet and communication industries has led to a proliferation of networks and data, accompanied by an increase in security threats and network intrusions. Malicious intruders pose significant risks to the confidentiality, integrity, and availability of networks. To mitigate these risks, monitoring network traffic and preventing intrusions are critical, achievable through a Network Intrusion Detection System (NIDS). Leveraging Machine Learning (ML), ML-based Network Intrusion Detection Systems (NIDS) have emerged as powerful tools to detect network attacks, providing effective, network-wide detection capabilities.
In the ever-evolving landscape of connected devices, developing and operating network intrusion detection systems face continuous challenges due to the constant evolution of attacker tactics and methods. Consequently, ML techniques are increasingly employed in NIDS to enhance detection accuracy and adaptability.
To install the MultipleNIDS package, follow these steps:
cd MultipleNIDS
pip install pandas numpy sklearn streamlit- Comprehensive Network Monitoring:
- Real-time analysis and monitoring of network traffic.
- ML-Based Detection:
- Utilizes advanced machine learning algorithms for precise intrusion detection.
- Scalability:
- Designed to handle the expansion of network devices efficiently.
- Adaptability:
- Continuously evolves to counteract new and sophisticated attack strategies.