NETWORK INTRUSION DETECTION SYSTEM USING MACHINE LEARNING TECHNIQUES
Budget: $30 – $250 USD
Project requirements:
• The "Network Intrusion Detection System” must monitor the network of computers (use virtual machines here to perform suspicious activity), looking out for potentially risky actions include obtaining confidential information or corrupting/hacking networks.
• Dataset needs to be trained - KDD Cup 1999 dataset
• Use Wireshark to read the network packets
• For the evaluation of IDS, System needs to be implemented using Random Forest machine learning technique.
• System should detect the intrusion and send an email alert to the network administrator
• Track the data and store it in the MS SQL database
• Create a single web page and add a button which should start/stop the IDS system developed.
• Display the trend of malicious activities detected by the system in graphical format in the web page
• 4 to 5 types of attacks has to be performed and same should be captured by the IDS system developed.
• The "Network Intrusion Detection System” must monitor the network of computers (use virtual machines here to perform suspicious activity), looking out for potentially risky actions include obtaining confidential information or corrupting/hacking networks.
• Dataset needs to be trained - KDD Cup 1999 dataset
• Use Wireshark to read the network packets
• For the evaluation of IDS, System needs to be implemented using Random Forest machine learning technique.
• System should detect the intrusion and send an email alert to the network administrator
• Track the data and store it in the MS SQL database
• Create a single web page and add a button which should start/stop the IDS system developed.
• Display the trend of malicious activities detected by the system in graphical format in the web page
• 4 to 5 types of attacks has to be performed and same should be captured by the IDS system developed.