Multi-frame Signature-cum Anomaly-based Intrusion Detection Systems (MSAIDS) to Protect Privacy of Users over Mobile Collaborative Learning (MCL)
This work addresses privacy and security issues for users in mobile collaborative learning frameworks, though it appears incremental as it builds on existing IDS with new rules and algorithms.
The paper tackled the problem of rogue DHCP servers in mobile collaborative learning by introducing MSAIDS, which detects and blocks malicious attacks to protect user privacy and network security, validated through simulation.
The rogue DHCP is unauthorized server that releases the incorrect IP address to users and sniffs the traffic illegally. The contribution specially provides privacy to users and enhances the security aspects of mobile supported collaborative framework (MSCF) explained in [24].The paper introduces multi-frame signature-cum anomaly-based intrusion detection systems (MSAIDS) supported with novel algorithms and inclusion of new rules in existing IDS. The major target of contribution is to detect the malicious attacks and blocks the illegal activities of rogue DHCP server. This innovative security mechanism reinforces the confidence of users, protects network from illicit intervention and restore the privacy of users. Finally, the paper validates the idea through simulation and compares the findings with known existing techniques.