A Proposed Framework for Integrating Stack Path Identification and Encryption Informed by Machine Learning as a Spoofing Defense Mechanism
Anne Mwende Kaluvu · IOSR Journal of Computer Engineering · 2014
Spoofing attacks have been terrorizing the information world for decades; so many methodologies have been formulated to attempt the eradication of these attacks.This study elaborates on a proposed framework for integrating StackPi and Encryption informed by Machine learning as spoofing defense methodologies.IP Spoofing is one of the major tools used by hackers in the internet to mount spoofing attacks and has been difficult to eradicate.Stack Pi uses Path Identification markings to differentiate between spoofed packets and the legitimate packets and in addition encryption is used to apply proper authentication measures that can enhance the speed of detection and prevention of IP spoofed packet.Machine learning incorporated in this framework to address the short comings of StackPi-IP filtering method and thereby increasing its efficiency.The integration of these three methodologies makes an ideal mechanism for eradicating spoof attacks.