Artificial Malware Immunization Based on Dynamically Assigned Sense of Self
Computer malwares (e.g., botnets, rootkits and spyware) are one of the most serious threats to all computers and networks. Most malwares conduct their malicious actions via hijacking the control flow of the infected system or program. Therefore, it is critically important to protect the mission critical systems from malicious control flows. Inspired by the self-nonself discrimination in natural immune system, this research explores a new direction in building the artificial malware immune systems. Most existing models of self of the protected program or system are passive reflection of the existing being (e.g., system call sequence) of the protected program or system. Instead of passively reflecting the existing being of the protected program, the authors actively assign a unique mark to the protected program or system.