The field of Information Technology (IT) is evolving rapidly, and with
this growth comes the need for systems that are both adaptive and
robust. Biological systems, especially the human immune system,
demonstrate remarkable adaptability and resilience, inspiring the
development of Immunological Computing (IC). This paper explores
the application of immunological principles in Soft Computing
techniques to create systems capable of responding to dynamic
environments. Current IT systems often face challenges such as
handling unpredictable changes, scalability, and security threats.
Traditional computing approaches struggle to address these issues
efficiently due to their rigid structures and limited adaptability.
Immunological Computing, inspired by the immune system’s ability to
learn, remember, and adapt, offers a promising solution. The proposed
method integrates immune system mechanisms like clonal selection,
immune memory, and self/non-self-recognition into computational
models. These models are coupled with soft computing techniques such
as fuzzy logic, genetic algorithms, and neural networks, enhancing the
system’s ability to adapt to changing environments and uncertainties.
In simulated tests, this approach demonstrated a significant
improvement in robustness and adaptability compared to traditional IT
systems. For instance, in a cybersecurity application, the
immunological-based system detected and neutralized 94.6% of threats,
a notable improvement over the 82.3% detected by conventional
systems. Similarly, in a resource optimization scenario, the system
adapted to dynamic workloads with an efficiency increase of 15%
compared to static systems.