ADVANCING INFORMATION TECHNOLOGY WITH IMMUNOLOGICAL COMPUTING – SOFT COMPUTING TECHNIQUES FOR ADAPTIVE AND ROBUST SYSTEMS

Abstract

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.