Revisiting Parasitic Computing: Ethical and Technical Dimensions in Resource Optimization - Godfrey Oise; Clement Nwabuokei; Richard Igbunu; Prosper Ejenarhome (Vokasi UNESA Bulletin of Engineering, Technology and Applied Science, 2025)
Parasitic computing is a provocative concept enabling one system to offload computational tasks to remote hosts without explicit consent by exploiting communication protocols such as TCP/IP. While initially demonstrated as a conceptual hack, its implications for distributed computing, ethics, and resource optimization remain underexplored in modern contexts. This study revisits the original parasitic computing model, focusing on operational feasibility, technical efficiency, and ethical boundaries. We implement a Python-based simulation that encodes logical operations (AND, OR) into TCP packets by manipulating checksum fields—a core mechanism of the parasitic approach. We conducted over 6,000 packet transmissions across various network latency conditions using loopback and LAN environments to measure success rates, response times, and failure thresholds. Results show that logical operations can succeed under low-latency conditions with over 94% accuracy, but performance degrades sharply under higher round-trip times, dropping below 70%. These findings suggest parasitic computing may be technically viable within tightly controlled environments but face significant limitations in broader network applications. The researchers critically examine ethical considerations, emphasizing the risks of unauthorized computation, resource exploitation, and potential security breaches. This study contributes a reproducible methodology and empirical data, offering a renewed perspective on parasitic computing’s technical boundaries and future potential. It further calls for responsible experimentation and proposes hybrid models combining parasitic techniques with legitimate distributed computing frameworks and new safeguards to detect and mitigate unintended abuses. The paper proposes directions for improving protocol resilience and computational fairness in open networks.