Neuromorphic computing for real-time adaptive penetration testing: analysis of human intuition in AI-dominated work space
Department of Computer Science, School of Mathematics and Computing, Kampala International University-Uganda.
Research Article
World Journal of Advanced Research and Reviews, 2024, 24(03), 2038-2051
Article DOI: 10.30574/wjarr.2024.24.3.3860
Publication history:
Abstract:
This research investigates the revolutionary integration of neuromorphic computing with human intuition in the domain of real-time adaptive penetration testing, addressing the critical challenges facing modern cybersecurity in AI-dominated workspaces. The study presents a novel approach that combines the parallel processing capabilities of neuromorphic architectures with the nuanced decision-making abilities of human security experts, resulting in a hybrid system that significantly enhances threat detection and response capabilities. Our research implements a sophisticated neuromorphic computing model featuring 1024 input neurons, 2048 hidden layer neurons, and 512 output neurons, utilizing modified leaky integrate-and-fire algorithms for spike processing. The system incorporates real-time adaptive mechanisms that enable dynamic threat modeling and immediate response generation, while simultaneously learning from human expert intuition through a carefully designed collaborative interface. This architecture demonstrates remarkable improvements in both processing efficiency and threat detection accuracy, achieving a 94.7% true positive rate while maintaining an exceptionally low 2.3% false positive rate. The experimental methodology encompassed extensive testing across a diverse range of attack scenarios, including advanced persistent threats, zero-day vulnerabilities, and sophisticated social engineering attacks. The test environment comprised 500 virtual nodes distributed across multiple security zones, providing a realistic platform for comprehensive system evaluation. Performance metrics revealed significant improvements over traditional approaches, including an 89% success rate in adapting to previously unseen attack patterns and a 76% reduction in decision-making time for complex threats. Quantitative analysis demonstrates the system's superior capabilities in real-time threat detection and response, with average detection latencies of 1.2 milliseconds and consistent performance maintaining up to 100,000 concurrent connections. Qualitative assessment of human-AI synergy, conducted with 50 experienced penetration testers, revealed that 92% reported enhanced decision-making capabilities, while 95% experienced reduced cognitive load during complex scenarios. The research contributes significantly to the field by establishing a new paradigm for adaptive penetration testing that effectively combines neuromorphic computing efficiency with human intuitive expertise. The findings demonstrate that this integration not only enhances detection and response capabilities but also provides a more sustainable approach to cybersecurity in increasingly complex technological environments. Furthermore, the study opens new avenues for research in human-AI collaboration within cybersecurity, suggesting promising directions for future development in adaptive security systems. The implications of this research extend beyond immediate cybersecurity applications, offering insights into the broader field of human-AI collaboration in critical decision-making scenarios. The study also addresses important ethical considerations regarding AI autonomy in security operations, providing guidelines for responsible implementation of AI-driven security solutions while maintaining essential human oversight.
Keywords:
Neuromorphic Computing; Penetration Testing; Cybersecurity; Human-AI Collaboration; Adaptive Systems; Real-Time Processing; Security Automation; Threat Detection; Artificial Intelligence; Machine Learning
Full text article in PDF:
Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0