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Applications of spiking neural P systems in cybersecurity

  • Mihail-Iulian Pleṣa,
  • Marian Gheorghe,
  • Florentin Ipate,
  • Gexiang Zhang

摘要

Spiking neural P systems are third-generation neural networks that are much more energy efficient than the current ones. In this paper, we investigate for the first time the possibility of using spiking neural P systems to solve cybersecurity-related problems. We proposed a new architecture called cyber spiking neural P systems (Cyber-SN P systems for short), which is designed especially for cybersecurity data and problems. We trained multiple Cyber-SN P systems to detect malware on the Android platform, phishing websites, and spam e-mails. We show through experiments that these networks can efficiently classify cybersecurity-related data with much fewer training epochs than perceptron-based artificial neural networks.