Research output: Contribution to journal › Article
The security of resources in a corporate network is always important to the organization. For this reason, different techniques, such as firewall and intrusion detection systems, are important. Years of long research have resulted in the contribution of different advancements in these techniques. Artificial intelligence, machine learning techniques, soft computing techniques, and bio-inspired techniques have been efficient in detecting advanced network attacks. However, very often, different new attacks are most successful in breaching these detection techniques. This very reason has been a motivation for us to explore the biological aspects and its defense mechanisms for designing a secure network model. After much study, we have identified that plants have a very well-established and evolved detection and a response mechanism to pathogens. In this paper, we have proposed and implemented a network attack detection and a response model inspired by plants. It is a three-layered model in analogy to the three-layer defense mechanism of plants to pathogens. We have further tested the proposed model to different network attacks and have compared the results to the open-source intrusion detection system, Snort. The experimental results also establish that the model is competent to detect and trigger an automated response whenever required.