| Autor: |
Chirag Ganguli, Shishir Kumar Shandilya, Maryna Nehrey, Myroslav Havryliuk |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Systems, Vol 11, Iss 1, p 27 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2079-8954 |
| DOI: |
10.3390/systems11010027 |
| Popis: |
With the significant growth of the cyber environment over recent years, defensive mechanisms against adversaries have become an important step in maintaining online safety. The adaptive defense mechanism is an evolving approach that, when combined with nature-inspired algorithms, allows users to effectively run a series of artificial intelligence-driven tests on their customized networks to detect normal and under attack behavior of the nodes or machines attached to the network. This includes a detailed analysis of the difference in the throughput, end-to-end delay, and packet delivery ratio of the nodes before and after an attack. In this paper, we compare the behavior and fitness of the nodes when nodes under a simulated attack are altered, aiding several nature-inspired cyber security-based adaptive defense mechanism approaches and achieving clear experimental results. The simulation results show the effectiveness of the fitness of the nodes and their differences through a specially crafted metric value defined using the network performance statistics and the actual throughput difference of the attacked node before and after the attack. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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