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Adaptive Network Sustainability and Defense Based on Artificial Bees Colony Optimization Algorithm for Nature Inspired Cyber Security.

Authors :
Ganguli, Chirag
Shandilya, Shishir Kumar
Gregus, Michal
Basystiuk, Oleh
Source :
Computer Systems Science & Engineering; 2024, Vol. 48 Issue 3, p739-758, 20p
Publication Year :
2024

Abstract

Cyber Defense is becoming a major issue for every organization to keep business continuity intact. The presented paper explores the effectiveness of a meta-heuristic optimization algorithm-Artificial Bees Colony Algorithm (ABC) as an Nature Inspired Cyber Security mechanism to achieve adaptive defense. It experiments on the Denial- Of-Service attack scenarios which involves limiting the traffic flow for each node. Businesses today have adapted their service distribution models to include the use of the Internet, allowing them to effectively manage and interact with their customer data. This shift has created an increased reliance on online services to store vast amounts of confidential customer data, meaning any disruption or outage of these services could be disastrous for the business, leaving them without the knowledge to serve their customers. Adversaries can exploit such an event to gain unauthorized access to the confidential data of the customers. The proposed algorithm utilizes an Adaptive Defense approach to continuously select nodes that could present characteristics of a probable malicious entity. For any changes in network parameters, the cluster of nodes is selected in the prepared solution set as a probable malicious node and the traffic rate with the ratio of packet delivery is managed with respect to the properties of normal nodes to deliver a disaster recovery plan for potential businesses. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02676192
Volume :
48
Issue :
3
Database :
Complementary Index
Journal :
Computer Systems Science & Engineering
Publication Type :
Academic Journal
Accession number :
177472552
Full Text :
https://doi.org/10.32604/csse.2024.042607