{"title":"Exploiting self-evolutionary strategies of components for Dynamic Heterogeneous Redundancy","authors":"","doi":"10.1016/j.compeleceng.2024.109756","DOIUrl":null,"url":null,"abstract":"<div><div>The network security situation is increasingly serious. Traditional security equipment like firewalls and intrusion detection systems cannot prevent unknown risks due to their passive nature. Dynamic Heterogeneous Redundancy (DHR) actively defends by switching the attack surface and confusing attackers, becoming essential in modern network defense. However, existing DHR approaches based on scheduling algorithms struggle under high-intensity attacks due to resource limitations. To overcome this weakness, we propose a Genetic Algorithm and Particle Swarm Optimization (GAPSO), a novel DHR architecture. GAPSO provides real-time security awareness of host components, maps potential attacker paths, and calculates the risk probability of each component. High-risk components evolve into others in the pool during attacks. Experiments show that GAPSO significantly reduces system risk compared to scheduling-based DHR and effectively delays the hacker’s attack lifecycle. Additionally, we developed a prototype system and evaluated it in a real network environment, obtaining positive results.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Electrical Engineering","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0045790624006839","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
Abstract
The network security situation is increasingly serious. Traditional security equipment like firewalls and intrusion detection systems cannot prevent unknown risks due to their passive nature. Dynamic Heterogeneous Redundancy (DHR) actively defends by switching the attack surface and confusing attackers, becoming essential in modern network defense. However, existing DHR approaches based on scheduling algorithms struggle under high-intensity attacks due to resource limitations. To overcome this weakness, we propose a Genetic Algorithm and Particle Swarm Optimization (GAPSO), a novel DHR architecture. GAPSO provides real-time security awareness of host components, maps potential attacker paths, and calculates the risk probability of each component. High-risk components evolve into others in the pool during attacks. Experiments show that GAPSO significantly reduces system risk compared to scheduling-based DHR and effectively delays the hacker’s attack lifecycle. Additionally, we developed a prototype system and evaluated it in a real network environment, obtaining positive results.
期刊介绍:
The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency.
Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.