SIDS-DDoS, a Smart Intrusion Detection System for Distributed Denial of Service Attacks
In the last few years, the Digital Services industry has grown tremendously, offering numerous services through the Internet and using a recent concept or business model called cloud computing. For this reason, new threats and cyber-attacks have appeared, such as Denial of Service attacks. Their mai...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9152
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9152
- Palabra clave:
- Classification model
Data set
DoS attacks
Feature selection
Machine learning
Support vector machine
Classification (of information)
Feature extraction
Information services
Intrusion detection
Learning systems
Network security
Support vector machines
Web services
Business modeling
Classification models
Computer resources
Cross-validation technique
Data set
Distributed denial of service attack
Intrusion Detection Systems
Network bandwidth
Denial-of-service attack
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
| Summary: | In the last few years, the Digital Services industry has grown tremendously, offering numerous services through the Internet and using a recent concept or business model called cloud computing. For this reason, new threats and cyber-attacks have appeared, such as Denial of Service attacks. Their main objective is to prevent legitimate users from accessing services (websites, online stores, blogs, social media, banking services, etc.) offered by different companies on the Internet. In addition, it produces collateral damage in host and web servers, for example, exhaustion of network bandwidth and computer resources of the victim. In this article, we will analyze the information contained in NSL-KDD data-set, which possesses important records about the several behaviors of network traffic. These will be selected to present two methods of selection of features that allow the selection of the most relevant attributes within the data set, to build an Intrusion Detection System. The attributes selected for this experiment will be of great help to train and test various kernels of the Support Vector Machine. Once the model has been tested, an evaluation of the classification model will be performed using the cross-validation technique and we finally can choose the best classifier. © 2020, Springer Nature Switzerland AG. |
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