Cyber Security against Intrusion Detection Using Ensemble-Based Approaches

The attacks of cyber are rapidly increasing due to advanced techniques applied by hackers. Furthermore, cyber security is demanding day by day, as cybercriminals are performing cyberattacks in this digital world. So, designing privacy and security measurements for IoT-based systems is necessary for...

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Bibliographic Details
Published in:Security and communication networks 2023, Vol.2023, p.1-7
Main Authors: Alatawi, Mohammed Naif, Alsubaie, Najah, Ullah Khan, Habib, Sadad, Tariq, Alwageed, Hathal Salamah, Ali, Shaukat, Zada, Islam
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Classification
Communication
Cybersecurity
Datasets
Deep learning
Feature selection
Hybrid systems
Internet of Things
Intrusion detection systems
Machine learning
Reliability analysis
Resampling
Servers
Smart cities
Software
Swarm intelligence
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Summary:The attacks of cyber are rapidly increasing due to advanced techniques applied by hackers. Furthermore, cyber security is demanding day by day, as cybercriminals are performing cyberattacks in this digital world. So, designing privacy and security measurements for IoT-based systems is necessary for secure network. Although various techniques of machine learning are applied to achieve the goal of cyber security, but still a lot of work is needed against intrusion detection. Recently, the concept of hybrid learning gives more attention to information security specialists for further improvement against cyber threats. In the proposed framework, a hybrid method of swarm intelligence and evolutionary for feature selection, namely, PSO-GA (PSO-based GA) is applied on dataset named CICIDS-2017 before training the model. The model is evaluated using ELM-BA based on bootstrap resampling to increase the reliability of ELM. This work achieved highest accuracy of 100% on PortScan, Sql injection, and brute force attack, which shows that the proposed model can be employed effectively in cybersecurity applications.
ISSN:1939-0114
1939-0122
DOI:10.1155/2023/8048311