article · Big Data Mining and Analytics
Cloud computing provides on-demand access to computational and storage resources, yet securing these services against attacks remains a critical challenge for cloud providers. Intrusion detection systems help safeguard these environments by monitoring network traffic to identify abnormal and malicious behaviour. A machine learning model combining feature engineering with a random forest classifier offers a way to improve detection accuracy within cloud networks. When tested on standard benchmark data, the approach attained an accuracy of 98.3 percent on the Bot-IoT dataset and 99.99 percent on the NSL-KDD dataset. Across these evaluations, the model demonstrated strong performance in terms of overall accuracy, precision, and recall when compared to existing contemporary detection techniques, providing an effective framework for identifying cyber attacks across cloud systems.
Cloud services underpin many modern digital systems, making their security vital for safeguarding sensitive information and maintaining operational reliability. As cyber threats grow, intrusion detection tools that accurately distinguish benign network activity from malicious attacks help cloud providers protect their infrastructure and maintain dependable services for users.
The model could be applied by cloud providers and network security teams seeking to strengthen automated threat monitoring and attack detection across cloud infrastructure. Because the approach has been evaluated and validated using standard benchmark datasets rather than deployed in operational production environments, it currently appears to be at an applied and tested stage of research.
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Cloud computing (CC) is a novel technology that has made it easier to access network and computer resources on demand such as storage and data management services. In addition, it aims to strengthen systems and make them useful. Regardless of these advantages, cloud providers suffer from many security limits. Particularly, the security of resources and services represents a real challenge for cloud technologies. For this reason, a set of solutions have been implemented to improve cloud security by monitoring resources, services, and networks, then detect attacks. Actually, intrusion detection system (IDS) is an enhanced mechanism used to control traffic within networks and detect abnormal activities. This paper presents a cloud-based intrusion detection model based on random forest (RF) and feature engineering. Specifically, the RF classifier is obtained and integrated to enhance accuracy (ACC) of the proposed detection model. The proposed model approach has been evaluated and validated on two datasets and gives 98.3% ACC and 99.99% ACC using Bot-IoT and NSL-KDD datasets, respectively. Consequently, the obtained results present good performances in terms of ACC, precision, and recall when compared to the recent related works.
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DOI: 10.26599/bdma.2022.9020038
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