지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.12
- 수록면
- 699 - 708 (10page)
- DOI
- 10.9728/jcc.2023.12.5.2.699
이용수
초록· 키워드
오류제보하기
With the increase in the use of the Internet, most applications now have an online presence, which has increased Internet of Things-based applications. Therefore, implies that most of our applications could be exposed to malicious activity with-in the network, and the burden of protecting the network and detecting any attack quickly becomes more significant as new attacks surface and cyberattacks in-crease. The development of a multi-level data security system for the Internet of Things using dimensionality reduction techniques was first realized by analyzing existing data security systems. This was achieved by developing a multi-level data security system using Dimensionality Reduction Technique. Machine Learning Technique was used to dynamically detect malicious activity beyond the Intruder Detection System (IDS). Firstly, Principal Component Analysis (PCA) was used to authenticate the status of the source of data transmitted. The first layer of IDS was achieved using Naïve Bayes classifier, while Random Forest was adopted in the second layer. The work was able to identify the normal and malicious packets in the first layer, while in the second layer, the type of attacks on the IoT network were identified. Finally, PCA enhanced security by classifying malicious packets. The results demonstrate that our proposed PCA detection model gives a relatively high Precision rate with 95%, 94.4%, 97.5%, 95%, and 96% for the Normal, Flooding, Scheduling, Gray hole, and Blackhole attacks, respectively. The re-search shows that the PCA Technique performed well when deployed, and the test with relevant performance evaluation metrics shows better accuracy regarding detecting malicious Nodes.
목차
Abstract
1. Introduction
2. Review of Related Work
3. Methodology and system analysis
4. Experiment and Result
5. Conclusion
References
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