Detecting Network Anomalies Using the Rain Optimization Algorithm and Hoeffding Tree-based Autoencoder

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Mohammad Mohammad Abbas; Kolahkaj Maral

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2024 | Issue:10

Download Full-Text

View:

Download:

Information Seminar Paper

Title

Detecting Network Anomalies Using the Rain Optimization Algorithm and Hoeffding Tree-based Autoencoder

Author(s)

Mohammad Mohammad Abbas | Kolahkaj Maral

Keywords

Security

Network Anomaly

Intrusion Detection

Machine Learning

Rain Optimization Algorithm

Hoeffding Tree-based Autoencoder Network

Abstract

The utilization of computer systems has rapidly expanded, accompanied by a corresponding rise in Security threats such as hackers, viruses, worms, and similar malicious entities spreading at an alarming rate across networks. In response, anomaly Intrusion Detection methods have been developed to counter these attacks. However, as information systems evolve, certain detection techniques have seen a decline in effectiveness due to the escalating volume of network data traffic and the continuous need for swift responses. Addressing this critical issue, this research proposes a method to enhance the accuracy of feature selection and extraction for Intrusion Detection and anomaly classification. This is achieved through the integration of optimization and autoencoder algorithms, evaluating the impact of Machine Learning and artificial intelligence in Network Anomaly detection. Utilizing the NSL-KDD dataset, the study begins with data collection and preparation, followed by the application of optimization algorithms such as the Rain Optimization Algorithm (ROA) and Artificial Bee Colony (ABC) in conjunction with various neural network architectures, including Radial Basis Function neural network, decision tree, Support Vector Machine, K-Nearest Neighbors, ensemble network, mountain model, SOM clustering, and ultimately the Hoeffding Tree-based Autoencoder Network. Results demonstrate that the proposed approach, leveraging the Rain Optimization Algorithm and Hoeffding Tree-based Autoencoder Network, excels in feature selection and extraction during training, effectively detecting and classifying intrusion or anomaly occurrences with high accuracy. Notably, among the algorithms tested, the Hoeffding Tree-based Autoencoder Network achieved an accuracy of 98. 74%, indicating commendable performance in classification and result evaluation.

Cites

No record.

References

No record.

Cite

APA: Copy

Mohammad, Mohammad Abbas, & Kolahkaj, Maral. (2024). Detecting Network Anomalies Using the Rain Optimization Algorithm and Hoeffding Tree-based Autoencoder. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/1147674/en

Vancouver: Copy

Mohammad Mohammad Abbas, Kolahkaj Maral. Detecting Network Anomalies Using the Rain Optimization Algorithm and Hoeffding Tree-based Autoencoder. 2024. Available from: https://sid.ir/paper/1147674/en

IEEE: Copy

Mohammad Abbas Mohammad, and Maral Kolahkaj, “Detecting Network Anomalies Using the Rain Optimization Algorithm and Hoeffding Tree-based Autoencoder,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2024, [Online]. Available: https://sid.ir/paper/1147674/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops