Detecting Network Intrusions with Resilient Approaches Based on Convolutional Neural Networks
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 13798 LNCS, Page: 238-248
2023
- 2Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures2
- Readers2
Conference Paper Description
An anomaly-dependent network intrusion detection method is proposed by presenting a hybrid of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) approaches where it detects various network attacks by training a CNN on the NSL-KDD dataset. The proposed approach is able to automatically discovers significant traits without human intervention and achieves higher accuracy in detecting attacks which can improve the performance of intrusion detection systems. The CNN-LSTM approach is applied to detect attacks such as DoS, Probe, U2R, and R2L, and identify normal traffic at the same time. The experimental result reached a low false-positive rate, a high accuracy rate, and a low time average. The results show that the proposed approach outperformed the current state-of-the-art by 4.69% in terms of accuracy and conventional CNN achieved a greater accuracy of roughly 3% compared to the current approach, which amounted to 98.95%, 97.81%, and 94.26% respectively. The consistency of the simulated attacks is unequal which leads to issues in the prediction, therefore, additional efforts to collect more consistent traffic on unusual attacks are required. The proposed approach is targeted to be applied as an effective tool for solving complex classification problems such as NIDS.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85159419106&origin=inward; http://dx.doi.org/10.1007/978-3-031-29927-8_19; https://link.springer.com/10.1007/978-3-031-29927-8_19; https://dx.doi.org/10.1007/978-3-031-29927-8_19; https://link.springer.com/chapter/10.1007/978-3-031-29927-8_19
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know