This research compares Random Forest (RF) and Support Vector Machines (SVM) in detecting intrusions using the UNSW-NB15 dataset. RF achieves 99.64% accuracy, with 4454 True Positives (TP), 4400 True Negatives (TN), 31 False Positives (FP), and 1 False Negative (FN). In contrast, SVM ...
The Bot-IoT Dataset | UNSW Research. https://research.unsw.edu.au/projects/bot-iot-dataset NSL-KDD | Datasets | Research | Canadian Institute for Cybersecurity | UNB. https://www.unb.ca/cic/datasets/nsl.html The CTU-13 Dataset. A Labeled Dataset with Botnet, Normal and Background traffi...
Here, we use RNN to deal with the network intrusion problem. The UNSW-NB15 dataset is used. - Cumt-Seu/Time-related-Intrusion-Detection-Model-based-on-Recurrent-Neural-Network
(3)UNSW-NB15 The UNSW-NB15 dataset was created by the Cyber Range Lab of the Australian Centre for Cyber Security. It is widely used due to its various novel attack patterns. The attack types include Fuzzer, Analysis, Backdoor, DoS, Exploits, Generic, Reconnaissance, Shellcode, and Worms....
Belouch et al. (2018) [17] compared four well-known classification algorithms, SVM, naïve Bayes, decision tree, and random forest, using Apache Spark with the UNSW-NB15 dataset. They found that random forest gave the best performance followed by decision tree and naïve Bayes. ...
The above models were trained and tested with the UNSWNB15 dataset. Similar work was carried out in [13]. Here, the authors not only focused on the model’s effectiveness but also on its efficiency. Using the same dataset, the authors conclude that AODE is the best algorithm, with a ...
https://research.unsw.edu.au/projects/unsw-nb15-dataset Google Scholar [35] https://www.unb.ca/cic/datasets/ids-2017.html Google Scholar [36] Nour Moustafa, Jill Slay The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with...
UNSW-NB15数据集的实验结果表明，MOCFL在动态环境中表现出色，表现出卓越的鲁棒性和准确性，同时保持合理...
preliminary tests to determine the effectiveness of different machine learning methods using network traffic, specifically the identification of botnet traffic from the UNSW-NB15 dataset. Results obtained from the previous tests, along with the dataset described in this paper, will be used for ...
We transform the tabular datasets into CSV files with a tabular structure. ISCX12, IoT Network, and UNSWNB15 are available only as a collection of monitored PCAP network packets, which we convert into CSV format usingtshark. Then, we remove features that are specific to the setup that was ...