A Novel Forward-Propagation Workflow Assessment Method for Malicious Packet Detection
In recent times, there has been a huge upsurge in malicious attacks despite sophisticated technologies in digital network data transmission. This research proposes an innovative method that utilizes the forward-propagation workflow of the convolutional neural network (CNN) algorithm to detect malici...
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| Format: | Article |
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MDPI AG
2022-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/11/4167 |
| _version_ | 1797491621074305024 |
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| author | Nagaiah Mohanan Balamurugan Raju Kannadasan Mohammed H. Alsharif Peerapong Uthansakul |
| author_facet | Nagaiah Mohanan Balamurugan Raju Kannadasan Mohammed H. Alsharif Peerapong Uthansakul |
| author_sort | Nagaiah Mohanan Balamurugan |
| collection | DOAJ |
| description | In recent times, there has been a huge upsurge in malicious attacks despite sophisticated technologies in digital network data transmission. This research proposes an innovative method that utilizes the forward-propagation workflow of the convolutional neural network (CNN) algorithm to detect malicious information effectively. The performance comparison of this approach was accomplished using accuracy, precision, false-positive and false-negative rates with k-nearest neighbor (KNN) and support vector machine (SVM) algorithms. To detect malicious packets in the original dataset, an experiment was carried out using CNN’s forward-propagation workflow method (N = 11) as well as the KNN and the SVM machine learning algorithms with a significant value of 0.005. The accuracy, precision, false-positive and false-negative rates were evaluated to detect malicious packets present in normal data packets. The mean performance measures of the proposed forward-propagation method of the CNN algorithm were evaluated using the Statistical Package for the Social Sciences (SPSS) tool. The results showed that the mean accuracy (98.84%) and mean precision (99.08%) of the proposed forward propagation of the CNN algorithm appeared to be higher than the mean accuracy (95.55%) and mean precision (95.97%) of the KNN algorithm, as well as the mean accuracy (94.43%) and mean precision (94.58%) of the SVM algorithm. Moreover, the false-positive rate (1.93%) and false-negative rate (3.49%) of the proposed method appeared to be significantly higher than the KNN algorithm’s false-positive (4.04%) and false-negative (6.24%) as well as the SVM algorithm’s false-positive (5.03%) and false-negative rate (7.21%). Hence, it can be concluded that the forward-propagation method of the CNN algorithm is better than the KNN and SVM algorithms at detecting malicious information. |
| first_indexed | 2024-03-10T00:51:55Z |
| format | Article |
| id | doaj.art-7b0d5601f1a94456b4baa107cc2958e3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T00:51:55Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7b0d5601f1a94456b4baa107cc2958e32023-11-23T14:49:41ZengMDPI AGSensors1424-82202022-05-012211416710.3390/s22114167A Novel Forward-Propagation Workflow Assessment Method for Malicious Packet DetectionNagaiah Mohanan Balamurugan0Raju Kannadasan1Mohammed H. Alsharif2Peerapong Uthansakul3Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Sriperumbudur, Chennai 602117, IndiaDepartment of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Sriperumbudur, Chennai 602117, IndiaDepartment of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, Seoul 05006, KoreaSchool of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, ThailandIn recent times, there has been a huge upsurge in malicious attacks despite sophisticated technologies in digital network data transmission. This research proposes an innovative method that utilizes the forward-propagation workflow of the convolutional neural network (CNN) algorithm to detect malicious information effectively. The performance comparison of this approach was accomplished using accuracy, precision, false-positive and false-negative rates with k-nearest neighbor (KNN) and support vector machine (SVM) algorithms. To detect malicious packets in the original dataset, an experiment was carried out using CNN’s forward-propagation workflow method (N = 11) as well as the KNN and the SVM machine learning algorithms with a significant value of 0.005. The accuracy, precision, false-positive and false-negative rates were evaluated to detect malicious packets present in normal data packets. The mean performance measures of the proposed forward-propagation method of the CNN algorithm were evaluated using the Statistical Package for the Social Sciences (SPSS) tool. The results showed that the mean accuracy (98.84%) and mean precision (99.08%) of the proposed forward propagation of the CNN algorithm appeared to be higher than the mean accuracy (95.55%) and mean precision (95.97%) of the KNN algorithm, as well as the mean accuracy (94.43%) and mean precision (94.58%) of the SVM algorithm. Moreover, the false-positive rate (1.93%) and false-negative rate (3.49%) of the proposed method appeared to be significantly higher than the KNN algorithm’s false-positive (4.04%) and false-negative (6.24%) as well as the SVM algorithm’s false-positive (5.03%) and false-negative rate (7.21%). Hence, it can be concluded that the forward-propagation method of the CNN algorithm is better than the KNN and SVM algorithms at detecting malicious information.https://www.mdpi.com/1424-8220/22/11/4167novel forward propagationconvolutional neural networkk-nearest neighborsupport vector machinedeep learningmachine learning |
| spellingShingle | Nagaiah Mohanan Balamurugan Raju Kannadasan Mohammed H. Alsharif Peerapong Uthansakul A Novel Forward-Propagation Workflow Assessment Method for Malicious Packet Detection Sensors novel forward propagation convolutional neural network k-nearest neighbor support vector machine deep learning machine learning |
| title | A Novel Forward-Propagation Workflow Assessment Method for Malicious Packet Detection |
| title_full | A Novel Forward-Propagation Workflow Assessment Method for Malicious Packet Detection |
| title_fullStr | A Novel Forward-Propagation Workflow Assessment Method for Malicious Packet Detection |
| title_full_unstemmed | A Novel Forward-Propagation Workflow Assessment Method for Malicious Packet Detection |
| title_short | A Novel Forward-Propagation Workflow Assessment Method for Malicious Packet Detection |
| title_sort | novel forward propagation workflow assessment method for malicious packet detection |
| topic | novel forward propagation convolutional neural network k-nearest neighbor support vector machine deep learning machine learning |
| url | https://www.mdpi.com/1424-8220/22/11/4167 |
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