Improved Intrusion Detection System That Uses Machine Learning Techniques to Proactively Defend DDoS Attack
This abstract aims to provide a comprehensive analysis of the intricacies of DDoS attacks, which are increasingly prevalent and malicious cyber-attacks that disrupt the normal flow of traffic to a targeted server by exponentially increasing network traffic. To secure distributed systems against DDoS...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2023/06/itmconf_icdsac2023_05011.pdf |
| _version_ | 1797745637874204672 |
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| author | T Rajendran E Abishekraj U Dhanush |
| author_facet | T Rajendran E Abishekraj U Dhanush |
| author_sort | T Rajendran |
| collection | DOAJ |
| description | This abstract aims to provide a comprehensive analysis of the intricacies of DDoS attacks, which are increasingly prevalent and malicious cyber-attacks that disrupt the normal flow of traffic to a targeted server by exponentially increasing network traffic. To secure distributed systems against DDoS attacks, intrusion detection mechanisms and machine learning techniques are commonly employed. The CICDDoS2019 dataset is often utilized for the detection and prevention of these attacks. The dataset undergoes pre-processing and is split into training and test datasets. Machine learning techniques are then utilized to predict and classify the attacks using the test dataset. The protocols which are examined during the attack are SNMP, NTP, UDP, and DNS. The accuracy is obtained by comparing the predicted results with the training dataset. Machine learning algorithms such as K- Nearest Neighbor(K-NN)-96.49%, Support Vector Machine (SVM)-79.61%, Random Forest (RF)-99.10%, and Gaussian Naïve Bayes (GNB)-78.75% have been found to produce high levels of accuracy for attack classification. |
| first_indexed | 2024-03-12T15:26:06Z |
| format | Article |
| id | doaj.art-ce96c984231e4ddcae99ed2dd46ddc37 |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-03-12T15:26:06Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-ce96c984231e4ddcae99ed2dd46ddc372023-08-10T13:16:50ZengEDP SciencesITM Web of Conferences2271-20972023-01-01560501110.1051/itmconf/20235605011itmconf_icdsac2023_05011Improved Intrusion Detection System That Uses Machine Learning Techniques to Proactively Defend DDoS AttackT Rajendran0E Abishekraj1U Dhanush2Department of Computer Science and Engineering, Rajalakshmi Institute of TechnologyDepartment of Computer Science and Engineering, Rajalakshmi Institute of TechnologyDepartment of Computer Science and Engineering, Rajalakshmi Institute of TechnologyThis abstract aims to provide a comprehensive analysis of the intricacies of DDoS attacks, which are increasingly prevalent and malicious cyber-attacks that disrupt the normal flow of traffic to a targeted server by exponentially increasing network traffic. To secure distributed systems against DDoS attacks, intrusion detection mechanisms and machine learning techniques are commonly employed. The CICDDoS2019 dataset is often utilized for the detection and prevention of these attacks. The dataset undergoes pre-processing and is split into training and test datasets. Machine learning techniques are then utilized to predict and classify the attacks using the test dataset. The protocols which are examined during the attack are SNMP, NTP, UDP, and DNS. The accuracy is obtained by comparing the predicted results with the training dataset. Machine learning algorithms such as K- Nearest Neighbor(K-NN)-96.49%, Support Vector Machine (SVM)-79.61%, Random Forest (RF)-99.10%, and Gaussian Naïve Bayes (GNB)-78.75% have been found to produce high levels of accuracy for attack classification.https://www.itm-conferences.org/articles/itmconf/pdf/2023/06/itmconf_icdsac2023_05011.pdf |
| spellingShingle | T Rajendran E Abishekraj U Dhanush Improved Intrusion Detection System That Uses Machine Learning Techniques to Proactively Defend DDoS Attack ITM Web of Conferences |
| title | Improved Intrusion Detection System That Uses Machine Learning Techniques to Proactively Defend DDoS Attack |
| title_full | Improved Intrusion Detection System That Uses Machine Learning Techniques to Proactively Defend DDoS Attack |
| title_fullStr | Improved Intrusion Detection System That Uses Machine Learning Techniques to Proactively Defend DDoS Attack |
| title_full_unstemmed | Improved Intrusion Detection System That Uses Machine Learning Techniques to Proactively Defend DDoS Attack |
| title_short | Improved Intrusion Detection System That Uses Machine Learning Techniques to Proactively Defend DDoS Attack |
| title_sort | improved intrusion detection system that uses machine learning techniques to proactively defend ddos attack |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2023/06/itmconf_icdsac2023_05011.pdf |
| work_keys_str_mv | AT trajendran improvedintrusiondetectionsystemthatusesmachinelearningtechniquestoproactivelydefendddosattack AT eabishekraj improvedintrusiondetectionsystemthatusesmachinelearningtechniquestoproactivelydefendddosattack AT udhanush improvedintrusiondetectionsystemthatusesmachinelearningtechniquestoproactivelydefendddosattack |