MapReduce based intelligent model for intrusion detection using machine learning technique
With the emergence of the Internet of Things (IoT), the computer networks’ phenomenal expansion, and enormous relevant applications, data is continuously increasing. In this way, cybersecurity has gained significant importance in protecting networks from different cyber-attacks like Intrusions, Deni...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157821003530 |
| _version_ | 1811183983920152576 |
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| author | Muhammad Asif Sagheer Abbas M.A. Khan Areej Fatima Muhammad Adnan Khan Sang-Woong Lee |
| author_facet | Muhammad Asif Sagheer Abbas M.A. Khan Areej Fatima Muhammad Adnan Khan Sang-Woong Lee |
| author_sort | Muhammad Asif |
| collection | DOAJ |
| description | With the emergence of the Internet of Things (IoT), the computer networks’ phenomenal expansion, and enormous relevant applications, data is continuously increasing. In this way, cybersecurity has gained significant importance in protecting networks from different cyber-attacks like Intrusions, Denial-of-Service (DoS), Eavesdropping, Rushing Attack, etc. A traditional Intrusion Detection System (IDS) tangled with the clustering technique plays a vital role in modern security. Still, it has limitations to analyze the vast volumes of data to identify an anomaly intelligently. Machine learning is a technique that may be tangled with the MapReduce-Based Intelligent Model for Intrusion Detection (MR-IMID) to automate intrusion detection intelligently. MR-IMID is proposed to detect intrusions on a network with multiple data classification tasks in this research work. The proposed MR-IMID processes big data sets reliably using commodity hardware. In this proposed research work, multiple network sources are being utilized in Real-time for intrusion detection. In this proposed research, the MR-IMID detects intrusions by predicting unknown test scenarios and stores the data in the database to minimize future inconsistencies. The detection accuracy of the proposed model during training and validation phases is 97.7% and 95.7%, respectively, which is better than previously published approaches. |
| first_indexed | 2024-04-11T13:05:48Z |
| format | Article |
| id | doaj.art-3b292696ec7a423a98e9f1ddb7291c28 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-11T13:05:48Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-3b292696ec7a423a98e9f1ddb7291c282022-12-22T04:22:45ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-11-01341097239731MapReduce based intelligent model for intrusion detection using machine learning techniqueMuhammad Asif0Sagheer Abbas1M.A. Khan2Areej Fatima3Muhammad Adnan Khan4Sang-Woong Lee5School of Computer Science, National College of Business Administration and Economics, Lahore 54000, PakistanSchool of Computer Science, National College of Business Administration and Economics, Lahore 54000, PakistanRiphah School of Computing & Innovation, Riphah International University, Lahore Campus, Lahore 54000, PakistanDepartment of Computer Science, Lahore Garrison University, Lahore 54000, PakistanPattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam 13557, South Korea; Corresponding author.Pattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam 13557, South KoreaWith the emergence of the Internet of Things (IoT), the computer networks’ phenomenal expansion, and enormous relevant applications, data is continuously increasing. In this way, cybersecurity has gained significant importance in protecting networks from different cyber-attacks like Intrusions, Denial-of-Service (DoS), Eavesdropping, Rushing Attack, etc. A traditional Intrusion Detection System (IDS) tangled with the clustering technique plays a vital role in modern security. Still, it has limitations to analyze the vast volumes of data to identify an anomaly intelligently. Machine learning is a technique that may be tangled with the MapReduce-Based Intelligent Model for Intrusion Detection (MR-IMID) to automate intrusion detection intelligently. MR-IMID is proposed to detect intrusions on a network with multiple data classification tasks in this research work. The proposed MR-IMID processes big data sets reliably using commodity hardware. In this proposed research work, multiple network sources are being utilized in Real-time for intrusion detection. In this proposed research, the MR-IMID detects intrusions by predicting unknown test scenarios and stores the data in the database to minimize future inconsistencies. The detection accuracy of the proposed model during training and validation phases is 97.7% and 95.7%, respectively, which is better than previously published approaches.http://www.sciencedirect.com/science/article/pii/S1319157821003530Denial-of-ServiceIntrusion detection systemCyber-attacksNetwork trafficHadoop distributed file system |
| spellingShingle | Muhammad Asif Sagheer Abbas M.A. Khan Areej Fatima Muhammad Adnan Khan Sang-Woong Lee MapReduce based intelligent model for intrusion detection using machine learning technique Journal of King Saud University: Computer and Information Sciences Denial-of-Service Intrusion detection system Cyber-attacks Network traffic Hadoop distributed file system |
| title | MapReduce based intelligent model for intrusion detection using machine learning technique |
| title_full | MapReduce based intelligent model for intrusion detection using machine learning technique |
| title_fullStr | MapReduce based intelligent model for intrusion detection using machine learning technique |
| title_full_unstemmed | MapReduce based intelligent model for intrusion detection using machine learning technique |
| title_short | MapReduce based intelligent model for intrusion detection using machine learning technique |
| title_sort | mapreduce based intelligent model for intrusion detection using machine learning technique |
| topic | Denial-of-Service Intrusion detection system Cyber-attacks Network traffic Hadoop distributed file system |
| url | http://www.sciencedirect.com/science/article/pii/S1319157821003530 |
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