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...

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Vydané v:Journal of King Saud University. Computer and information sciences Ročník 34; číslo 10; s. 9723 - 9731
Hlavní autori: Muhammad Asif, Sagheer Abbas, M.A. Khan, Areej Fatima, Muhammad Adnan Khan, Sang-Woong Lee
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Springer 01.11.2022
Predmet:
Cyber-attacks
Denial-of-Service
Hadoop distributed file system
Intrusion detection system
Network traffic
ISSN:1319-1578
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Popis
Shrnutí: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.
ISSN:1319-1578
DOI:10.1016/j.jksuci.2021.12.008