MapReduce based intelligent model for intrusion detection using machine learning technique

Abstract

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.

Keywords

• Denial-of-Service
• Intrusion detection system
• Cyber-attacks
• Network traffic
• Hadoop distributed file system