A novel machine learning-based artificial intelligence approach for log analysis using blockchain technology

Yıl 2024, Cilt: 42 Sayı: 5, 1391 - 1409, 04.10.2024

Rizwan Ur Rahman Pavan Kumar , Gaurav Pramod Kachare Meeraj Mahendra Gawde Tenzin Tsundue1 Deepak Singh Tomar

Öz

Cybercrime is one of the fastest-growing crimes worldwide. It is observed that every seven seconds, cyber attackers penetrate cyber systems. While detecting an anomaly or attack, the log system is one of the crucial components of any system storing and managing all the events. It has always been challenging to detect an anomaly in logs. This is because of continuous and ever-changing log events and their mutability property. In this paper, we develop a ma-chine learning-based artificial intelligence approach to address this issue of log analysis by proposing two modules. The first one is anomaly detection using different machine learning models. The second one is a distributed immutable storage system for securely storing the logs. In addition, we present a descriptive and user-friendly web application by integrating all modules using HTML, CSS, and Flask Framework on the Heroku cloud environment. The re-sults demonstrate that the proposed hybrid machine learning models are capable of achieving 99.7% accuracy in detecting network anomalies.

Anahtar Kelimeler

Immutable Storage, Isolation Forest Machine Learning, Logs, KNN

Kaynakça

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