Advanced Hybrid Technique in Detecting Cloud Web Application’s Attacks

Amar, Meryem; Lemoudden, Mouad; El Ouahidi, Bouabid

Abstract

Recently cloud computing has emerged the IT world. It eventually promoted the acquisition of resources and services as needed, but it has also instilled fear and user’s renunciations. However, Machine learning processing has proven high robustness in solving security flaws and reducing false alarm rates in detecting attacks. This paper, proposes a hybrid system that does not only labels behaviors based on machine learning algorithms using both misuse and anomaly-detection, but also highlights correlations between network relevant features, speeds up the updating of signatures dictionary and upgrades the analysis of user behavior.

Citation

Amar, M., Lemoudden, M., & El Ouahidi, B. (2019). Advanced Hybrid Technique in Detecting Cloud Web Application’s Attacks. In Machine Learning for Networking: First International Conference, MLN 2018, Paris, France, November 27–29, 2018, Revised Selected Papers (79-97). https://doi.org/10.1007/978-3-030-19945-6_6

• Conference Name: International Conference on Machine Learning for Networking
• Conference Location: Paris, France
• Start Date: Nov 27, 2018
• End Date: Nov 29, 2018
• Online Publication Date: May 10, 2019
• Publication Date: 2019
• Deposit Date: Feb 28, 2023
• Publisher: Springer
• Pages: 79-97
• Series Title: Lecture Notes in Computer Science
• Series Number: 11407
• Series ISSN: 1611-3349
• Book Title: Machine Learning for Networking: First International Conference, MLN 2018, Paris, France, November 27–29, 2018, Revised Selected Papers
• ISBN: 978-3-030-19944-9
• DOI: https://doi.org/10.1007/978-3-030-19945-6_6
• Keywords: Attack-detection, Cloud, IDS, Machine learning, Security, Similarities