Selected article for: "artificial intelligence and deep learning"
Author: Jemal, Ines; Haddar, Mohamed Amine; Cheikhrouhou, Omar; Mahfoudhi, Adel
Title: ASCII Embedding: An Efficient Deep Learning Method for Web Attacks Detection
  • Cord-id: 15g1wqfs
  • Document date: 2021_2_22
    • ID: 15g1wqfs
    Snippet: Web security is a homogeneous mixture of modern machinery and software technologies designed to protect the personal and confidential data of all Internet users. After many decades of hard work in web security, the protection of personal data remains an obsession for legitimate internet users. Nowadays, artificial intelligence techniques are overcoming classical signature-based and anomaly-based techniques, which unable to detect zero-day attacks. To help reduce fraud and electronic theft at the
    Document: Web security is a homogeneous mixture of modern machinery and software technologies designed to protect the personal and confidential data of all Internet users. After many decades of hard work in web security, the protection of personal data remains an obsession for legitimate internet users. Nowadays, artificial intelligence techniques are overcoming classical signature-based and anomaly-based techniques, which unable to detect zero-day attacks. To help reduce fraud and electronic theft at the server-side, we propose in this paper a novel deep learning method to preprocess the input of neural networks. This technique, called ASCII Embedding, aims to efficiently detect web server attacks. Using an online real dataset CSIC 2010, we evaluated and compared our technique to existing ones as word, and character embedding approaches. The experimental results prove that our technique outperforms the existing works accuracy and reaches 98.2434%.

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