Browsing by Author "Eltanbouly, Sohaila"

Now showing items 1-5 of 5

    • Hybrid Machine Learning for Network Anomaly Intrusion Detection 

      Chkirbene, Zina; Eltanbouly, Sohaila; Bashendy, May; Alnaimi, Noora; Erbad, Aiman ( IEEE , 2020 , Conference)
      In this paper, a hybrid approach of combing two machine learning algorithms is proposed to detect the different possible attacks by performing effective feature selection and classification. This system uses Random Forest ...

    • LIME: Long-Term Forecasting Model for Desalination Membrane Fouling to Estimate the Remaining Useful Life of Membrane 

      Eltanbouly, Sohaila; Erradi, Abdelkarim; Tantawy, Ashraf; Ben Said, Ahmed; Shaban, Khaled; ... more authors ( Springer Science and Business Media Deutschland GmbH , 2023 , Conference)
      Membrane fouling is one of the major problems in desalination processes as it can cause a severe drop in the quality and quantity of the permeate water. This paper presents a data-driven approach for long-term forecasting ...

    • Machine Learning Techniques for Network Anomaly Detection: A Survey 

      Eltanbouly, Sohaila; Bashendy, May; Alnaimi, Noora; Chkirbene, Zina; Erbad, Aiman ( IEEE , 2020 , Conference)
      Nowadays, distributed data processing in cloud computing has gained increasing attention from many researchers. The intense transfer of data has made the network an attractive and vulnerable target for attackers to exploit ...

    • Multimodal Intrusion Detection System for Cyber Physical Systems 

      Eltanbouly, Sohaila Salah (2021 , Master Thesis)
      Cyber-Physical Systems (CPS) are deployed to control critical infrastructure in many fields, including industry and manufacturing. In recent years, CPS have been affected by cyberattacks due to the increased connectivity ...

    • Simple but not naive: Fine-grained arabic dialect identification using only n-grams 

      Eltanbouly, Sohaila; Bashendy, May; Elsayed, Tamer ( Association for Computational Linguistics (ACL) , 2019 , Conference)
      This paper presents the participation of Qatar University team in MADAR shared task, which addresses the problem of sentence-level fine-grained Arabic Dialect Identification over 25 different Arabic dialects in addition ...