• Hybrid Machine Learning for Network Anomaly Intrusion Detection 

      Chkirbene, Zina; Eltanbouly, Sohaila; Bashendy, May; Alnaimi, Noora; Erbad, Aiman ( IEEE , 2020 , Conference Paper)
      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 ...
    • Machine Learning Techniques for Network Anomaly Detection: A Survey 

      Eltanbouly, Sohaila; Bashendy, May; Alnaimi, Noora; Chkirbene, Zina; Erbad, Aiman ( IEEE , 2020 , Conference Paper)
      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 ...
    • Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things 

      Zolanvari M.; Teixeira M.A.; Gupta L.; Khan K.M.; Jain R. ( Institute of Electrical and Electronics Engineers Inc. , 2019 , Article)
      It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning (ML) and big data analytics are the two powerful leverages ...