14
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Recurrent Neural Networks Based Online Behavioural Malware Detection Techniques for Cloud Infrastructure

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Related collections

          Most cited references39

          • Record: found
          • Abstract: not found
          • Conference Proceedings: not found

          Deep Residual Learning for Image Recognition

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Long Short-Term Memory

            Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Densely Connected Convolutional Networks

                Bookmark

                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                IEEE Access
                IEEE Access
                Institute of Electrical and Electronics Engineers (IEEE)
                2169-3536
                2021
                2021
                : 9
                : 68066-68080
                Article
                10.1109/ACCESS.2021.3077498
                de46a333-f72f-49dc-8ee2-2e1908a31ba3
                © 2021

                https://creativecommons.org/licenses/by/4.0/legalcode

                History

                Comments

                Comment on this article