term memory, self-organizing map, auto-encoder, restricted Boltzmann machine, deep belief networks, generative adversarial network, deep transfer learning, as well as deep reinforcement learning, or their ensembles and hybrid approaches can be used to intelligently tackle the diverse cybersecurity issues...
Deep Instinct takes a prevention-first approach to stopping ransomware and other malware using the world’s first and only purpose-built, deep learning cybersecurity framework.
But, cyber security is challenging task. Malware detection and network intrusion detection are the two areas where DL shows significant improvement over rule based and classic machine learning (ML) based solutions. DL based neural nets are used in user and entity behaviour analytics (UEBA). UEBA ...
We look into developments in end-to-end deep learning for cybersecurity and provide insights into its current and future effectiveness.
Different techniques and algorithms under deep reinforcement learning have shown great promise in applications ranging from games to industrial processes, where it is claimed to augment systems with general AI capabilities. These algorithms have recently also been used in cybersecurity, especially in ...
Shina decided that it no longer wanted to wait for Health IT to make a decision about, what will protect the company’s 3Di Picture Archiving, Communication System and Imaging applications. When 3Di will be implemented, it will have the best cybersecurity protection based on deep learning with...
Deep Learning Algorithms for Cybersecurity Applications: A Technological and Status Review 2021, Computer Science Review Show abstract Echo state network based ensemble approach for wind power forecasting 2019, Energy Conversion and Management Citation Excerpt : In addition, wind power forecasting results ...
This paper has introduced a new network forensics framework that is named Particle Deep Framework (PDF) for discovering cyber-attacks and tracing them in IoT networks. Show abstract HYDRA: A multimodal deep learning framework for malware classification 2020, Computers and Security Show abstract A ...
Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams Analysis of an organization's computer network activity is a key component of early detection and mitigation of insider threat, a growing concern for many ... A Tuor,S Kaplan,B Hutchinson,... 被引量...
there is no an edited book that focuses in merging the security, privacy and deep learning in cyber-physical systems and IoT platforms. There are few edited books that focus on each perspective individually. However, merging both perspectives give a clear view about the current and future develop...