The combined impact of new computing resources and techniques with an increasing avalanche of large datasets, is transforming many research areas and may lead to technological breakthroughs that can be used by billions of people. In the recent years, Machine Learning and especially its subfield Deep Learning have seen impressive advances. Techniques developed within these two fields are now able to analyze and learn from huge amounts of real world examples in a disparate formats. While the number of Machine Learning algorithms is extensive and growing, their implementations through frameworks and libraries is also extensive and growing too. The software development in this field is fast paced with a large number of open-source software coming from the academy, industry, start-ups or wider open-source communities. This survey presents a recent time-slide comprehensive overview with comparisons as well as trends in development and usage of cutting-edge Artificial Intelligence software. It also provides an overview of massive parallelism support that is capable of scaling computation effectively and efficiently in the era of Big Data.
The work presented in this paper deals with a proactive network monitoring for security and protection of computing infrastructures. We provide an exploitation of an intelligent module, in the form of a as a machine learning application using deep learning modeling, in order to enhance functionality of intrusion detection system supervising network traffic flows. Currently, intrusion detection systems work well for network monitoring in near real-time and they effectively deal with threats in a reactive way. Deep learning is the emerging generation of artificial intelligence techniques and one of the most promising candidates for intelligence integration into traditional solutions leading to quality improvement of the original solutions. The work presented in this paper faces the challenge of cooperation between deep learning techniques and large-scale data processing. The outcomes obtained from extensive and careful experiments show the applicability and feasibility of simultaneously modelled multiple monitoring channels using deep learning techniques. The proper joining of deep learning modelling with scalable data preprocessing ensures high quality and stability of model performance in dynamic and fast-changing environments such as network traffic flow monitoring.
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