Titlebook: Machine Learning for Networking; First International éric Renault,Paul Mühlethaler,Selma Boumerdassi Conference proceedings 2019 Springer

可忽略

Inordinate

,Advanced Hybrid Technique in Detecting Cloud Web Application’s Attacks,ed fear and user’s renunciations. However, Machine learning processing has proven high robustness in solving security flaws and reducing false alarm rates in detecting attacks. This paper, proposes a hybrid system that does not only labels behaviors based on machine learning algorithms using both mi

llibretto

Machine-Learned Classifiers for Protocol Selection on a Shared Network,raffic might influence which protocol to choose for a new foreground data transfer. Unfortunately, global knowledge can be difficult to obtain in a dynamic distributed system like a wide-area network (WAN)..Therefore, we introduce and evaluate a machine-learning (ML) approach to network performance,

Pert敏捷

不再流行

Inverse Kinematics Using Arduino and Unity for People with Motor Skill Limitations,ture the movement from the extremities in people with limited movement. It is noteworthy to mention that the results of said application are not discussed here but only the creation process is described. The first part is dedicated to describing the hardware required to use the app’s programmed tech

任意

: A Deep Learning Approach to Maximising the Utility of Virtualised Millimetre-Wave Backhauls,network slice aims to accommodate the demands of increasingly diverse services. However, precise allocation of resources to slices across future 5G millimetre-wave backhaul networks, to optimise the total network utility, is challenging. This is because the performance of different services often de

explicit

Malware Detection System Based on an In-Depth Analysis of the Portable Executable Headers, security field. In this paper, we propose a fast and highly accurate detection system of Portable Executable (PE) malware. The proposed system relies on analyzing the fields of the PE-headers using a basic way and a more in-depth way in order to generate a set of standard attributes (SAT), and mean

ANNUL

DNS Traffic Forecasting Using Deep Neural Networks,rs’ queries becomes a very valuable data source, since it reveals user patterns and how their Internet usage changes through time. The periodicity in human behavior is also reflected in how users use the Internet and therefore in the DNS queries they generate. Thus, in this paper we propose the use

智力高

Energy-Based Connected Dominating Set for Data Aggregation for Intelligent Wireless Sensor Networkstant method to maximize sensors’ lifetime. Aggregating sensed data from multiple sensors eliminates the redundant transmissions and provides fused information to the sink. It has been proved in the literature that a structure based data aggregation gives better results in terms of packet delivery an

amorphous