Titlebook: Machine Learning for Cyber Security; Third International Xiaofeng Chen,Hongyang Yan,Xiangliang Zhang Conference proceedings 2020 Springer

Delectable

A Scientometric Analysis of Malware Detection Research Based on CiteSpace,nt trend in this field concisely and intuitively, which provide the theoretical basis for the in-depth study of malware detection for the scientific research. Finally, the malware detection model is designed according to the analysis results of Mapping Knowledge Domain to achieve effective and efficient performance in malware detection.

极小

Detection of Malicious Domains in APT via Mining Massive DNS Logs,tures and calculate the score of being malicious from the DNS logs with minimal ground truth. We implement and validate our framework on anonymous DNS logs released by LANL. The experiment shows that our approach identifies previously unknown malicious domains and achieves high detection rates.

patriot

Spatio-Temporal Graph Convolutional Networks for DDoS Attack Detecting,network topology and network traffic, this model has a good recognition rate on the online DDoS data set, and can achieve detection of DDoS attacks under the premise of using only two-way traffic information.

ineptitude

A New Lightweight CRNN Model for Keyword Spotting with Edge Computing Devices,tructure, and it uses a feature enhancement method. The experimental results on Google Speech Commands Dataset depict that EdgeCRNN can test 11.1 audio data per second on Raspberry Pi 3B+, which are 2.2 times that of Tpool2. Compared with Tpool2, the accuracy of EdgeCRNN reaches 98.05% whilst its performance is also competitive.

Extort

Conference proceedings 2020in Xi’an, China in October 2020..The 118 full papers and 40 short papers presented were carefully reviewed and selected from 360 submissions. The papers offer a wide range of the following subjects: Machine learning, security, privacy-preserving, cyber security, Adversarial machine Learning,  Malwar

arterioles

大暴雨

Arbitrary

0302-9743 . The papers offer a wide range of the following subjects: Machine learning, security, privacy-preserving, cyber security, Adversarial machine Learning,  Malware detection and analysis,  Data mining, and  Artificial Intelligence. .978-3-030-62222-0978-3-030-62223-7Series ISSN 0302-9743 Series E-ISSN 1611-3349

Cocker

0302-9743 020, held in Xi’an, China in October 2020..The 118 full papers and 40 short papers presented were carefully reviewed and selected from 360 submissions. The papers offer a wide range of the following subjects: Machine learning, security, privacy-preserving, cyber security, Adversarial machine Learnin

角斗士

An Anomalous Traffic Detection Approach for the Private Network Based on Self-learning Model, its effectiveness and efficiency, a self-learning model is proposed and deployed in the detection approach. Finally, we conduct necessary evaluations about the proposed approach. The test results show that the approach can reach a good effect for detecting the unknown anomalous traffic.