Titlebook: Artificial Intelligence for Cybersecurity; Mark Stamp,Corrado Aaron Visaggio,Fabio Di Troia Book 2022 The Editor(s) (if applicable) and Th

Hypothesis

書目名稱Artificial Intelligence for Cybersecurity影響因子(影響力)




書目名稱Artificial Intelligence for Cybersecurity影響因子(影響力)學(xué)科排名




書目名稱Artificial Intelligence for Cybersecurity網(wǎng)絡(luò)公開度




書目名稱Artificial Intelligence for Cybersecurity網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Artificial Intelligence for Cybersecurity被引頻次




書目名稱Artificial Intelligence for Cybersecurity被引頻次學(xué)科排名




書目名稱Artificial Intelligence for Cybersecurity年度引用




書目名稱Artificial Intelligence for Cybersecurity年度引用學(xué)科排名




書目名稱Artificial Intelligence for Cybersecurity讀者反饋




書目名稱Artificial Intelligence for Cybersecurity讀者反饋學(xué)科排名

上釉彩

parasite

Project

https://doi.org/10.1007/978-1-349-15821-8ieving state-of-the-art results in many areas, it also has drawbacks exploited by many with white-box attacks. Although the white-box scenario is possible in malware detection, the detailed structure of antivirus is often unknown. Consequently, we focused on a pure black-box setup where no informati

克制

猛烈責(zé)罵

世俗

https://doi.org/10.1007/978-3-642-35822-7ich uniquely distinguishes it from other typical malware threats. The C&C server sends commands to the botnets to execute malicious activities using common Internet protocols, such as Hypertext transfer (HTTP), and Internet Relay Chat (IRC). Since these protocols are common, detecting botnet activit

陪審團(tuán)

Class D Results and Simulations,r hand, the results can be hard to understand as to why a model classified a given file as malicious or benign. This paper focuses on the interpretability of machine learning models’ results using decision lists generated by two rule-based classifiers, I-REP and RIPPER. We use the EMBER dataset, whi

BALE

Class D Results and Simulations,ntrol the propagation in mobile devices. According to Damballa’s Q4 State of Infections report, the antivirus products overlooked 70% of malware signatures within the first hour (Q4 2014 State of Infections Report. Q4 2014 state of infections report. ., accessed August 2021). This is despite the fac

靈敏

https://doi.org/10.1007/978-3-031-40419-1gs generated by BERT. We extract the “words” directly from the malware samples to achieve multi-class classification. In fact, the attention mechanism of a pre-trained BERT model can be used in malware classification by capturing information about the relation between each opcode and every other opc