| 期刊全稱 | Artificial Intelligence Tools for Cyber Attribution | | 影響因子2023 | Eric Nunes,Paulo Shakarian,Andrew Ruef | | 視頻video | http://file.papertrans.cn/163/162153/162153.mp4 | | 學(xué)科分類 | SpringerBriefs in Computer Science | | 圖書封面 |  | | 影響因子 | .This SpringerBrief discusses how to develop intelligent systems for cyber attribution regarding cyber-attacks. Specifically, the authors review the?multiple facets of the cyber attribution problem that make it difficult for?“out-of-the-box” artificial intelligence and machine learning techniques to?handle...?Attributing a cyber-operation through the use of multiple pieces of?technical evidence (i.e., malware reverse-engineering and source tracking)?and conventional intelligence sources (i.e., human or signals intelligence) is?a difficult problem not only due to the effort required to obtain evidence,?but the ease with which an adversary can plant false evidence...This SpringerBrief not only lays out the theoretical foundations for how to?handle the unique aspects of cyber attribution – and how to update?models used for this purpose – but it also describes a series of empirical?results, as well as compares results of specially-designed frameworks for?cyber attribution to standard machine learning approaches...?Cyber attribution is not only a challenging problem, but there are also?problems in performing such research, particularly in obtaining relevant?data. This SpringerBrief desc | | Pindex | Book 2018 |
The information of publication is updating
|
|
|Archiver|手機版|小黑屋|
派博傳思國際
( 京公網(wǎng)安備110108008328)
GMT+8, 2025-10-9 00:29
發(fā)表于 2025-3-21 18:31:31
