transparent computing
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Author(s):  
Sidahmed Benabderrahmane ◽  
Ghita Berrada ◽  
James Cheney ◽  
Petko Valtchev

Advanced persistent threats (APT) are stealthy cyber-attacks that are aimed at stealing valuable information from target organizations and tend to extend in time. Blocking all APTs is impossible, security experts caution, hence the importance of research on early detection and damage limitation. Whole-system provenance-tracking and provenance trace mining are considered promising as they can help find causal relationships between activities and flag suspicious event sequences as they occur. We introduce an unsupervised method that exploits OS-independent features reflecting process activity to detect realistic APT-like attacks from provenance traces. Anomalous processes are ranked using both frequent and rare event associations learned from traces. Results are then presented as implications which, since interpretable, help leverage causality in explaining the detected anomalies. When evaluated on Transparent Computing program datasets (DARPA), our method outperformed competing approaches.


2020 ◽  
Vol 29 (5) ◽  
pp. 793-811
Author(s):  
Yaoxue Zhang ◽  
Sijing Duan ◽  
Deyu Zhang ◽  
Ju Ren

2019 ◽  
Vol 68 (4) ◽  
pp. 585-596 ◽  
Author(s):  
Liwei Kuang ◽  
Laurence T. Yang ◽  
Qing Zhu ◽  
Jinjun Chen

IEEE Network ◽  
2019 ◽  
Vol 33 (2) ◽  
pp. 132-137 ◽  
Author(s):  
Yuezhi Zhou ◽  
Bowen Yang ◽  
Chao Wu ◽  
Ju Ren ◽  
Yaoxue Zhang

2018 ◽  
Vol 13 (1) ◽  
pp. 64-81
Author(s):  
Weimin Li ◽  
Bin Wang ◽  
Jinfang Sheng ◽  
Xiangyu Hou ◽  
Lin Chen ◽  
...  

2018 ◽  
Vol 118 ◽  
pp. 5-13 ◽  
Author(s):  
Hui Guo ◽  
Ju Ren ◽  
Deyu Zhang ◽  
Yaoxue Zhang ◽  
Junying Hu

Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 981 ◽  
Author(s):  
Weimin Li ◽  
Bin Wang ◽  
Jinfang Sheng ◽  
Ke Dong ◽  
Zitong Li ◽  
...  

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