@Article{cmc.2020.011071,
AUTHOR = {Tairan Hu, Tianyang Zhou, Yichao Zang, Qingxian Wang, Hang Li},
TITLE = {APU-D* Lite: Attack Planning under Uncertainty Based on  D* Lite},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {2},
PAGES = {1795--1807},
URL = {http://www.techscience.com/cmc/v65n2/39907},
ISSN = {1546-2226},
ABSTRACT = {With serious cybersecurity situations and frequent network attacks, the demands 
for automated pentests continue to increase, and the key issue lies in attack planning. 
Considering the limited viewpoint of the attacker, attack planning under uncertainty is 
more suitable and practical for pentesting than is the traditional planning approach, but it 
also poses some challenges. To address the efficiency problem in uncertainty planning, we 
propose the APU-D* Lite algorithm in this paper. First, the pentest framework is mapped 
to the planning problem with the Planning Domain Definition Language (PDDL). Next, 
we develop the pentest information graph to organize network information and assess
relevant exploitation actions, which helps to simplify the problem scale. Then, the APUD* Lite algorithm is introduced based on the idea of incremental heuristic searching. This 
method plans for both hosts and actions, which meets the requirements of pentesting. With 
the pentest information graph as the input, the output is an alternating host and action 
sequence. In experiments, we use the attack success rate to represent the uncertainty level 
of the environment. The result shows that APU-D* Lite displays better reliability and 
efficiency than classical planning algorithms at different attack success rates.},
DOI = {10.32604/cmc.2020.011071}
}