APU-D* Lite: Attack Planning under Uncertainty Based on D* Lite

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 APU-D*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.

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

The development of distributed renewable energy,such as photovoltaic power and wind power generation,makes the energy system cleaner,and is of great significance in reducing carbon emissions.However,weather can affect distributed renewable energy power generation,and the uncertainty of output brings challenges to uncertainty planning for distributed renewable energy.Energy systems with high penetration of distributed renewable energy involve the high-dimensional,nonlinear dynamics of large-scale complex systems,and the optimal solution of the uncertainty model is a difficult problem.From the perspective of statistical machine learning,the theory of planning of distributed renewable energy systems under uncertainty is reviewed and some key technologies are put forward for applying advanced artificial intelligence to distributed renewable power uncertainty planning.

Modelling medium-and long-term purchasing plans for environment-orientated container trucks:a case study of Yangtze River port

The transportation sector is the most significant contributor to anthropogenic greenhouse gas(GHG)emissions.Particularly,maritime transportation,which is predominantly powered by fossil-fuel engines,accounts for more than 90%of world freight movement and emits 3%of global carbon dioxide(CO_(2))emissions.China is the world’s largest emitter of CO_(2 )and plays a key role in mitigating global climate change.In order to tackle this pressing concern,this study analyses the port’s throughput,the current number of trucks and their emissions during the container truck purchasing process.Previous studies about container truck purchasing plans mostly focused on the trucks’price and port needs.The objective of this study is to minimize the total cost of a port’s inland transportation using optimization technique such as the interval uncertainty planning model to convert container truck emissions into social costs.The study considers the port of Yangtze as a case study.The study has designed two scenarios.(i)The base scenario(business-asusual,BAU)is used to quantify the relationship between pollutant emissions and system cost.In the base scenario,no environmental control facilities are used during the planning period,and there is no need to purchase new energy container trucks.(ii)The expected scenario(Scenario A)is for three planning periods.In Scenario A,the emissions levels are required to remain at the same level as the first planning period during the whole planning period.By solving the above model,the number of all truck types,system cost,container throughput and truck emissions in the port area were analysed.The results showed that if no emission reduction control measures are implemented in the next 9 years,the growth rate of pollutants in the port area could reach 20%.In addition,the findings showed clearly that truck emissions are reduced by purchasing new energy trucks and restricting the number of fossil-fuel(diesel)trucks.This study could also help to minimize system costs associated with port planning and management.