Intelligent penetration testing is of great significance for the improvement of the security of information systems,and the critical issue is the planning of penetration test paths.In view of the difficulty for attack...Intelligent penetration testing is of great significance for the improvement of the security of information systems,and the critical issue is the planning of penetration test paths.In view of the difficulty for attackers to obtain complete network information in realistic network scenarios,Reinforcement Learning(RL)is a promising solution to discover the optimal penetration path under incomplete information about the target network.Existing RL-based methods are challenged by the sizeable discrete action space,which leads to difficulties in the convergence.Moreover,most methods still rely on experts’knowledge.To address these issues,this paper proposes a penetration path planning method based on reinforcement learning with episodic memory.First,the penetration testing problem is formally described in terms of reinforcement learning.To speed up the training process without specific prior knowledge,the proposed algorithm introduces episodic memory to store experienced advantageous strategies for the first time.Furthermore,the method offers an exploration strategy based on episodic memory to guide the agents in learning.The design makes full use of historical experience to achieve the purpose of reducing blind exploration and improving planning efficiency.Ultimately,comparison experiments are carried out with the existing RL-based methods.The results reveal that the proposed method has better convergence performance.The running time is reduced by more than 20%.展开更多
Mining penetration testing semantic knowledge hidden in vast amounts of raw penetration testing data is of vital importance for automated penetration testing.Associative rule mining,a data mining technique,has been st...Mining penetration testing semantic knowledge hidden in vast amounts of raw penetration testing data is of vital importance for automated penetration testing.Associative rule mining,a data mining technique,has been studied and explored for a long time.However,few studies have focused on knowledge discovery in the penetration testing area.The experimental result reveals that the long-tail distribution of penetration testing data nullifies the effectiveness of associative rule mining algorithms that are based on frequent pattern.To address this problem,a Bayesian inference based penetration semantic knowledge mining algorithm is proposed.First,a directed bipartite graph model,a kind of Bayesian network,is constructed to formalize penetration testing data.Then,we adopt the maximum likelihood estimate method to optimize the model parameters and decompose a large Bayesian network into smaller networks based on conditional independence of variables for improved solution efficiency.Finally,irrelevant variable elimination is adopted to extract penetration semantic knowledge from the conditional probability distribution of the model.The experimental results show that the proposed method can discover penetration semantic knowledge from raw penetration testing data effectively and efficiently.展开更多
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,at...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.展开更多
To the Editor:De-escalating the treatment of ductal carcinoma in situ(DCIS)of the breast has been investigated in recent years.A 5-year adjuvant endocrine therapy(AET)regimen is the recommended treatment for hormone r...To the Editor:De-escalating the treatment of ductal carcinoma in situ(DCIS)of the breast has been investigated in recent years.A 5-year adjuvant endocrine therapy(AET)regimen is the recommended treatment for hormone receptor-positive DCIS.Selective estrogen receptor modulators(such as the commonly used tamoxifen and toremifene)and aromatase inhibitors(AIs)are the currently used AET drugs.However,long-term AET is accompanied by severe side effects such as menopausal symptoms,endometrial carcinoma,cardiovascular events,venous thromboembolic events,osteoporosis,and joint pain that can significantly reduce patients’quality of life.It was reported that only 50%of the patients completed the recommended 5-year AET mainly because of side effects,while higher adherence rates were observed with shorter-duration regimens.[1,2]Therefore,de-escalation of AET should be considered to reduce the side effects of AET and improve adherence.展开更多
文摘Intelligent penetration testing is of great significance for the improvement of the security of information systems,and the critical issue is the planning of penetration test paths.In view of the difficulty for attackers to obtain complete network information in realistic network scenarios,Reinforcement Learning(RL)is a promising solution to discover the optimal penetration path under incomplete information about the target network.Existing RL-based methods are challenged by the sizeable discrete action space,which leads to difficulties in the convergence.Moreover,most methods still rely on experts’knowledge.To address these issues,this paper proposes a penetration path planning method based on reinforcement learning with episodic memory.First,the penetration testing problem is formally described in terms of reinforcement learning.To speed up the training process without specific prior knowledge,the proposed algorithm introduces episodic memory to store experienced advantageous strategies for the first time.Furthermore,the method offers an exploration strategy based on episodic memory to guide the agents in learning.The design makes full use of historical experience to achieve the purpose of reducing blind exploration and improving planning efficiency.Ultimately,comparison experiments are carried out with the existing RL-based methods.The results reveal that the proposed method has better convergence performance.The running time is reduced by more than 20%.
基金the National Natural Science Foundation of China No.61502528.
文摘Mining penetration testing semantic knowledge hidden in vast amounts of raw penetration testing data is of vital importance for automated penetration testing.Associative rule mining,a data mining technique,has been studied and explored for a long time.However,few studies have focused on knowledge discovery in the penetration testing area.The experimental result reveals that the long-tail distribution of penetration testing data nullifies the effectiveness of associative rule mining algorithms that are based on frequent pattern.To address this problem,a Bayesian inference based penetration semantic knowledge mining algorithm is proposed.First,a directed bipartite graph model,a kind of Bayesian network,is constructed to formalize penetration testing data.Then,we adopt the maximum likelihood estimate method to optimize the model parameters and decompose a large Bayesian network into smaller networks based on conditional independence of variables for improved solution efficiency.Finally,irrelevant variable elimination is adopted to extract penetration semantic knowledge from the conditional probability distribution of the model.The experimental results show that the proposed method can discover penetration semantic knowledge from raw penetration testing data effectively and efficiently.
文摘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.
基金funded by grants from the 2023 Applied Basic research project of Liaoning province(No.2023JH2/101300048 to B.Chen)2021 Science and Technology project of Shenyang(No.21-173-9-07 to G.Zhu).
文摘To the Editor:De-escalating the treatment of ductal carcinoma in situ(DCIS)of the breast has been investigated in recent years.A 5-year adjuvant endocrine therapy(AET)regimen is the recommended treatment for hormone receptor-positive DCIS.Selective estrogen receptor modulators(such as the commonly used tamoxifen and toremifene)and aromatase inhibitors(AIs)are the currently used AET drugs.However,long-term AET is accompanied by severe side effects such as menopausal symptoms,endometrial carcinoma,cardiovascular events,venous thromboembolic events,osteoporosis,and joint pain that can significantly reduce patients’quality of life.It was reported that only 50%of the patients completed the recommended 5-year AET mainly because of side effects,while higher adherence rates were observed with shorter-duration regimens.[1,2]Therefore,de-escalation of AET should be considered to reduce the side effects of AET and improve adherence.
