Passive worms can passively propagate through embedding themselves into some sharing files, which can result in significant damage to unstructured P2P networks. To study the passive worm behaviors, this paper firstly ...Passive worms can passively propagate through embedding themselves into some sharing files, which can result in significant damage to unstructured P2P networks. To study the passive worm behaviors, this paper firstly analyzes and obtains the average delay for all peers in the whole transmitting process due to the limitation of network throughput, and then proposes a mathematical model for the propagation of passive worms over the unstructured P2P networks. The model mainly takes the effect of the network throughput into account, and applies a new healthy files dissemination-based defense strategy according to the file popularity which follows the Zipf distribution. The simulation results show that the propagation of passive worms is mainly governed by the number of hops, initially infected files and uninfected files. The larger the number of hops, the more rapidly the passive worms propagate. If the number of the initially infected files is increased by the attackers, the propagation speed of passive worms increases obviously. A larger size of the uninfected file results in a better attack performance. However, the number of files generated by passive worms is not an important factor governing the propagation of passive worms. The effectiveness of healthy files dissemination strategy is verified. This model can provide a guideline in the control of unstructured P2P networks as well as passive worm defense.展开更多
Deep Reinforcement Learning(DRL)is a class of Machine Learning(ML)that combines Deep Learning with Reinforcement Learning and provides a framework by which a system can learn from its previous actions in an environmen...Deep Reinforcement Learning(DRL)is a class of Machine Learning(ML)that combines Deep Learning with Reinforcement Learning and provides a framework by which a system can learn from its previous actions in an environment to select its efforts in the future efficiently.DRL has been used in many application fields,including games,robots,networks,etc.for creating autonomous systems that improve themselves with experience.It is well acknowledged that DRL is well suited to solve optimization problems in distributed systems in general and network routing especially.Therefore,a novel query routing approach called Deep Reinforcement Learning based Route Selection(DRLRS)is proposed for unstructured P2P networks based on a Deep Q-Learning algorithm.The main objective of this approach is to achieve better retrieval effectiveness with reduced searching cost by less number of connected peers,exchangedmessages,and reduced time.The simulation results shows a significantly improve searching a resource with compression to k-Random Walker and Directed BFS.Here,retrieval effectiveness,search cost in terms of connected peers,and average overhead are 1.28,106,149,respectively.展开更多
基金National Natural Science Foundation of China (No.60633020 and No. 90204012)Natural Science Foundation of Hebei Province (No. F2006000177)
文摘Passive worms can passively propagate through embedding themselves into some sharing files, which can result in significant damage to unstructured P2P networks. To study the passive worm behaviors, this paper firstly analyzes and obtains the average delay for all peers in the whole transmitting process due to the limitation of network throughput, and then proposes a mathematical model for the propagation of passive worms over the unstructured P2P networks. The model mainly takes the effect of the network throughput into account, and applies a new healthy files dissemination-based defense strategy according to the file popularity which follows the Zipf distribution. The simulation results show that the propagation of passive worms is mainly governed by the number of hops, initially infected files and uninfected files. The larger the number of hops, the more rapidly the passive worms propagate. If the number of the initially infected files is increased by the attackers, the propagation speed of passive worms increases obviously. A larger size of the uninfected file results in a better attack performance. However, the number of files generated by passive worms is not an important factor governing the propagation of passive worms. The effectiveness of healthy files dissemination strategy is verified. This model can provide a guideline in the control of unstructured P2P networks as well as passive worm defense.
基金Authors would like to thank the Deanship of Scientific Research at Shaqra University for supporting this work under Project No.g01/n04.
文摘Deep Reinforcement Learning(DRL)is a class of Machine Learning(ML)that combines Deep Learning with Reinforcement Learning and provides a framework by which a system can learn from its previous actions in an environment to select its efforts in the future efficiently.DRL has been used in many application fields,including games,robots,networks,etc.for creating autonomous systems that improve themselves with experience.It is well acknowledged that DRL is well suited to solve optimization problems in distributed systems in general and network routing especially.Therefore,a novel query routing approach called Deep Reinforcement Learning based Route Selection(DRLRS)is proposed for unstructured P2P networks based on a Deep Q-Learning algorithm.The main objective of this approach is to achieve better retrieval effectiveness with reduced searching cost by less number of connected peers,exchangedmessages,and reduced time.The simulation results shows a significantly improve searching a resource with compression to k-Random Walker and Directed BFS.Here,retrieval effectiveness,search cost in terms of connected peers,and average overhead are 1.28,106,149,respectively.
