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
This paper describes a routing algorithm for risk scanning agents using ant colony algorithm in P2P（peerto peer） network. Every peer in the P2P network is capable of updating its routing table in a real-time way, wh...This paper describes a routing algorithm for risk scanning agents using ant colony algorithm in P2P（peerto peer） network. Every peer in the P2P network is capable of updating its routing table in a real-time way, which enables agents to dynamically and automatically select, according to current traffic condition of the network, the global optimal traversal path. An adjusting mechanism is given to adjust the routing table when peers join or leave. By means of exchanging pheromone intensity of part of paths, the algorithm provides agents with more choices as to which one to move and avoids prematurely reaching local optimal path. And parameters of the algorithm are determined by lots of simulation testing. And we also compare with other routing algorithms in unstructured P2P network in the end.展开更多
In order to reduce the maintenance cost of structured Peer-to-Peer (P2P),Clone Node Protocol (CNP) based on user behavior is proposed.CNP considers the regularity of user behavior and uses the method of clone node.A B...In order to reduce the maintenance cost of structured Peer-to-Peer (P2P),Clone Node Protocol (CNP) based on user behavior is proposed.CNP considers the regularity of user behavior and uses the method of clone node.A Bidirectional Clone Node Chord model (BCNChord) based on CNP protocol is designed and realized.In BCNChord,Anticlockwise Searching Algorithm,Difference Push Synchronize Algorithm and Optimal Maintenance Algorithm are put forward to increase the performances.In experiments,according to the frequency of nodes,the maintenance cost of BCNChord can be 3.5%～32.5% lower than that of Chord.In the network of 212 nodes,the logic path hop is steady at 6,which is much more prior to 12 of Chord and 10 of CNChord.Theoretical analysis and experimental results show that BCNChord can effectively reduce the maintenance cost of its structure and simultaneously improve the query efficiency up to (1/4)O(logN).BCNChord is more suitable for highly dynamic environment and higher real-time system.展开更多
Decentralized and unstructured peer-to-peer applications such as Gnutella are attractive because they require no centralized directories and no precise control over network topology or data placement. Search algorithm...Decentralized and unstructured peer-to-peer applications such as Gnutella are attractive because they require no centralized directories and no precise control over network topology or data placement. Search algorithm is the major component of the distributed system and its efficiency also does influence the systems performance. However the flooding-based query algorithm used in Gnutella produces huge traffic and does not scale well. Gnutella-like P2P topology has power-law characteristic, so a search algorithm was proposed based on high degree nodes of power-law network, High Degree Nodes-Based Search （HDNBS）. Extensive simulation results show that this algorithm performs on power-law networks very well, achieves almost 100% success rates, produces O（logN） messages per query and can locate target file within O（lagN） hops.展开更多
Media streaming delivery in wireless ad hoc networks is challenging due to the stringent resource restrictions,po-tential high loss rate and the decentralized architecture. To support long and high-quality streams,one...Media streaming delivery in wireless ad hoc networks is challenging due to the stringent resource restrictions,po-tential high loss rate and the decentralized architecture. To support long and high-quality streams,one viable approach is that a media stream is partitioned into segments,and then the segments are replicated in a network and served in a peer-to-peer(P2P) fashion. However,the searching strategy for segments is one key problem with the approach. This paper proposes a hybrid ants-like search algorithm(HASA) for P2P media streaming distribution in ad hoc networks. It takes the advantages of random walks and ants-like algorithms for searching in unstructured P2P networks,such as low transmitting latency,less jitter times,and low unnecessary traffic. We quantify the performance of our scheme in terms of response time,jitter times,and network messages for media streaming distribution. Simulation results showed that it can effectively improve the search efficiency for P2P media streaming distribution in ad hoc networks.展开更多
The trustworthiness and security of routing in the existing Peer-to-Peer (P2P) networks can not be ensured because of the diversity of the strategies of P2P nodes. This paper firstly uses game theory to establish game...The trustworthiness and security of routing in the existing Peer-to-Peer (P2P) networks can not be ensured because of the diversity of the strategies of P2P nodes. This paper firstly uses game theory to establish game model of the strategies and profits of various types of routing nodes. Then,two incentive mechanisms for the corresponding stages of P2P trustworthy routing are proposed,namely trust associated mechanism and trust compensated mechanism. Simulation results show that the incentive mechanisms proposed in this paper will encourage cooperation actions of good nodes and restrain malicious actions of bad nodes,which ensure the trustworthiness of routing consequently.展开更多
We present an effective routing solution for the backbone of hierarchical MANETs. </span></span><span><span><span style="font-family:""><span style="font-family:Ver...We present an effective routing solution for the backbone of hierarchical MANETs. </span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">Our solution leverages the storage and retrieval mechanisms of a Distributed Hash Table (DHT) common to many (structured) P2P overlays. Th</span><span style="font-family:Verdana;">e DHT provides routing information in a decentralized fash</span><span style="font-family:Verdana;">ion, while supporting different forms of node and network mobility. We split a flat network into clusters, each having a gateway who participates in a DHT overlay. These g</span><span style="font-family:Verdana;">ateways interconnect the clusters in a backbone network. Two routing </span><span style="font-family:Verdana;">approaches for the backbone are explore</span><span style="font-family:Verdana;">d: floodi</span><span style="font-family:Verdana;">ng and a new solution exploit</span><span style="font-family:Verdana;">ing the storage and retrieval capabilities of a P2P overlay based on a DHT.</span><span style="font-family:Verdana;"> We </span><span style="font-family:Verdana;">implement both approaches in a net</span><span style="font-family:Verdana;">work simulator and thoroughly evaluate th</span><span style="font-family:Verdana;">e performance of the proposed scheme using a range of stati</span><span style="font-family:Verdana;">c and mobile scenarios. We also compare our solution against flooding. The simulation results show that our solution, even in the presence of mobility, achieved well abo</span><span style="font-family:Verdana;">ve 90% success rates and maintained very low and constant round tr</span><span style="font-family:Verdana;">ip times, unlike the flooding approach. In fact, the performance of the proposed </span><span style="font-family:Verdana;">inter-cluster routing solution, in many cases, is comparable to the perfo</span><span style="font-family:Verdana;">rma</span><span style="font-family:Verdana;">nce of the intra-cluster routing case. The advantage of our proposed ap</span><span style="font-family:Verdana;">proach compared to flooding increases as the number of clusters increases, demonstrating the superior scalability of our proposed approach.展开更多
As one of the underlying technologies of the blockchain,the consensus algorithm plays a vital role in ensuring security and efficiency.As a consensus algorithm for the private blockchain,Raft has better performance th...As one of the underlying technologies of the blockchain,the consensus algorithm plays a vital role in ensuring security and efficiency.As a consensus algorithm for the private blockchain,Raft has better performance than the rest of the consensus algorithms,and it does not cause problems such as the concentrated hashing power,resource waste and fork.However,Raft can only be used in a non-byzantine environment with a small network size.In order to enable Raft to be used in a large-scale network with a certain number of byzantine nodes,this paper combines Raft and credit model to propose a Raft blockchain consensus algorithm based on credit model CRaft.In the node credit evaluation phase,RBF-based support vector machine is used as the anomaly detection method,and the node credit evaluation model is constructed.Then the Trust Nodes List(TNL)mechanism is introduced to make the consensus phase in a creditable network environment.Finally,the common node is synchronized to the consensus node to update the blockchain of the entire network.Experiments show that CRaft has better throughput and lower latency than the commonly used consortium blockchain consensus algorithm PBFT(Practical Byzantine Fault Tolerance).展开更多
A hierarchical peer-to-peer（P2P）model and a data fusion method for network security situation awareness system are proposed to improve the efficiency of distributed security behavior monitoring network.The single po...A hierarchical peer-to-peer（P2P）model and a data fusion method for network security situation awareness system are proposed to improve the efficiency of distributed security behavior monitoring network.The single point failure of data analysis nodes is avoided by this P2P model,in which a greedy data forwarding method based on node priority and link delay is devised to promote the efficiency of data analysis nodes.And the data fusion method based on repulsive theory-Dumpster/Shafer（PSORT-DS）is used to deal with the challenge of multi-source alarm information.This data fusion method debases the false alarm rate.Compared with improved Dumpster/Shafer（DS）theoretical method based on particle swarm optimization（PSO）and classical DS evidence theoretical method,the proposed model reduces false alarm rate by 3%and 7%,respectively,whereas their detection rate increases by 4%and 16%,respectively.展开更多
One of the key challenges in ad-hoc networks is the resource discovery problem.How efciently&quickly the queried resource/object can be resolved in such a highly dynamic self-evolving network is the underlying que...One of the key challenges in ad-hoc networks is the resource discovery problem.How efciently&quickly the queried resource/object can be resolved in such a highly dynamic self-evolving network is the underlying question?Broadcasting is a basic technique in the Mobile Ad-hoc Networks(MANETs),and it refers to sending a packet from one node to every other node within the transmission range.Flooding is a type of broadcast where the received packet is retransmitted once by every node.The naive ooding technique oods the network with query messages,while the random walk scheme operates by contacting subsets of each node’s neighbors at every step,thereby restricting the search space.Many earlier works have mainly focused on the simulation-based analysis of ooding technique,and its variants,in a wired network scenario.Although,there have been some empirical studies in peer-to-peer(P2P)networks,the analytical results are still lacking,especially in the context of mobile P2P networks.In this article,we mathematically model different widely used existing search techniques,and compare with the proposed improved random walk method,a simple lightweight approach suitable for the non-DHT architecture.We provide analytical expressions to measure the performance of the different ooding-based search techniques,and our proposed technique.We analytically derive 3 relevant key performance measures,i.e.,the avg.number of steps needed to nd a resource,the probability of locating a resource,and the avg.number of messages generated during the entire search process.展开更多
The rise of automation with Machine-Type Communication(MTC)holds great potential in developing Industrial Internet of Things(IIoT)-based applications such as smart cities,Intelligent Transportation Systems(ITS),supply...The rise of automation with Machine-Type Communication(MTC)holds great potential in developing Industrial Internet of Things(IIoT)-based applications such as smart cities,Intelligent Transportation Systems(ITS),supply chains,and smart industries without any human intervention.However,MTC has to cope with significant security challenges due to heterogeneous data,public network connectivity,and inadequate security mechanism.To overcome the aforementioned issues,we have proposed a blockchain and garlic-routing-based secure data exchange framework,i.e.,GRADE,which alleviates the security constraints and maintains the stable connection in MTC.First,the Long-Short-Term Memory(LSTM)-based Nadam optimizer efficiently predicts the class label,i.e.,malicious and non-malicious,and forwards the non-malicious data requests of MTC to the Garlic Routing(GR)network.The GR network assigns a unique ElGamal encrypted session tag to each machine partaking in MTC.Then,an Advanced Encryption Standard(AES)is applied to encrypt the MTC data requests.Further,the InterPlanetary File System(IPFS)-based blockchain is employed to store the machine's session tags,which increases the scalability of the proposed GRADE framework.Additionally,the proposed framework has utilized the indispensable benefits of the 6G network to enhance the network performance of MTC.Lastly,the proposed GRADE framework is evaluated against different performance metrics such as scalability,packet loss,accuracy,and compromised rate of the MTC data request.The results show that the GRADE framework outperforms the baseline methods in terms of accuracy,i.e.,98.9%,compromised rate,i.e.,18.5%,scalability,i.e.,47.2%,and packet loss ratio,i.e.,24.3%.展开更多
A P2P approaches to extend the ability of Video on Demand systems to serve more users. In the proposed system users share with each other the media data obtained and the media server is no longer the only source to ge...A P2P approaches to extend the ability of Video on Demand systems to serve more users. In the proposed system users share with each other the media data obtained and the media server is no longer the only source to get data from, thereby, the load on the media server could be greatly alleviated and the overall system capacity increases and more users could be served. The P2P streaming system introduces efficient searching;data transfer dynamically monitoring and initial buffering to maintain a high quality of playback. Its provider selection policy helps to reduce the load of the underlying network by avoiding remote data transfer.展开更多
Towards the problems of existing detection methods,a novel real-time detection method(DMFIF) based on fractal and information fusion is proposed.It focuses on the intrinsic macroscopic characteristics of network,which...Towards the problems of existing detection methods,a novel real-time detection method(DMFIF) based on fractal and information fusion is proposed.It focuses on the intrinsic macroscopic characteristics of network,which reflect not the "unique" abnormalities of P2P botnets but the "common" abnormalities of them.It regards network traffic as the signal,and synthetically considers the macroscopic characteristics of network under different time scales with the fractal theory,including the self-similarity and the local singularity,which don't vary with the topology structures,the protocols and the attack types of P2P botnet.At first detect traffic abnormalities of the above characteristics with the nonparametric CUSUM algorithm,and achieve the final result by fusing the above detection results with the Dempster-Shafer evidence theory.Moreover,the side effect on detecting P2P botnet which web applications generated is considered.The experiments show that DMFIF can detect P2P botnet with a higher degree of precision.展开更多
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.
基金Supported by the National Natural Science Foun-dation of China (60403027) Natural Science Foundation of HubeiProvince (2005ABA258) the Opening Foundation of State KeyLaboratory of Software Engineering (SKLSE05-07)
文摘This paper describes a routing algorithm for risk scanning agents using ant colony algorithm in P2P（peerto peer） network. Every peer in the P2P network is capable of updating its routing table in a real-time way, which enables agents to dynamically and automatically select, according to current traffic condition of the network, the global optimal traversal path. An adjusting mechanism is given to adjust the routing table when peers join or leave. By means of exchanging pheromone intensity of part of paths, the algorithm provides agents with more choices as to which one to move and avoids prematurely reaching local optimal path. And parameters of the algorithm are determined by lots of simulation testing. And we also compare with other routing algorithms in unstructured P2P network in the end.
基金supported by the National Natural Science Foundation of China under Grant No.61100205Science and Technology Project of Beijing Municipal Education Commission under Grant No.KM201110016006Doctor Start-up Foundation of BUCEA under Grant No.101002508
文摘In order to reduce the maintenance cost of structured Peer-to-Peer (P2P),Clone Node Protocol (CNP) based on user behavior is proposed.CNP considers the regularity of user behavior and uses the method of clone node.A Bidirectional Clone Node Chord model (BCNChord) based on CNP protocol is designed and realized.In BCNChord,Anticlockwise Searching Algorithm,Difference Push Synchronize Algorithm and Optimal Maintenance Algorithm are put forward to increase the performances.In experiments,according to the frequency of nodes,the maintenance cost of BCNChord can be 3.5%～32.5% lower than that of Chord.In the network of 212 nodes,the logic path hop is steady at 6,which is much more prior to 12 of Chord and 10 of CNChord.Theoretical analysis and experimental results show that BCNChord can effectively reduce the maintenance cost of its structure and simultaneously improve the query efficiency up to (1/4)O(logN).BCNChord is more suitable for highly dynamic environment and higher real-time system.
文摘Decentralized and unstructured peer-to-peer applications such as Gnutella are attractive because they require no centralized directories and no precise control over network topology or data placement. Search algorithm is the major component of the distributed system and its efficiency also does influence the systems performance. However the flooding-based query algorithm used in Gnutella produces huge traffic and does not scale well. Gnutella-like P2P topology has power-law characteristic, so a search algorithm was proposed based on high degree nodes of power-law network, High Degree Nodes-Based Search （HDNBS）. Extensive simulation results show that this algorithm performs on power-law networks very well, achieves almost 100% success rates, produces O（logN） messages per query and can locate target file within O（lagN） hops.
基金Project supported by the National Natural Science Foundation of China (No. 60302004)the Natural Science Foundation of HubeiProvince, China (No. 2005ABA264)
文摘Media streaming delivery in wireless ad hoc networks is challenging due to the stringent resource restrictions,po-tential high loss rate and the decentralized architecture. To support long and high-quality streams,one viable approach is that a media stream is partitioned into segments,and then the segments are replicated in a network and served in a peer-to-peer(P2P) fashion. However,the searching strategy for segments is one key problem with the approach. This paper proposes a hybrid ants-like search algorithm(HASA) for P2P media streaming distribution in ad hoc networks. It takes the advantages of random walks and ants-like algorithms for searching in unstructured P2P networks,such as low transmitting latency,less jitter times,and low unnecessary traffic. We quantify the performance of our scheme in terms of response time,jitter times,and network messages for media streaming distribution. Simulation results showed that it can effectively improve the search efficiency for P2P media streaming distribution in ad hoc networks.
基金Supported by the Hi-Tech R&D Program (863) of China (2006AA01Z232)the Research Innovation Program for Graduate Student in Jiangsu Province (CX07B-11OZ)
文摘The trustworthiness and security of routing in the existing Peer-to-Peer (P2P) networks can not be ensured because of the diversity of the strategies of P2P nodes. This paper firstly uses game theory to establish game model of the strategies and profits of various types of routing nodes. Then,two incentive mechanisms for the corresponding stages of P2P trustworthy routing are proposed,namely trust associated mechanism and trust compensated mechanism. Simulation results show that the incentive mechanisms proposed in this paper will encourage cooperation actions of good nodes and restrain malicious actions of bad nodes,which ensure the trustworthiness of routing consequently.
文摘We present an effective routing solution for the backbone of hierarchical MANETs. </span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">Our solution leverages the storage and retrieval mechanisms of a Distributed Hash Table (DHT) common to many (structured) P2P overlays. Th</span><span style="font-family:Verdana;">e DHT provides routing information in a decentralized fash</span><span style="font-family:Verdana;">ion, while supporting different forms of node and network mobility. We split a flat network into clusters, each having a gateway who participates in a DHT overlay. These g</span><span style="font-family:Verdana;">ateways interconnect the clusters in a backbone network. Two routing </span><span style="font-family:Verdana;">approaches for the backbone are explore</span><span style="font-family:Verdana;">d: floodi</span><span style="font-family:Verdana;">ng and a new solution exploit</span><span style="font-family:Verdana;">ing the storage and retrieval capabilities of a P2P overlay based on a DHT.</span><span style="font-family:Verdana;"> We </span><span style="font-family:Verdana;">implement both approaches in a net</span><span style="font-family:Verdana;">work simulator and thoroughly evaluate th</span><span style="font-family:Verdana;">e performance of the proposed scheme using a range of stati</span><span style="font-family:Verdana;">c and mobile scenarios. We also compare our solution against flooding. The simulation results show that our solution, even in the presence of mobility, achieved well abo</span><span style="font-family:Verdana;">ve 90% success rates and maintained very low and constant round tr</span><span style="font-family:Verdana;">ip times, unlike the flooding approach. In fact, the performance of the proposed </span><span style="font-family:Verdana;">inter-cluster routing solution, in many cases, is comparable to the perfo</span><span style="font-family:Verdana;">rma</span><span style="font-family:Verdana;">nce of the intra-cluster routing case. The advantage of our proposed ap</span><span style="font-family:Verdana;">proach compared to flooding increases as the number of clusters increases, demonstrating the superior scalability of our proposed approach.
基金Supported by the National Natural Science Foundation of China(61672297)。
文摘As one of the underlying technologies of the blockchain,the consensus algorithm plays a vital role in ensuring security and efficiency.As a consensus algorithm for the private blockchain,Raft has better performance than the rest of the consensus algorithms,and it does not cause problems such as the concentrated hashing power,resource waste and fork.However,Raft can only be used in a non-byzantine environment with a small network size.In order to enable Raft to be used in a large-scale network with a certain number of byzantine nodes,this paper combines Raft and credit model to propose a Raft blockchain consensus algorithm based on credit model CRaft.In the node credit evaluation phase,RBF-based support vector machine is used as the anomaly detection method,and the node credit evaluation model is constructed.Then the Trust Nodes List(TNL)mechanism is introduced to make the consensus phase in a creditable network environment.Finally,the common node is synchronized to the consensus node to update the blockchain of the entire network.Experiments show that CRaft has better throughput and lower latency than the commonly used consortium blockchain consensus algorithm PBFT(Practical Byzantine Fault Tolerance).
基金Supported by the National Natural Science Foundation of China(61370212)the Research Fund for the Doctoral Program of Higher Education of China(20122304130002)+1 种基金the Natural Science Foundation of Heilongjiang Province(ZD 201102)the Fundamental Research Fund for the Central Universities(HEUCFZ1213,HEUCF100601)
文摘A hierarchical peer-to-peer（P2P）model and a data fusion method for network security situation awareness system are proposed to improve the efficiency of distributed security behavior monitoring network.The single point failure of data analysis nodes is avoided by this P2P model,in which a greedy data forwarding method based on node priority and link delay is devised to promote the efficiency of data analysis nodes.And the data fusion method based on repulsive theory-Dumpster/Shafer（PSORT-DS）is used to deal with the challenge of multi-source alarm information.This data fusion method debases the false alarm rate.Compared with improved Dumpster/Shafer（DS）theoretical method based on particle swarm optimization（PSO）and classical DS evidence theoretical method,the proposed model reduces false alarm rate by 3%and 7%,respectively,whereas their detection rate increases by 4%and 16%,respectively.
文摘One of the key challenges in ad-hoc networks is the resource discovery problem.How efciently&quickly the queried resource/object can be resolved in such a highly dynamic self-evolving network is the underlying question?Broadcasting is a basic technique in the Mobile Ad-hoc Networks(MANETs),and it refers to sending a packet from one node to every other node within the transmission range.Flooding is a type of broadcast where the received packet is retransmitted once by every node.The naive ooding technique oods the network with query messages,while the random walk scheme operates by contacting subsets of each node’s neighbors at every step,thereby restricting the search space.Many earlier works have mainly focused on the simulation-based analysis of ooding technique,and its variants,in a wired network scenario.Although,there have been some empirical studies in peer-to-peer(P2P)networks,the analytical results are still lacking,especially in the context of mobile P2P networks.In this article,we mathematically model different widely used existing search techniques,and compare with the proposed improved random walk method,a simple lightweight approach suitable for the non-DHT architecture.We provide analytical expressions to measure the performance of the different ooding-based search techniques,and our proposed technique.We analytically derive 3 relevant key performance measures,i.e.,the avg.number of steps needed to nd a resource,the probability of locating a resource,and the avg.number of messages generated during the entire search process.
文摘The rise of automation with Machine-Type Communication(MTC)holds great potential in developing Industrial Internet of Things(IIoT)-based applications such as smart cities,Intelligent Transportation Systems(ITS),supply chains,and smart industries without any human intervention.However,MTC has to cope with significant security challenges due to heterogeneous data,public network connectivity,and inadequate security mechanism.To overcome the aforementioned issues,we have proposed a blockchain and garlic-routing-based secure data exchange framework,i.e.,GRADE,which alleviates the security constraints and maintains the stable connection in MTC.First,the Long-Short-Term Memory(LSTM)-based Nadam optimizer efficiently predicts the class label,i.e.,malicious and non-malicious,and forwards the non-malicious data requests of MTC to the Garlic Routing(GR)network.The GR network assigns a unique ElGamal encrypted session tag to each machine partaking in MTC.Then,an Advanced Encryption Standard(AES)is applied to encrypt the MTC data requests.Further,the InterPlanetary File System(IPFS)-based blockchain is employed to store the machine's session tags,which increases the scalability of the proposed GRADE framework.Additionally,the proposed framework has utilized the indispensable benefits of the 6G network to enhance the network performance of MTC.Lastly,the proposed GRADE framework is evaluated against different performance metrics such as scalability,packet loss,accuracy,and compromised rate of the MTC data request.The results show that the GRADE framework outperforms the baseline methods in terms of accuracy,i.e.,98.9%,compromised rate,i.e.,18.5%,scalability,i.e.,47.2%,and packet loss ratio,i.e.,24.3%.
文摘A P2P approaches to extend the ability of Video on Demand systems to serve more users. In the proposed system users share with each other the media data obtained and the media server is no longer the only source to get data from, thereby, the load on the media server could be greatly alleviated and the overall system capacity increases and more users could be served. The P2P streaming system introduces efficient searching;data transfer dynamically monitoring and initial buffering to maintain a high quality of playback. Its provider selection policy helps to reduce the load of the underlying network by avoiding remote data transfer.
基金supported by National High Technical Research and Development Program of China(863 Program)under Grant No.2011AA7031024GNational Natural Science Foundation of China under Grant No.90204014
文摘Towards the problems of existing detection methods,a novel real-time detection method(DMFIF) based on fractal and information fusion is proposed.It focuses on the intrinsic macroscopic characteristics of network,which reflect not the "unique" abnormalities of P2P botnets but the "common" abnormalities of them.It regards network traffic as the signal,and synthetically considers the macroscopic characteristics of network under different time scales with the fractal theory,including the self-similarity and the local singularity,which don't vary with the topology structures,the protocols and the attack types of P2P botnet.At first detect traffic abnormalities of the above characteristics with the nonparametric CUSUM algorithm,and achieve the final result by fusing the above detection results with the Dempster-Shafer evidence theory.Moreover,the side effect on detecting P2P botnet which web applications generated is considered.The experiments show that DMFIF can detect P2P botnet with a higher degree of precision.
