The proliferation of internet traffic encryption has become a double-edged sword. While it significantly enhances user privacy, it also inadvertently shields cyber-attacks from detection, presenting a formidable chall...The proliferation of internet traffic encryption has become a double-edged sword. While it significantly enhances user privacy, it also inadvertently shields cyber-attacks from detection, presenting a formidable challenge to cybersecurity. Traditional machine learning and deep learning techniques often fall short in identifying encrypted malicious traffic due to their inability to fully extract and utilize the implicit relational and positional information embedded within data packets. This limitation has led to an unresolved challenge in the cybersecurity community: how to effectively extract valuable insights from the complex patterns of traffic packet transmission. Consequently, this paper introduces the TB-Graph model, an encrypted malicious traffic classification model based on a relational graph attention network. The model is a heterogeneous traffic burst graph that embeds side-channel features, which are unaffected by encryption, into the graph nodes and connects them with three different types of burst edges. Subsequently, we design a relational positional coding that prevents the loss of temporal relationships between the original traffic flows during graph transformation. Ultimately, TB-Graph leverages the powerful graph representation learning capabilities of Relational Graph Attention Network (RGAT) to extract latent behavioral features from the burst graph nodes and edge relationships. Experimental results show that TB-Graph outperforms various state-of-the-art methods in fine-grained encrypted malicious traffic classification tasks on two public datasets, indicating its enhanced capability for identifying encrypted malicious traffic.展开更多
SINR distribution and rate overage distribution are crucial for optimization of deployment of Ultra-dense Het Nets.Most existing literatures assume that BSs have full queues and full-buffer traffic.In fact,due to ultr...SINR distribution and rate overage distribution are crucial for optimization of deployment of Ultra-dense Het Nets.Most existing literatures assume that BSs have full queues and full-buffer traffic.In fact,due to ultra-dense deployment of small cells,traffic in small cell varies dramatically in time and space domains.Hence,it is more practical to investigate scenario with burst traffic.In this paper,we consider a two-tier non-uniform ultra-dense Het Net with burst traffic,where macro BSs are located according to Poisson Point Process(PPP),and pico BSs are located according to Poisson Hole Process(PHP).The closed-form expressions of SINR distribution and rate distribution are derived,and then validated through simulation.Our study shows that different from the result of full buffer case,the SINR distribution and rate distribution of users depend on the average transmission probabilities of BSs in burst traffic case.展开更多
Information-centric satellite networks play a crucial role in remote sensing applications,particularly in the transmission of remote sensing images.However,the occurrence of burst traffic poses significant challenges ...Information-centric satellite networks play a crucial role in remote sensing applications,particularly in the transmission of remote sensing images.However,the occurrence of burst traffic poses significant challenges in meeting the increased bandwidth demands.Traditional content delivery networks are ill-equipped to handle such bursts due to their pre-deployed content.In this paper,we propose an optimal replication strategy for mitigating burst traffic in information-centric satellite networks,specifically focusing on the transmission of remote sensing images.Our strategy involves selecting the most optimal replication delivery satellite node when multiple users subscribe to the same remote sensing content within a short time,effectively reducing network transmission data and preventing throughput degradation caused by burst traffic expansion.We formulate the content delivery process as a multi-objective optimization problem and apply Markov decision processes to determine the optimal value for burst traffic reduction.To address these challenges,we leverage federated reinforcement learning techniques.Additionally,we use bloom filters with subdivision and data identification methods to enable rapid retrieval and encoding of remote sensing images.Through software-based simulations using a low Earth orbit satellite constellation,we validate the effectiveness of our proposed strategy,achieving a significant 17%reduction in the average delivery delay.This paper offers valuable insights into efficient content delivery in satellite networks,specifically targeting the transmission of remote sensing images,and presents a promising approach to mitigate burst traffic challenges in information-centric environments.展开更多
The performance of the algorithm of the data channel scheduling algorithm of latest available unscheduled channel with void filling (LAUC-VF) under bursty traffic is presented firstly. A bursty traffic model for optic...The performance of the algorithm of the data channel scheduling algorithm of latest available unscheduled channel with void filling (LAUC-VF) under bursty traffic is presented firstly. A bursty traffic model for optical burst switch performance simulation is also introduced.展开更多
文摘The proliferation of internet traffic encryption has become a double-edged sword. While it significantly enhances user privacy, it also inadvertently shields cyber-attacks from detection, presenting a formidable challenge to cybersecurity. Traditional machine learning and deep learning techniques often fall short in identifying encrypted malicious traffic due to their inability to fully extract and utilize the implicit relational and positional information embedded within data packets. This limitation has led to an unresolved challenge in the cybersecurity community: how to effectively extract valuable insights from the complex patterns of traffic packet transmission. Consequently, this paper introduces the TB-Graph model, an encrypted malicious traffic classification model based on a relational graph attention network. The model is a heterogeneous traffic burst graph that embeds side-channel features, which are unaffected by encryption, into the graph nodes and connects them with three different types of burst edges. Subsequently, we design a relational positional coding that prevents the loss of temporal relationships between the original traffic flows during graph transformation. Ultimately, TB-Graph leverages the powerful graph representation learning capabilities of Relational Graph Attention Network (RGAT) to extract latent behavioral features from the burst graph nodes and edge relationships. Experimental results show that TB-Graph outperforms various state-of-the-art methods in fine-grained encrypted malicious traffic classification tasks on two public datasets, indicating its enhanced capability for identifying encrypted malicious traffic.
基金partially supported by National 863 Program(2014AA01A702)National Basic Research Program of China(973 Program 2012CB316004)National Natural Science Foundation(61271205,61221002 and 61201170)
文摘SINR distribution and rate overage distribution are crucial for optimization of deployment of Ultra-dense Het Nets.Most existing literatures assume that BSs have full queues and full-buffer traffic.In fact,due to ultra-dense deployment of small cells,traffic in small cell varies dramatically in time and space domains.Hence,it is more practical to investigate scenario with burst traffic.In this paper,we consider a two-tier non-uniform ultra-dense Het Net with burst traffic,where macro BSs are located according to Poisson Point Process(PPP),and pico BSs are located according to Poisson Hole Process(PHP).The closed-form expressions of SINR distribution and rate distribution are derived,and then validated through simulation.Our study shows that different from the result of full buffer case,the SINR distribution and rate distribution of users depend on the average transmission probabilities of BSs in burst traffic case.
基金Project supported by the National Natural Science Foundation of China(No.U21A20451)。
文摘Information-centric satellite networks play a crucial role in remote sensing applications,particularly in the transmission of remote sensing images.However,the occurrence of burst traffic poses significant challenges in meeting the increased bandwidth demands.Traditional content delivery networks are ill-equipped to handle such bursts due to their pre-deployed content.In this paper,we propose an optimal replication strategy for mitigating burst traffic in information-centric satellite networks,specifically focusing on the transmission of remote sensing images.Our strategy involves selecting the most optimal replication delivery satellite node when multiple users subscribe to the same remote sensing content within a short time,effectively reducing network transmission data and preventing throughput degradation caused by burst traffic expansion.We formulate the content delivery process as a multi-objective optimization problem and apply Markov decision processes to determine the optimal value for burst traffic reduction.To address these challenges,we leverage federated reinforcement learning techniques.Additionally,we use bloom filters with subdivision and data identification methods to enable rapid retrieval and encoding of remote sensing images.Through software-based simulations using a low Earth orbit satellite constellation,we validate the effectiveness of our proposed strategy,achieving a significant 17%reduction in the average delivery delay.This paper offers valuable insights into efficient content delivery in satellite networks,specifically targeting the transmission of remote sensing images,and presents a promising approach to mitigate burst traffic challenges in information-centric environments.
文摘The performance of the algorithm of the data channel scheduling algorithm of latest available unscheduled channel with void filling (LAUC-VF) under bursty traffic is presented firstly. A bursty traffic model for optical burst switch performance simulation is also introduced.
