Pub Date : 2022-10-31DOI: 10.23919/CNSM55787.2022.9965128
Bjørn Ivar Teigen, N. Davies, P. Thompson, K. Ellefsen, T. Skeie, J. Tørresen
This work introduces a class of network performance models designed to capture variations in network quality on diverse timescales. By explicitly modeling how quality changes over time, the proposed models enable computation of performance metrics that are beyond the scope of steady-state methods such as Markov chains. We use the quality attenuation (ΔQ) metric to quantify network quality, and ΔQ generative models specify how quality attenuation varies over time. Variation over time is modeled using a finite state machine with timed state transitions. We show how the models can be used to shed light on practical problems by presenting novel results for the problem of buffer sizing. In addition to the buffer sizing results, this work presents the ΔQ generative model structure and the basic algorithms needed to work with the models.
{"title":"ΔQ Generative Models: Modeling Time-Variation in Network Quality","authors":"Bjørn Ivar Teigen, N. Davies, P. Thompson, K. Ellefsen, T. Skeie, J. Tørresen","doi":"10.23919/CNSM55787.2022.9965128","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9965128","url":null,"abstract":"This work introduces a class of network performance models designed to capture variations in network quality on diverse timescales. By explicitly modeling how quality changes over time, the proposed models enable computation of performance metrics that are beyond the scope of steady-state methods such as Markov chains. We use the quality attenuation (ΔQ) metric to quantify network quality, and ΔQ generative models specify how quality attenuation varies over time. Variation over time is modeled using a finite state machine with timed state transitions. We show how the models can be used to shed light on practical problems by presenting novel results for the problem of buffer sizing. In addition to the buffer sizing results, this work presents the ΔQ generative model structure and the basic algorithms needed to work with the models.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125004810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-31DOI: 10.23919/CNSM55787.2022.9964907
Alvi Ataur Khalil, M. Rahman
In military operations, unmanned aerial vehicles (UAVs) have been heavily utilized in recent years. However, due to the antenna installment regulation, UAVs cannot be controlled by human operators in a restricted area. Hence, artificial intelligence (AI)-driven UAVs are the practical solution to this out-of-coverage problem. With the increased use of autonomous UAVs in military applications, defense systems are deployed to target and shoot down the enemy UAVs in operation. Thus, UAVs are needed to be trained, not only to achieve goals but also to avoid static and dynamic hostile defense systems. In this work, we propose FED-UP, a federated deep reinforcement learning (DRL)-based UAV path planning framework, that enables UAVs to carry out missions in a hostile environment with a dynamic defense system. The federated learning (FL) based training accelerates the reinforcement learning process and improves model performance. We additionally introduce significant reply memory buffer (SRMB) to quicken the training process more, by selecting the crucial experiences during the training period. The experimental results validate the efficiency of the proposed model in controlling UAVs in dynamic, hostile environments.
{"title":"FED-UP: Federated Deep Reinforcement Learning-based UAV Path Planning against Hostile Defense System","authors":"Alvi Ataur Khalil, M. Rahman","doi":"10.23919/CNSM55787.2022.9964907","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964907","url":null,"abstract":"In military operations, unmanned aerial vehicles (UAVs) have been heavily utilized in recent years. However, due to the antenna installment regulation, UAVs cannot be controlled by human operators in a restricted area. Hence, artificial intelligence (AI)-driven UAVs are the practical solution to this out-of-coverage problem. With the increased use of autonomous UAVs in military applications, defense systems are deployed to target and shoot down the enemy UAVs in operation. Thus, UAVs are needed to be trained, not only to achieve goals but also to avoid static and dynamic hostile defense systems. In this work, we propose FED-UP, a federated deep reinforcement learning (DRL)-based UAV path planning framework, that enables UAVs to carry out missions in a hostile environment with a dynamic defense system. The federated learning (FL) based training accelerates the reinforcement learning process and improves model performance. We additionally introduce significant reply memory buffer (SRMB) to quicken the training process more, by selecting the crucial experiences during the training period. The experimental results validate the efficiency of the proposed model in controlling UAVs in dynamic, hostile environments.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126022274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-31DOI: 10.23919/CNSM55787.2022.9964525
Max Helm, Florian Wiedner, G. Carle
Modeling extreme latencies in communication net-works can contribute information to network planning and flow admission under service level agreements. Extreme Value Theory is such an approach that utilizes real-world measurement data. It is often applied without verifying the resulting model predictions on larger datasets. Here we show that such models can provide accurate predictions over larger datasets while being applied to 100 random network topologies and configurations. We found that applying derived models with a bounded tail to a twentyfold time period results in a prediction accuracy of 75% for extreme latency exceedances. Furthermore, we show that tail latency quantiles can be predicted on a flow level with median absolute percentage errors ranging from 0.7% to 16.8%. Therefore, we consider this approach to be useful for dimensioning networks under latency-constrained service level agreements.
{"title":"Flow-level Tail Latency Estimation and Verification based on Extreme Value Theory","authors":"Max Helm, Florian Wiedner, G. Carle","doi":"10.23919/CNSM55787.2022.9964525","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964525","url":null,"abstract":"Modeling extreme latencies in communication net-works can contribute information to network planning and flow admission under service level agreements. Extreme Value Theory is such an approach that utilizes real-world measurement data. It is often applied without verifying the resulting model predictions on larger datasets. Here we show that such models can provide accurate predictions over larger datasets while being applied to 100 random network topologies and configurations. We found that applying derived models with a bounded tail to a twentyfold time period results in a prediction accuracy of 75% for extreme latency exceedances. Furthermore, we show that tail latency quantiles can be predicted on a flow level with median absolute percentage errors ranging from 0.7% to 16.8%. Therefore, we consider this approach to be useful for dimensioning networks under latency-constrained service level agreements.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131915370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-31DOI: 10.23919/CNSM55787.2022.9964551
Qingsong Liu, Yaoyu Zhang
We consider the caching problem in an online learning perspective, i.e., no model assumptions and prior knowledge for the file request sequence. Our goal is to design an efficient on-line caching policy with minimal regret, i.e, minimizing the total number of cache-miss with respect to the best static configuration in hindsight. Previous studies such as Follow-The-Perturbed-Leader (FTPL) caching policy, have provided some near-optimal results, but their theoretical performance guarantees only valid for the regime wherein all arrival requests could be seen by the cache, which is not the case in some practical scenarios like caching at cellular base station, content dissemination via DNS, etc. Hence our work study the partial-feedback regime wherein only requests for currently cached files are seen by the cache, which is more challenging and has not been studied before in the online learning perspective. We propose an online caching policy combining the FTPL with a novel popularity estimation procedure called Geometric Resampling (GR), and show that it yields the first sublinear regret guarantee in this regime. We also conduct numerical experiments to validate the theoretical guarantees of our caching policy.
{"title":"Learning to Caching Under the Partial-feedback Regime","authors":"Qingsong Liu, Yaoyu Zhang","doi":"10.23919/CNSM55787.2022.9964551","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964551","url":null,"abstract":"We consider the caching problem in an online learning perspective, i.e., no model assumptions and prior knowledge for the file request sequence. Our goal is to design an efficient on-line caching policy with minimal regret, i.e, minimizing the total number of cache-miss with respect to the best static configuration in hindsight. Previous studies such as Follow-The-Perturbed-Leader (FTPL) caching policy, have provided some near-optimal results, but their theoretical performance guarantees only valid for the regime wherein all arrival requests could be seen by the cache, which is not the case in some practical scenarios like caching at cellular base station, content dissemination via DNS, etc. Hence our work study the partial-feedback regime wherein only requests for currently cached files are seen by the cache, which is more challenging and has not been studied before in the online learning perspective. We propose an online caching policy combining the FTPL with a novel popularity estimation procedure called Geometric Resampling (GR), and show that it yields the first sublinear regret guarantee in this regime. We also conduct numerical experiments to validate the theoretical guarantees of our caching policy.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124586208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-31DOI: 10.23919/CNSM55787.2022.9964543
Yaozhong Liu, Long Pan, Chenglong Li, Lin He, Yirui Luo, Guanglei Song, Jiahai Yang, Zhiliang Wang
We present PerfTrace, an end-to-end tool for efficient, real-time, and multi-metric network performance monitoring. PerfTrace provides a high integration of different existing measurement functions, supporting the measurement of essential metrics such as latency, jitter, packet loss, and available bandwidth. More importantly, innovative schemes and algorithms are proposed to address the weaknesses of existing tools.After conducting comprehensive evaluations, we find that (i) PerfTrace measures one-way and two-way latency, jitter, and packet loss ∼9.4× faster and ∼3.6× more data-efficiently; (ii) PerfTrace measures available bandwidth in our testbed with minimal mean relative error (5.22%), outperforming all the tools compared (ranging from 8.17% to 37.24%). Meanwhile, PerfTrace consumes a more constant percentage of bandwidth resources than other tools when monitoring available bandwidth. PerfTrace’s data overhead is always only about 1/600 of the total bandwidth for a measurement frequency once per minute.
{"title":"PerfTrace: A New Multi-metric Network Performance Monitoring Tool","authors":"Yaozhong Liu, Long Pan, Chenglong Li, Lin He, Yirui Luo, Guanglei Song, Jiahai Yang, Zhiliang Wang","doi":"10.23919/CNSM55787.2022.9964543","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964543","url":null,"abstract":"We present PerfTrace, an end-to-end tool for efficient, real-time, and multi-metric network performance monitoring. PerfTrace provides a high integration of different existing measurement functions, supporting the measurement of essential metrics such as latency, jitter, packet loss, and available bandwidth. More importantly, innovative schemes and algorithms are proposed to address the weaknesses of existing tools.After conducting comprehensive evaluations, we find that (i) PerfTrace measures one-way and two-way latency, jitter, and packet loss ∼9.4× faster and ∼3.6× more data-efficiently; (ii) PerfTrace measures available bandwidth in our testbed with minimal mean relative error (5.22%), outperforming all the tools compared (ranging from 8.17% to 37.24%). Meanwhile, PerfTrace consumes a more constant percentage of bandwidth resources than other tools when monitoring available bandwidth. PerfTrace’s data overhead is always only about 1/600 of the total bandwidth for a measurement frequency once per minute.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122108611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-31DOI: 10.23919/CNSM55787.2022.9964669
Minh Nguyen, Babak Taraghi, A. Bentaleb, Roger Zimmermann, C. Timmerer
In this paper, we introduce a CMCD-Aware per-Device bitrate LADder construction (CADLAD) that leverages the Common Media Client Data (CMCD) standard to address the above issues. CADLAD comprises components at both client and server sides. The client calculates the top bitrate (tb) — a CMCD parameter to indicate the highest bitrate that can be rendered at the client — and sends it to the server together with its device type and screen resolution. The server decides on a suitable bitrate ladder, whose maximum bitrate and resolution are based on CMCD parameters, to the client device with the purpose of providing maximum QoE while minimizing delivered data. CADLAD has two versions to work in Video on Demand (VoD) and live streaming scenarios. Our CADLAD is client agnostic; hence, it can work with any players and ABR algorithms at the client. The experimental results show that CADLAD is able to increase the QoE by 2.6x while saving 71% of delivered data, compared to an existing bitrate ladder of an available video dataset. We implement our idea within CAdViSE — an open-source testbed for reproducibility.
{"title":"CADLAD: Device-aware Bitrate Ladder Construction for HTTP Adaptive Streaming","authors":"Minh Nguyen, Babak Taraghi, A. Bentaleb, Roger Zimmermann, C. Timmerer","doi":"10.23919/CNSM55787.2022.9964669","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964669","url":null,"abstract":"In this paper, we introduce a CMCD-Aware per-Device bitrate LADder construction (CADLAD) that leverages the Common Media Client Data (CMCD) standard to address the above issues. CADLAD comprises components at both client and server sides. The client calculates the top bitrate (tb) — a CMCD parameter to indicate the highest bitrate that can be rendered at the client — and sends it to the server together with its device type and screen resolution. The server decides on a suitable bitrate ladder, whose maximum bitrate and resolution are based on CMCD parameters, to the client device with the purpose of providing maximum QoE while minimizing delivered data. CADLAD has two versions to work in Video on Demand (VoD) and live streaming scenarios. Our CADLAD is client agnostic; hence, it can work with any players and ABR algorithms at the client. The experimental results show that CADLAD is able to increase the QoE by 2.6x while saving 71% of delivered data, compared to an existing bitrate ladder of an available video dataset. We implement our idea within CAdViSE — an open-source testbed for reproducibility.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123993350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-31DOI: 10.23919/CNSM55787.2022.9964900
Giles Winchester, G. Parisis, Robert Harper, L. Berthouze
A crucial step in remedying faults within network infrastructures is to determine their root cause. However, the large-scale, complex and dynamic nature of modern networks makes causal inference-based root cause analysis (RCA) challenging in terms of scalability and knowledge drift over time. In this paper, we propose a framework that utilises the neuroscientific concept of functional connectivity – a graph representation of statistical dependencies between events – as a scalable approach to acquire and maintain prior knowledge for causal inference-based RCA approaches in dynamic networks. We demonstrate on both synthetic and real world data that our proposed approach can provide significant speedups to existing causal inference approaches without significant loss of accuracy. Finally, we discuss the impact of the choice of user-defined parameters on causal inference accuracy and conclude that the framework can safely be deployed in the real world.
{"title":"Accelerating Causal Inference Based RCA Using Prior Knowledge From Functional Connectivity Inference","authors":"Giles Winchester, G. Parisis, Robert Harper, L. Berthouze","doi":"10.23919/CNSM55787.2022.9964900","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964900","url":null,"abstract":"A crucial step in remedying faults within network infrastructures is to determine their root cause. However, the large-scale, complex and dynamic nature of modern networks makes causal inference-based root cause analysis (RCA) challenging in terms of scalability and knowledge drift over time. In this paper, we propose a framework that utilises the neuroscientific concept of functional connectivity – a graph representation of statistical dependencies between events – as a scalable approach to acquire and maintain prior knowledge for causal inference-based RCA approaches in dynamic networks. We demonstrate on both synthetic and real world data that our proposed approach can provide significant speedups to existing causal inference approaches without significant loss of accuracy. Finally, we discuss the impact of the choice of user-defined parameters on causal inference accuracy and conclude that the framework can safely be deployed in the real world.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128363087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-31DOI: 10.23919/CNSM55787.2022.9964510
Antonino Angi, Alessio Sacco, Flavio Esposito, G. Marchetto, A. Clemm
The challenges of managing datacenter traffic increase with the complexity and variety of new Internet and Web applications. Efficient network management systems are often required to thwart delays and minimize failures. In this regard, it appears helpful to identify in advance the different classes of flows that (co)exist in the network, characterizing them into different types according to the different latency/bandwidth requirements. In this paper, we propose Howdah, a traffic identification and profiling mechanism that uses Machine Learning and a congestion-aware forwarding strategy to offer adaptation to different traffic classes with the support of programmable data-planes. With Howdah, sender and gateway elements inject in-band traffic information obtained using supervised learning. When a switch or a router receives a packet, it exploits such host-based traffic classification to adapt to a desirable traffic profile, for example, balancing the load. We compare our solutions against recent traffic engineering solutions and show the efficacy of cooperation between host traffic classification and P4-based switch forwarding policies, reducing packet transmission time in datacenter scenarios.
{"title":"Howdah: Load Profiling via In-Band Flow Classification and P4","authors":"Antonino Angi, Alessio Sacco, Flavio Esposito, G. Marchetto, A. Clemm","doi":"10.23919/CNSM55787.2022.9964510","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964510","url":null,"abstract":"The challenges of managing datacenter traffic increase with the complexity and variety of new Internet and Web applications. Efficient network management systems are often required to thwart delays and minimize failures. In this regard, it appears helpful to identify in advance the different classes of flows that (co)exist in the network, characterizing them into different types according to the different latency/bandwidth requirements. In this paper, we propose Howdah, a traffic identification and profiling mechanism that uses Machine Learning and a congestion-aware forwarding strategy to offer adaptation to different traffic classes with the support of programmable data-planes. With Howdah, sender and gateway elements inject in-band traffic information obtained using supervised learning. When a switch or a router receives a packet, it exploits such host-based traffic classification to adapt to a desirable traffic profile, for example, balancing the load. We compare our solutions against recent traffic engineering solutions and show the efficacy of cooperation between host traffic classification and P4-based switch forwarding policies, reducing packet transmission time in datacenter scenarios.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129116595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-31DOI: 10.23919/CNSM55787.2022.9964866
Katharina Dietz, Michael Seufert, T. Hossfeld
Wide Area Network (WAN) research benefits from the availability of realistic network topologies, e.g., as input to simulations, emulators, or testbeds. With the rise of Machine Learning (ML) and particularly Deep Learning (DL) methods, this demand for topologies, which can be used as training data, is greater than ever. However, public datasets are limited, thus, it is promising to generate synthetic graphs with realistic properties based on real topologies for the augmentation of existing data sets. As the generation of synthetic graphs has been in the focus of researchers of various applications fields since several decades, we have a variety of traditional model-dependent and model-independent graph generators at hand, as well as DL-based approaches, such as Generative Adversarial Networks (GANs). In this work, we adapt and evaluate these existing generators for the WAN use case, i.e., for generating synthetic WANs with realistic geographical distances between nodes. Moreover, we investigate a hierarchical graph synthesis approach, which divides the synthesis into local clusters. Finally, we compare the similarity of synthetic and real WAN topologies and discuss the suitability of the generators for data augmentation in the WAN use case.
{"title":"Comparing Traditional and GAN-based Approaches for the Synthesis of Wide Area Network Topologies","authors":"Katharina Dietz, Michael Seufert, T. Hossfeld","doi":"10.23919/CNSM55787.2022.9964866","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964866","url":null,"abstract":"Wide Area Network (WAN) research benefits from the availability of realistic network topologies, e.g., as input to simulations, emulators, or testbeds. With the rise of Machine Learning (ML) and particularly Deep Learning (DL) methods, this demand for topologies, which can be used as training data, is greater than ever. However, public datasets are limited, thus, it is promising to generate synthetic graphs with realistic properties based on real topologies for the augmentation of existing data sets. As the generation of synthetic graphs has been in the focus of researchers of various applications fields since several decades, we have a variety of traditional model-dependent and model-independent graph generators at hand, as well as DL-based approaches, such as Generative Adversarial Networks (GANs). In this work, we adapt and evaluate these existing generators for the WAN use case, i.e., for generating synthetic WANs with realistic geographical distances between nodes. Moreover, we investigate a hierarchical graph synthesis approach, which divides the synthesis into local clusters. Finally, we compare the similarity of synthetic and real WAN topologies and discuss the suitability of the generators for data augmentation in the WAN use case.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129282662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-31DOI: 10.23919/CNSM55787.2022.9964838
K. Hammar, R. Stadler
We present an online framework for learning and updating security policies in dynamic IT environments. It includes three components: a digital twin of the target system, which continuously collects data and evaluates learned policies; a system identification process, which periodically estimates system models based on the collected data; and a policy learning process that is based on reinforcement learning. To evaluate our framework, we apply it to an intrusion prevention use case that involves a dynamic IT infrastructure. Our results demonstrate that the framework automatically adapts security policies to changes in the IT infrastructure and that it outperforms a state-of-the-art method.
{"title":"An Online Framework for Adapting Security Policies in Dynamic IT Environments","authors":"K. Hammar, R. Stadler","doi":"10.23919/CNSM55787.2022.9964838","DOIUrl":"https://doi.org/10.23919/CNSM55787.2022.9964838","url":null,"abstract":"We present an online framework for learning and updating security policies in dynamic IT environments. It includes three components: a digital twin of the target system, which continuously collects data and evaluates learned policies; a system identification process, which periodically estimates system models based on the collected data; and a policy learning process that is based on reinforcement learning. To evaluate our framework, we apply it to an intrusion prevention use case that involves a dynamic IT infrastructure. Our results demonstrate that the framework automatically adapts security policies to changes in the IT infrastructure and that it outperforms a state-of-the-art method.","PeriodicalId":232521,"journal":{"name":"2022 18th International Conference on Network and Service Management (CNSM)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126752238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：