Rate splitting multiple access (RSMA) is promising to achieve high spectral efficiency with a higher flexibility relative to non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA). In this paper, a novel RSMA-based secure transmission scheme with artificial noise (AN) and an adaptive beamforming is developed with the aim of maximizing the secrecy sum rate (SSR) of the considered system subject to specific constraints. The joint optimization of the power allocation between useful messages and AN signals, rate splitting between the two legitimate receivers, and the two mastering parameters of the beamforming design is tackled. To solve such a non-convex problem, we first analytically reveal some properties of the solution and then focus on an asymptotic scenario with a sufficiently large number of transmit antennas to derive closed-form expressions for the optimal power allocation coefficients. This enables us to develop an efficient method to identify the optimal rate splitting and beamforming parameters. Our examinations demonstrate that the proposed RSMA-based scheme outperforms two benchmark schemes in terms of achieving a higher SSR and the achievable performance gain is exceptional when the number of transmit antennas is small.
{"title":"Resource Allocation for Secure Rate-Splitting Multiple Access with Adaptive Beamforming","authors":"Tenghao Cai, Jia Zhang, Shihao Yan, Lili Meng, Jiande Sun, N. Al-Dhahir","doi":"10.1109/ICCWorkshops50388.2021.9473880","DOIUrl":"https://doi.org/10.1109/ICCWorkshops50388.2021.9473880","url":null,"abstract":"Rate splitting multiple access (RSMA) is promising to achieve high spectral efficiency with a higher flexibility relative to non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA). In this paper, a novel RSMA-based secure transmission scheme with artificial noise (AN) and an adaptive beamforming is developed with the aim of maximizing the secrecy sum rate (SSR) of the considered system subject to specific constraints. The joint optimization of the power allocation between useful messages and AN signals, rate splitting between the two legitimate receivers, and the two mastering parameters of the beamforming design is tackled. To solve such a non-convex problem, we first analytically reveal some properties of the solution and then focus on an asymptotic scenario with a sufficiently large number of transmit antennas to derive closed-form expressions for the optimal power allocation coefficients. This enables us to develop an efficient method to identify the optimal rate splitting and beamforming parameters. Our examinations demonstrate that the proposed RSMA-based scheme outperforms two benchmark schemes in terms of achieving a higher SSR and the achievable performance gain is exceptional when the number of transmit antennas is small.","PeriodicalId":127186,"journal":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115557180","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 : 2021-06-01DOI: 10.1109/ICCWorkshops50388.2021.9473594
Ruikang Zhong, Xiao Liu, Yuanwei Liu, Di Zhang, Yue Chen
A communication enabled indoor intelligent robots (IRs) service framework is proposed, where the non-orthogonal multiple access (NOMA) technique is adopted to enhance the data rate and user fairness. Build on the proposed communication model, motions of IRs and the down-link power allocation policy are jointly optimized to maximize the mission efficiency and communication reliability of IRs. In an effort to find the optimal path for IRs from the initial point to their mission destinations, a novel reinforcement learning approach named deep transfer deterministic policy gradient (DT-DPG) algorithm is proposed. In order to save the training time and hardware costs, the radio map is investigated and provided to the agent as a virtual training environment. Our simulation demonstrates that 1) The participation of the NOMA technique effectively improves the communication reliability of IRs; 2) The radio map is qualified to be a virtual training environment, and its statistical channel state information improves training efficiency by about 30%; 3) The proposed algorithm is superior to the deep deterministic policy gradient (DDPG) algorithm in terms of the optimization performance, training time, and anti-local optimum ability.
{"title":"Path Design for NOMA-Enhanced Robots: A Machine Learning Approach with Radio Map","authors":"Ruikang Zhong, Xiao Liu, Yuanwei Liu, Di Zhang, Yue Chen","doi":"10.1109/ICCWorkshops50388.2021.9473594","DOIUrl":"https://doi.org/10.1109/ICCWorkshops50388.2021.9473594","url":null,"abstract":"A communication enabled indoor intelligent robots (IRs) service framework is proposed, where the non-orthogonal multiple access (NOMA) technique is adopted to enhance the data rate and user fairness. Build on the proposed communication model, motions of IRs and the down-link power allocation policy are jointly optimized to maximize the mission efficiency and communication reliability of IRs. In an effort to find the optimal path for IRs from the initial point to their mission destinations, a novel reinforcement learning approach named deep transfer deterministic policy gradient (DT-DPG) algorithm is proposed. In order to save the training time and hardware costs, the radio map is investigated and provided to the agent as a virtual training environment. Our simulation demonstrates that 1) The participation of the NOMA technique effectively improves the communication reliability of IRs; 2) The radio map is qualified to be a virtual training environment, and its statistical channel state information improves training efficiency by about 30%; 3) The proposed algorithm is superior to the deep deterministic policy gradient (DDPG) algorithm in terms of the optimization performance, training time, and anti-local optimum ability.","PeriodicalId":127186,"journal":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"344 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124313104","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 : 2021-06-01DOI: 10.1109/ICCWorkshops50388.2021.9473734
Jian-ji Ren, Junshuai Sun, Hui Tian, Wanli Ni, Gaofeng Nie, Yingying Wang
Federated learning (FL) and edge computing are both important technologies to support the future Internet of Things (IoT). Despite that the network supported by edge computing has great potential to promote FL, it is more challenging to achieve efficient FL due to more complex resource coupling in it. Focus on this problem, we formulate a problem which minimizes the weighted sum of system cost and learning cost by jointly optimizing bandwidth, computation frequency, transmission power allocation and subcarrier assignment. In order to solve this mixed-integer non-linear problem, we first decouple the bandwidth allocation subproblem from the original problem and obtain a closed-form solution. Further considering the remaining joint optimization problem of computation frequency, transmission power and subcarrier, an iterative algorithm with polynomial time complexity is designed. In an iteration, the latency and computation frequency optimization subproblem and transmission power and subcarrier optimization subproblem are solved using the proposed algorithms in turn. The iterative algorithm is repeated until convergence. Finally, to verify the performance of the algorithm, we compare the proposed algorithm with five baselines. Numerical results show the significant performance gain and the robustness of the proposed algorithm.
{"title":"Joint Resource Allocation for Efficient Federated Learning in Internet of Things Supported by Edge Computing","authors":"Jian-ji Ren, Junshuai Sun, Hui Tian, Wanli Ni, Gaofeng Nie, Yingying Wang","doi":"10.1109/ICCWorkshops50388.2021.9473734","DOIUrl":"https://doi.org/10.1109/ICCWorkshops50388.2021.9473734","url":null,"abstract":"Federated learning (FL) and edge computing are both important technologies to support the future Internet of Things (IoT). Despite that the network supported by edge computing has great potential to promote FL, it is more challenging to achieve efficient FL due to more complex resource coupling in it. Focus on this problem, we formulate a problem which minimizes the weighted sum of system cost and learning cost by jointly optimizing bandwidth, computation frequency, transmission power allocation and subcarrier assignment. In order to solve this mixed-integer non-linear problem, we first decouple the bandwidth allocation subproblem from the original problem and obtain a closed-form solution. Further considering the remaining joint optimization problem of computation frequency, transmission power and subcarrier, an iterative algorithm with polynomial time complexity is designed. In an iteration, the latency and computation frequency optimization subproblem and transmission power and subcarrier optimization subproblem are solved using the proposed algorithms in turn. The iterative algorithm is repeated until convergence. Finally, to verify the performance of the algorithm, we compare the proposed algorithm with five baselines. Numerical results show the significant performance gain and the robustness of the proposed algorithm.","PeriodicalId":127186,"journal":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122103154","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 : 2021-06-01DOI: 10.1109/ICCWorkshops50388.2021.9473740
Imran Mohammed, I. Collings, S. Hanly
This paper presents an accurate approach to predict the probability of line-of-sight for unmanned aerial vehicle (UAV) communications. We present a new numerical approach to calculate the probability of line-of-sight as a function of UAV height and distance to a ground based receiver, using a two- dimensional model of building and UAV locations. We use this numerical approach to calculate the probability of line-of-sight as a function of elevation angle. We also provide closed-form formulas for the probability of LoS as a function of elevation angle. We show that our approaches predict the probability of line-of-sight more accurately than the existing approaches.
{"title":"Line of Sight Probability Prediction for UAV Communication","authors":"Imran Mohammed, I. Collings, S. Hanly","doi":"10.1109/ICCWorkshops50388.2021.9473740","DOIUrl":"https://doi.org/10.1109/ICCWorkshops50388.2021.9473740","url":null,"abstract":"This paper presents an accurate approach to predict the probability of line-of-sight for unmanned aerial vehicle (UAV) communications. We present a new numerical approach to calculate the probability of line-of-sight as a function of UAV height and distance to a ground based receiver, using a two- dimensional model of building and UAV locations. We use this numerical approach to calculate the probability of line-of-sight as a function of elevation angle. We also provide closed-form formulas for the probability of LoS as a function of elevation angle. We show that our approaches predict the probability of line-of-sight more accurately than the existing approaches.","PeriodicalId":127186,"journal":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125347758","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 : 2021-06-01DOI: 10.1109/ICCWorkshops50388.2021.9473716
Ogeen H. Toma, M. López-Benítez
Cooperative spectrum sensing is a widely studied topic in cognitive radio, which is capable of improving the detection accuracy of the primary channel activities. In cooperative spectrum sensing, secondary users' observations are sent to a common receiver, the Fusion Centre (FC), to obtain a better understanding and decision about the state of the primary channel. This work, however, investigates how these observations of the secondary users can efficiently be exploited in such a way that minimises the collision ratio between the secondary and the primary users and at the same time maximises the exploitation of the unused frequency spectrum. As a result, a simple yet efficient approach is proposed for cooperative spectrum sensing, which, to the best of the authors' knowledge, has not been covered in the literature. This approach outperforms the conventional approach of cooperative spectrum sensing for reducing the interference and increasing the utilisation of the unused frequency spectrum in cognitive radio systems.
{"title":"Cooperative Spectrum Sensing: A New Approach for Minimum Interference and Maximum Utilisation","authors":"Ogeen H. Toma, M. López-Benítez","doi":"10.1109/ICCWorkshops50388.2021.9473716","DOIUrl":"https://doi.org/10.1109/ICCWorkshops50388.2021.9473716","url":null,"abstract":"Cooperative spectrum sensing is a widely studied topic in cognitive radio, which is capable of improving the detection accuracy of the primary channel activities. In cooperative spectrum sensing, secondary users' observations are sent to a common receiver, the Fusion Centre (FC), to obtain a better understanding and decision about the state of the primary channel. This work, however, investigates how these observations of the secondary users can efficiently be exploited in such a way that minimises the collision ratio between the secondary and the primary users and at the same time maximises the exploitation of the unused frequency spectrum. As a result, a simple yet efficient approach is proposed for cooperative spectrum sensing, which, to the best of the authors' knowledge, has not been covered in the literature. This approach outperforms the conventional approach of cooperative spectrum sensing for reducing the interference and increasing the utilisation of the unused frequency spectrum in cognitive radio systems.","PeriodicalId":127186,"journal":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122684135","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 : 2021-06-01DOI: 10.1109/ICCWorkshops50388.2021.9473855
M. Sadeghi, Shahram Mollahasani, M. Erol-Kantarci
The future electricity distribution system will be highly impacted by the emergence of peer-to-peer energy trading within microgrid (MG) communities. The idea of peer-to-peer energy trading is to export the surplus energy of a MG to a nearby MG or a group of MGs whose electrical load exceeds their generation. The variations in demand and generation, and the dynamic nature of these communities result in uncertainty on whether MGs will be able to satisfy their trading commitment or not. In this paper, the problem of energy trading among MGs is addressed with the objective of minimizing the cost under uncertainty. A Bayesian coalitional Game (BCG) based scheme is proposed, which helps the MGs to minimize the overall cost by forming stable coalitions. The results show 15% to 30% improvement in terms of cost minimization compared to an existing Q-learning based scheme and a conventional coalitional game theory (CG)-based approach from the literature.
{"title":"Cost-Aware Dynamic Bayesian Coalitional Game for Energy Trading among Microgrids","authors":"M. Sadeghi, Shahram Mollahasani, M. Erol-Kantarci","doi":"10.1109/ICCWorkshops50388.2021.9473855","DOIUrl":"https://doi.org/10.1109/ICCWorkshops50388.2021.9473855","url":null,"abstract":"The future electricity distribution system will be highly impacted by the emergence of peer-to-peer energy trading within microgrid (MG) communities. The idea of peer-to-peer energy trading is to export the surplus energy of a MG to a nearby MG or a group of MGs whose electrical load exceeds their generation. The variations in demand and generation, and the dynamic nature of these communities result in uncertainty on whether MGs will be able to satisfy their trading commitment or not. In this paper, the problem of energy trading among MGs is addressed with the objective of minimizing the cost under uncertainty. A Bayesian coalitional Game (BCG) based scheme is proposed, which helps the MGs to minimize the overall cost by forming stable coalitions. The results show 15% to 30% improvement in terms of cost minimization compared to an existing Q-learning based scheme and a conventional coalitional game theory (CG)-based approach from the literature.","PeriodicalId":127186,"journal":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129546318","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 : 2021-06-01DOI: 10.1109/ICCWorkshops50388.2021.9473591
H. A. Hamadi, Nida Nasir, C. Yeun, E. Damiani
This paper presents a new secure communication scheme for an Intelligent Public Transportation System (IPTS). The proposed scheme generates a cryptographic key and aims to share it among the ATSoI’s participant entities securely. It mitigates several cyber-threats such as; DoS attack, replay attack, Man-In-The-Middle Attack, and eavesdropping activities with more potential occurrences in such cooperative systems. Moreover, it relies on a nonce instead of a timestamp to avoid time synchronization problems. Thus, it provides mutual authentication, confidentiality, message integrity, the privacy of the entities’ IDs, and checking the entities’ availability. A formal verification tool (ProVerif) is used to validate the proposed protocol’s security robustness through two main steps. Firstly, coding of the proposed protocol using ProVerif syntax. Secondly, performing the analysis of the attack trace for the failure queries.
{"title":"A Verified Protocol for Secure Autonomous and Cooperative Public Transportation in Smart Cities","authors":"H. A. Hamadi, Nida Nasir, C. Yeun, E. Damiani","doi":"10.1109/ICCWorkshops50388.2021.9473591","DOIUrl":"https://doi.org/10.1109/ICCWorkshops50388.2021.9473591","url":null,"abstract":"This paper presents a new secure communication scheme for an Intelligent Public Transportation System (IPTS). The proposed scheme generates a cryptographic key and aims to share it among the ATSoI’s participant entities securely. It mitigates several cyber-threats such as; DoS attack, replay attack, Man-In-The-Middle Attack, and eavesdropping activities with more potential occurrences in such cooperative systems. Moreover, it relies on a nonce instead of a timestamp to avoid time synchronization problems. Thus, it provides mutual authentication, confidentiality, message integrity, the privacy of the entities’ IDs, and checking the entities’ availability. A formal verification tool (ProVerif) is used to validate the proposed protocol’s security robustness through two main steps. Firstly, coding of the proposed protocol using ProVerif syntax. Secondly, performing the analysis of the attack trace for the failure queries.","PeriodicalId":127186,"journal":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129695652","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 : 2021-06-01DOI: 10.1109/ICCWorkshops50388.2021.9473857
M. Sharma, P. Tan, E. Kurniawan, Sumei Sun
In this work, we develop a reinforcement learning (RL) based model-free approach to obtain a policy for joint packet scheduling and rate adaptation, such that the packet drop probability (PDP) is minimized. The developed learning scheme yields an online cross-layer scheduling policy which takes into account the randomness in packet arrivals and wireless channels, as well as the state of packet buffers. Inherent difference in the time-scales of packet arrival process and the wireless channel variations leads to sparsity in the observed reward signal. Since an RL agent learns by using the feedback obtained in terms of rewards for its actions, the sample complexity of RL approach increases exponentially due to resulting sparsity. Therefore, a basic RL based approach, e.g., double deep Q-network (DDQN) based RL, results in a policy with negligible performance gain over the state-of-the-art schemes, such as shortest processing time (SPT) based scheduling. In order to alleviate the sparse reward problem, we leverage prioritized experience replay (PER) and develop a DDQN-based learning scheme with PER. We observe through simulations that the policy learned using DDQN-PER approach results in a 3-5% lower PDP, compared to both the basic DDQN based RL and SPT scheme.
{"title":"Packet Drop Probability-Optimal Cross-layer Scheduling: Dealing with Curse of Sparsity using Prioritized Experience Replay","authors":"M. Sharma, P. Tan, E. Kurniawan, Sumei Sun","doi":"10.1109/ICCWorkshops50388.2021.9473857","DOIUrl":"https://doi.org/10.1109/ICCWorkshops50388.2021.9473857","url":null,"abstract":"In this work, we develop a reinforcement learning (RL) based model-free approach to obtain a policy for joint packet scheduling and rate adaptation, such that the packet drop probability (PDP) is minimized. The developed learning scheme yields an online cross-layer scheduling policy which takes into account the randomness in packet arrivals and wireless channels, as well as the state of packet buffers. Inherent difference in the time-scales of packet arrival process and the wireless channel variations leads to sparsity in the observed reward signal. Since an RL agent learns by using the feedback obtained in terms of rewards for its actions, the sample complexity of RL approach increases exponentially due to resulting sparsity. Therefore, a basic RL based approach, e.g., double deep Q-network (DDQN) based RL, results in a policy with negligible performance gain over the state-of-the-art schemes, such as shortest processing time (SPT) based scheduling. In order to alleviate the sparse reward problem, we leverage prioritized experience replay (PER) and develop a DDQN-based learning scheme with PER. We observe through simulations that the policy learned using DDQN-PER approach results in a 3-5% lower PDP, compared to both the basic DDQN based RL and SPT scheme.","PeriodicalId":127186,"journal":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129879109","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 : 2021-06-01DOI: 10.1109/ICCWorkshops50388.2021.9473824
Christopher B. Freas, Dhara Shah, Robert W. Harrison
Distributed denial of service attacks threaten the security and health of the Internet. Remediation relies on up-to-date and accurate attack signatures. Signature-based detection is relatively inexpensive computationally. Yet, signatures are inflexible when small variations exist in the attack vector. Attackers exploit this rigidity by altering their attacks to bypass the signatures. Our previous work revealed a critical problem with conventional machine learning models. Conventional models are unable to generalize on the temporal nature of network flow data to classify attacks. We thus explored the use of deep learning techniques on real flow data. We found that a variety of attacks could be identified with high accuracy compared to previous approaches. We show that a convolutional neural network can be implemented for this problem that is suitable for large volumes of data while maintaining useful levels of accuracy.
{"title":"Accuracy and Generalization of Deep Learning Applied to Large Scale Attacks","authors":"Christopher B. Freas, Dhara Shah, Robert W. Harrison","doi":"10.1109/ICCWorkshops50388.2021.9473824","DOIUrl":"https://doi.org/10.1109/ICCWorkshops50388.2021.9473824","url":null,"abstract":"Distributed denial of service attacks threaten the security and health of the Internet. Remediation relies on up-to-date and accurate attack signatures. Signature-based detection is relatively inexpensive computationally. Yet, signatures are inflexible when small variations exist in the attack vector. Attackers exploit this rigidity by altering their attacks to bypass the signatures. Our previous work revealed a critical problem with conventional machine learning models. Conventional models are unable to generalize on the temporal nature of network flow data to classify attacks. We thus explored the use of deep learning techniques on real flow data. We found that a variety of attacks could be identified with high accuracy compared to previous approaches. We show that a convolutional neural network can be implemented for this problem that is suitable for large volumes of data while maintaining useful levels of accuracy.","PeriodicalId":127186,"journal":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131165618","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}
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