Pub Date : 2022-08-01DOI: 10.1016/j.comcom.2022.08.011
Erik Ortiz Guerra, V. A. Reguera, C. Duran-Faundez, T. Nguyen
{"title":"Channel hopping for blind rendezvous in cognitive radio networks: A review","authors":"Erik Ortiz Guerra, V. A. Reguera, C. Duran-Faundez, T. Nguyen","doi":"10.1016/j.comcom.2022.08.011","DOIUrl":"https://doi.org/10.1016/j.comcom.2022.08.011","url":null,"abstract":"","PeriodicalId":10679,"journal":{"name":"Comput. Phys. Commun.","volume":"3 1","pages":"82-98"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75959069","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}
N. Auricchio, A. Cappuccio, Francesco Caturano, G. Perrone, S. Romano
{"title":"An automated approach to Web Offensive Security","authors":"N. Auricchio, A. Cappuccio, Francesco Caturano, G. Perrone, S. Romano","doi":"10.2139/ssrn.4057341","DOIUrl":"https://doi.org/10.2139/ssrn.4057341","url":null,"abstract":"","PeriodicalId":10679,"journal":{"name":"Comput. Phys. Commun.","volume":"21 1","pages":"248-261"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86128775","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 : 2022-07-31DOI: 10.48550/arXiv.2208.00383
Chenwei Zhao, Miao Ye, Xingsi Xue, Jianhui Lv, Qiuxiang Jiang, Yong Wang
Traditional multicast routing methods have some problems in constructing a multicast tree, such as limited access to network state information, poor adaptability to dynamic and complex changes in the network, and inflexible data forwarding. To address these defects, the optimal multicast routing problem in software-defined networking (SDN) is tailored as a multi-objective optimization problem, and an intelligent multicast routing algorithm DRL-M4MR based on the deep Q network (DQN) deep reinforcement learning (DRL) method is designed to construct a multicast tree in SDN. First, the multicast tree state matrix, link bandwidth matrix, link delay matrix, and link packet loss rate matrix are designed as the state space of the DRL agent by combining the global view and control of the SDN. Second, the action space of the agent is all the links in the network, and the action selection strategy is designed to add the links to the current multicast tree under four cases. Third, single-step and final reward function forms are designed to guide the intelligence to make decisions to construct the optimal multicast tree. The experimental results show that, compared with existing algorithms, the multicast tree construct by DRL-M4MR can obtain better bandwidth, delay, and packet loss rate performance after training, and it can make more intelligent multicast routing decisions in a dynamic network environment.
{"title":"DRL-M4MR: An Intelligent Multicast Routing Approach Based on DQN Deep Reinforcement Learning in SDN","authors":"Chenwei Zhao, Miao Ye, Xingsi Xue, Jianhui Lv, Qiuxiang Jiang, Yong Wang","doi":"10.48550/arXiv.2208.00383","DOIUrl":"https://doi.org/10.48550/arXiv.2208.00383","url":null,"abstract":"Traditional multicast routing methods have some problems in constructing a multicast tree, such as limited access to network state information, poor adaptability to dynamic and complex changes in the network, and inflexible data forwarding. To address these defects, the optimal multicast routing problem in software-defined networking (SDN) is tailored as a multi-objective optimization problem, and an intelligent multicast routing algorithm DRL-M4MR based on the deep Q network (DQN) deep reinforcement learning (DRL) method is designed to construct a multicast tree in SDN. First, the multicast tree state matrix, link bandwidth matrix, link delay matrix, and link packet loss rate matrix are designed as the state space of the DRL agent by combining the global view and control of the SDN. Second, the action space of the agent is all the links in the network, and the action selection strategy is designed to add the links to the current multicast tree under four cases. Third, single-step and final reward function forms are designed to guide the intelligence to make decisions to construct the optimal multicast tree. The experimental results show that, compared with existing algorithms, the multicast tree construct by DRL-M4MR can obtain better bandwidth, delay, and packet loss rate performance after training, and it can make more intelligent multicast routing decisions in a dynamic network environment.","PeriodicalId":10679,"journal":{"name":"Comput. Phys. Commun.","volume":"70 1","pages":"101919"},"PeriodicalIF":0.0,"publicationDate":"2022-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83814982","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 : 2022-06-07DOI: 10.48550/arXiv.2206.03084
Andrea De Salve, P. Mori, L. Ricci, R. D. Pietro
In recent years, Decentralized Online Social Networks (DOSNs) have been attracting the attention of many users because they reduce the risk of censorship, surveillance, and information leakage from the service provider. In contrast to the most popular Online Social Networks, which are based on centralized architectures (e.g., Facebook, Twitter, or Instagram), DOSNs are not based on a single service provider acting as a central authority. Indeed, the contents that are published on DOSNs are stored on the devices made available by their users, which cooperate to execute the tasks needed to provide the service. To continuously guarantee their availability, the contents published by a user could be stored on the devices of other users, simply because they are online when required. Consequently, such contents must be properly protected by the DOSN infrastructure, in order to ensure that they can be really accessed only by users who have the permission of the publishers. As a consequence, DOSNs require efficient solutions for protecting the privacy of the contents published by each user with respect to the other users of the social network. In this paper, we investigate and compare the principal content privacy enforcement models adopted by current DOSNs evaluating their suitability to support different types of privacy policies based on user groups. Such evaluation is carried out by implementing several models and comparing their performance for the typical operations performed on groups, i.e., content publish, user join and leave. Further, we also highlight the limitations of current approaches and show future research directions. This contribution, other than being interesting on its own, provides a blueprint for researchers and practitioners interested in implementing DOSNs, and also highlights a few open research directions.
近年来，分散式在线社交网络(Decentralized Online Social Networks，简称DOSNs)因其降低了服务提供商审查、监视和信息泄露的风险而受到许多用户的关注。与基于集中式架构的最流行的在线社交网络(例如，Facebook, Twitter或Instagram)相反，dosn不是基于充当中央权威的单个服务提供商。实际上，在dosn上发布的内容存储在用户可用的设备上，这些设备合作执行提供服务所需的任务。为了持续保证它们的可用性，用户发布的内容可以存储在其他用户的设备上，因为它们在需要时是在线的。因此，这些内容必须由DOSN基础结构适当地保护，以确保只有获得发布者许可的用户才能真正访问它们。因此，dosn需要有效的解决方案来保护每个用户发布的内容相对于社交网络的其他用户的隐私。在本文中，我们调查和比较了当前的dos采用的主要内容隐私强制模型，评估了它们在支持不同类型的基于用户组的隐私策略方面的适用性。这种评估是通过实现几个模型并比较它们对组执行的典型操作(即内容发布、用户加入和离开)的性能来进行的。此外，我们还强调了现有方法的局限性，并指出了未来的研究方向。这篇文章除了本身很有趣之外，还为对实现dosn感兴趣的研究人员和实践者提供了一个蓝图，并强调了一些开放的研究方向。
{"title":"Content Privacy Enforcement Models in Decentralized Online Social Networks: State of Play, Solutions, Limitations, and Future Directions","authors":"Andrea De Salve, P. Mori, L. Ricci, R. D. Pietro","doi":"10.48550/arXiv.2206.03084","DOIUrl":"https://doi.org/10.48550/arXiv.2206.03084","url":null,"abstract":"In recent years, Decentralized Online Social Networks (DOSNs) have been attracting the attention of many users because they reduce the risk of censorship, surveillance, and information leakage from the service provider. In contrast to the most popular Online Social Networks, which are based on centralized architectures (e.g., Facebook, Twitter, or Instagram), DOSNs are not based on a single service provider acting as a central authority. Indeed, the contents that are published on DOSNs are stored on the devices made available by their users, which cooperate to execute the tasks needed to provide the service. To continuously guarantee their availability, the contents published by a user could be stored on the devices of other users, simply because they are online when required. Consequently, such contents must be properly protected by the DOSN infrastructure, in order to ensure that they can be really accessed only by users who have the permission of the publishers. As a consequence, DOSNs require efficient solutions for protecting the privacy of the contents published by each user with respect to the other users of the social network. In this paper, we investigate and compare the principal content privacy enforcement models adopted by current DOSNs evaluating their suitability to support different types of privacy policies based on user groups. Such evaluation is carried out by implementing several models and comparing their performance for the typical operations performed on groups, i.e., content publish, user join and leave. Further, we also highlight the limitations of current approaches and show future research directions. This contribution, other than being interesting on its own, provides a blueprint for researchers and practitioners interested in implementing DOSNs, and also highlights a few open research directions.","PeriodicalId":10679,"journal":{"name":"Comput. Phys. Commun.","volume":"146 1","pages":"199-225"},"PeriodicalIF":0.0,"publicationDate":"2022-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86024581","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}
Dongmei Zhou, Baowan Yan, Cuiran Li, A. Wang, Haixia Wei
{"title":"Relay selection scheme based on deep reinforcement learning in wireless sensor networks","authors":"Dongmei Zhou, Baowan Yan, Cuiran Li, A. Wang, Haixia Wei","doi":"10.2139/ssrn.4040127","DOIUrl":"https://doi.org/10.2139/ssrn.4040127","url":null,"abstract":"","PeriodicalId":10679,"journal":{"name":"Comput. Phys. Commun.","volume":"9 1","pages":"101799"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86409853","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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