Ori Glikstein, Sapir Hanina, N. Balal, G. Pinhasi, Y. Pinhasi
{"title":"Indoor-Outdoor of Wireless Link Propagation from tunnels and long corridors","authors":"Ori Glikstein, Sapir Hanina, N. Balal, G. Pinhasi, Y. Pinhasi","doi":"10.1109/cits55221.2022.9832979","DOIUrl":"https://doi.org/10.1109/cits55221.2022.9832979","url":null,"abstract":"Radio waves propagating in tunnels and long corridors have received attention in recent years. The indoor propagation inside a tunnel as well as the radiated pattern out of it are studied analytically and experimentally. The wave propagation along a tunnel is analyzed using ray tracing approach, which is useful when the tunnel’s dimensions much exceed the wavelength of the transmitted signal. A multi-ray model was used to reveal non-dimensional parameters, allowing realization of downscaled experiments. The resulted power intensity radiated out of the tunnel exit is calculated using farfield diffraction analysis. Scaled experiments were set to demonstrate the effects of the indoor transmission on the outdoor diffraction pattern. It is shown that the experimental measurements fit the numerical model results.","PeriodicalId":136239,"journal":{"name":"2022 International Conference on Computer, Information and Telecommunication Systems (CITS)","volume":"179 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121318488","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}
Abhijit Guha, M. Obaidat, Debabrata Samanta, SK Hafizul Islam
{"title":"Clustering-based Optimal Resource Allocation Strategy in Title Insurance Underwriting","authors":"Abhijit Guha, M. Obaidat, Debabrata Samanta, SK Hafizul Islam","doi":"10.1109/cits55221.2022.9832993","DOIUrl":"https://doi.org/10.1109/cits55221.2022.9832993","url":null,"abstract":"Production of insurance policies in all types of Insurance requires a thorough examination of the entity against which the Insurance is to be issued. In health insurance, it is the past medical data of the individuals. Vehicle insurance needs the examination of the vehicle and the owner’s data. Likewise, in Title Insurance, it is the historical data of the property which needs scrutiny before the policy issuance. Underwriters perform the job of examining the property records. The scrutiny of the property records requires a high degree of the domain and legal expertise, and title insurance underwriters are often associated with legal professions. They do the final round of validation of the examination process. There are examination teams that take care of the initial set of regular examination tasks associated with each title insurance order. Some human experts assign the orders to the team associates. Not all the orders are of the same complexity in terms of examination. The allocation of the tasks happens based on the gut feeling of the supervisor, considering their experience with the team members. Our research creates clusters of the orders based on specific parameters associated with the orders. It builds a cost model of the past associates working on orders belonging to different clusters. Based on this cost matrix, we have built an optimal task allocation model that assigns the orders to the associates with the promise of optimal cost using a Linear programming solution used frequently in operations research.","PeriodicalId":136239,"journal":{"name":"2022 International Conference on Computer, Information and Telecommunication Systems (CITS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125911651","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}
{"title":"Tracking container network connections in a Digital Data Marketplace with P4","authors":"Sara Shakeri, L. Veen, P. Grosso","doi":"10.1109/cits55221.2022.9832915","DOIUrl":"https://doi.org/10.1109/cits55221.2022.9832915","url":null,"abstract":"There are multiple organizations interested in sharing their data, and they can only do this if a secure platform for data sharing is available which can execute sharing requests under specific agreements and policies. Digital Data Marketplaces (DDMs) aim to provide such an infrastructure. For building a DDM infrastructure, we use containers to provide the required isolation between different sharing requests. However, one important challenge in a containerized DDM infrastructure is providing the ability to monitor the behavior of containers that are involved in the sharing transactions. In addition, the monitoring information in the network layer should be reported in a way that can be interpreted by the upper layers of DDM for further analysis. In this paper, we design a containerized DDM using P4. In our design, the flow traffic between containers is associated with the shared data in a DDM and can be understood by the upper layers. We present different scenarios to demonstrate how our setup can assist in tracking the behavior of containers and providing better performance and security.","PeriodicalId":136239,"journal":{"name":"2022 International Conference on Computer, Information and Telecommunication Systems (CITS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114833494","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}
{"title":"Energy Harvesting WSNs with Adaptive Modulation: Inter-delivery-aware Scheduling Algorithms","authors":"Chaima Zouine, Amina Hentati, J. Frigon","doi":"10.1109/cits55221.2022.9832980","DOIUrl":"https://doi.org/10.1109/cits55221.2022.9832980","url":null,"abstract":"In this paper, we deal with the regularity of status updates in a monitoring system. Specifically, we consider a system consisting of independent energy harvesting nodes with adaptive modulation capabilities that transmit status updates to a non energy harvesting sink over a fading channel. Due to the randomness of the energy arrival and the channel time variations, a node may have difficulties maintaining regular status updates. Hence, the objective of this work is to design scheduling algorithms that minimize the number of violations of inter-delivery time over a finite time horizon. An inter-delivery violation event occurs when the time duration between two consecutive status updates exceeds a given time limit. We focus on online modulation and power adaptation policies where the transmitting sensor node adjusts the M-ary modulation level and transmission power based on both the channel state and the battery level. Specifically, we propose both deterministic and randomized algorithms to efficiently solve the considered scheduling problem for the onenode system. Deterministic solutions were extended to the multi-node system. The numerical results show that the proposed algorithms realize significant gain in terms of violations events compared to the benchmark fixed modulation solutions.","PeriodicalId":136239,"journal":{"name":"2022 International Conference on Computer, Information and Telecommunication Systems (CITS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133777283","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}
{"title":"Ciphertext-Policy Attribute-based Encryption for Securing IoT Devices in Fog Computing","authors":"Shanshan Tu, Feng-Chung Huang, Shenmin Zhang, Akhtar Badshah, Hisham Alasmary, Muhammad Waqas","doi":"10.1109/cits55221.2022.9832996","DOIUrl":"https://doi.org/10.1109/cits55221.2022.9832996","url":null,"abstract":"Securing Internet of Things (IoT) devices in fog computing systems can be challenging due to the inherent limitations of IoT devices. For instance, cryptographic primitives, such as attribute-based encryption (ABE) schemes, are computationally expensive for deployment on IoT devices. Thus, ABE is not realistic in facilitating real-time updates in various applications of IoT. Therefore, attribute-based and multi-authority encryption schemes are designed that help attribute revocation and computation outsourcing from IoT devices to fog computing servers. The attribute revocation scheme is based on the ciphertext-policy attribute-based encryption (CP-ABE) technique. The CP-ABE scheme allows secret keys between IoT devices and fog nodes to be dynamically generated by incorporating the attribute group keys. Then, the encryption and decryption functions for IoT devices are outsourced to fog nodes, which present the CP-ABE validation.","PeriodicalId":136239,"journal":{"name":"2022 International Conference on Computer, Information and Telecommunication Systems (CITS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114967360","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}
{"title":"A CNN based localization and activity recognition algorithm using multi-receiver CSI measurements and decision fusion","authors":"Wei Sun, Jun Yan","doi":"10.1109/cits55221.2022.9832983","DOIUrl":"https://doi.org/10.1109/cits55221.2022.9832983","url":null,"abstract":"With the development of the internet of things, localization and activity recognition based on WIFI signals have received much attentions. In order to improve the performance, in this paper, a convolutional neural network (CNN) based localization and activity recognition algorithm using channel state information (CSI) measurements and decision fusion is proposed. In the off-line phase, the original CSI measurements obtained from multiple receivers are preprocessed to remove the outliers and noise by Hampel filter and Gaussian filter. Then the normalized CSI measurements are rendered into RGB images. At last, at each receiver, the CNN and distributed training is used for classification learning of localization and activity recognition, respectively. In the on-line phase, the decision-level fusion is used to fuse the intermediate estimation and obtain the final localization and activity recognition results. Experiment results show the better performance of the proposed algorithm.","PeriodicalId":136239,"journal":{"name":"2022 International Conference on Computer, Information and Telecommunication Systems (CITS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115021346","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}
Said Bakhshad, V. Ponnusamy, R. Annur, Muhammad Waqas, Hisham Alasmary, Shanshan Tu
{"title":"Deep Reinforcement Learning based Intrusion Detection System with Feature Selections Method and Optimal Hyper-parameter in IoT Environment","authors":"Said Bakhshad, V. Ponnusamy, R. Annur, Muhammad Waqas, Hisham Alasmary, Shanshan Tu","doi":"10.1109/cits55221.2022.9832976","DOIUrl":"https://doi.org/10.1109/cits55221.2022.9832976","url":null,"abstract":"The continuous rise of inter-contented Internet of Things (IoT) devices has significantly increased network traffic, complexity, and the ever-changing Internet environment, making them more vulnerable to security attacks. Therefore, a robust and elegant intrusion detection system (IDS) based on advanced machine learning methods is required for securing the IoT environment. This paper discusses the new deep reinforcement learning (DRL) based network intrusion detection system (NIDS) with feature selection methods. However, the structure and training of the DRL model are still challenging tasks. Moreover, the effectiveness and accuracy of DRL-IDS crucially depend on the suitable hyper-parameters adaptation, i.e., differing hyperparameters can result in markedly varied IDS performance. Furthermore, due to the commercial value of hyper-parameters, confidentiality may be deemed necessary, and proprietary algorithms may protect their exclusive use. Therefore, we find different optimal hyper-parameters values for the training of DRL agents. Furthermore, we evaluate the effectiveness of different hyper-parameters both theoretically and empirically. For instance, we assess the hyper-parameters for the case of varying routing systems and countermeasures and integrate the optimal hyper-parameters for various network performances.","PeriodicalId":136239,"journal":{"name":"2022 International Conference on Computer, Information and Telecommunication Systems (CITS)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122596463","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}
{"title":"CNN-Based Automatic Modulation Classification in OFDM Systems","authors":"Geonho Song, Mingyu Jang, D. Yoon","doi":"10.1109/cits55221.2022.9832989","DOIUrl":"https://doi.org/10.1109/cits55221.2022.9832989","url":null,"abstract":"Convolutional neural network (CNN)-based modulation classification schemes for orthogonal frequency division multiplexing (OFDM) signals have recently been reported. In this paper, we examine the effect of hyperparameters in a CNN model on classification performance and present improved performance of automatic modulation classification for OFDM signals. To do this, we first set a baseline CNN model for OFDM signal modulation classification and then conduct experiments by varying the hyperparameters, such as the size and number of convolution kernels, and the number of fully connected neurons, through computer simulations. We show that the kernel size has a dominant effect on the classification accuracy and should be large enough within an appropriate range to achieve high classification accuracy for a given in-phase and quadrature data set. Finally, we show that the tuned model outperforms the conventional work in terms of classification accuracy.","PeriodicalId":136239,"journal":{"name":"2022 International Conference on Computer, Information and Telecommunication Systems (CITS)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123165246","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}
{"title":"A Deep Learning Based Bluetooth Indoor Localization Algorithm by RSSI and AOA Feature Fusion","authors":"Dekang Zhu, Jun Yan","doi":"10.1109/cits55221.2022.9832985","DOIUrl":"https://doi.org/10.1109/cits55221.2022.9832985","url":null,"abstract":"With the development of the Internet of Things, location based services has received much attentions. Bluetooth 5.1 standard provides the Angle of Arrival (AOA) direction finding function, which opens a new approach for indoor Bluetooth localization. In this paper, a deep learning based Bluetooth indoor localization algorithm by received signal strength indicator (RSSI) and AOA feature fusion is proposed. For data preprocessing, the principal component analysis (PCA) is used to reduce the redundancy of RSSI measurement. The Kalman filter is used to smooth AOA measurement. Then, a convolutional neural network (CNN) is used for feature extraction which extracts the deep-level features of RSSI and AOA measurement respectively. After feature fusion for the above two features by concatenating operation, the Softmax layer is used for classification learning. At last, the localization classification model is obtained. The experimental results show that, compared with the existing localization algorithms, the proposed algorithm has significantly improved the localization performance.","PeriodicalId":136239,"journal":{"name":"2022 International Conference on Computer, Information and Telecommunication Systems (CITS)","volume":"7 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121014514","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}
S. M. Toapanta, Rodrigo Humberto Del Pozo Durango, Luis Enrique Mafla Gallegos, Ma. Roció Maciel Arellano, Jose Antonio Orizaga Trejo, María Mercedes Baño Hifóng
{"title":"Suitable Professional Identity Analysis to Improve Information Security Governance","authors":"S. M. Toapanta, Rodrigo Humberto Del Pozo Durango, Luis Enrique Mafla Gallegos, Ma. Roció Maciel Arellano, Jose Antonio Orizaga Trejo, María Mercedes Baño Hifóng","doi":"10.1109/cits55221.2022.9832999","DOIUrl":"https://doi.org/10.1109/cits55221.2022.9832999","url":null,"abstract":"Information security and cybersecurity governance problems are persistent worldwide in all public and private organizations. The objective of this research is to define appropriate indicators for professional identity to improve the governance of information security as an alternative to solving problems. The deductive method and exploratory research were used to analyze the reference articles. Turned out a table of indicators to define the appropriate professional identity to improve the governance of information security, which is an alternative to solve the problems of governance of information security and cybersecurity. It was concluded that it is necessary for public and private companies to redefine the profile of the professional for the management of ICT and governance of information security, considering their undergraduate, postgraduate academic training in the same area of knowledge of Information Technologies, in accordance with international standards and provisions of national and international control bodies, professional experience, courses related to IT management, projects, courses prior to certification, among others.","PeriodicalId":136239,"journal":{"name":"2022 International Conference on Computer, Information and Telecommunication Systems (CITS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131508997","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}