Pub Date : 2021-04-23DOI: 10.1109/ICCCS52626.2021.9449254
Wei Liu, Jinwei Xu, Yu Xia, Ming Xu, Mao Jing, Shunren Hu, Daqing Huang
Low power wireless links are prone to fluctuate when the channel environment changes. In order to reduce the impact of link fluctuations on data transmission, it is necessary to predict the link quality quickly and accurately and make dynamic adjustments according to prediction results. However, existing link quality prediction mechanisms lack sufficient consideration of the impact of link fluctuations, which leads to high prediction errors under the links with large fluctuations such as moderate and sudden changed links. In response to this problem, this paper proposed WNN-LQP, a more effective link quality prediction mechanism under the links with large fluctuations. By taking advantage of the higher resolution of link quality indicator in the transition region as well as the stronger learning ability and higher prediction accuracy of wavelet neural network, WNN-LQP could reduce the prediction errors under moderate and sudden changed links effectively. Compared with the similar mechanism, its prediction errors are reduced by 26.9% under both moderate and sudden changed links.
{"title":"Wavelet Neural Network Based Link Quality Prediction for Fluctuating Low Power Wireless Links","authors":"Wei Liu, Jinwei Xu, Yu Xia, Ming Xu, Mao Jing, Shunren Hu, Daqing Huang","doi":"10.1109/ICCCS52626.2021.9449254","DOIUrl":"https://doi.org/10.1109/ICCCS52626.2021.9449254","url":null,"abstract":"Low power wireless links are prone to fluctuate when the channel environment changes. In order to reduce the impact of link fluctuations on data transmission, it is necessary to predict the link quality quickly and accurately and make dynamic adjustments according to prediction results. However, existing link quality prediction mechanisms lack sufficient consideration of the impact of link fluctuations, which leads to high prediction errors under the links with large fluctuations such as moderate and sudden changed links. In response to this problem, this paper proposed WNN-LQP, a more effective link quality prediction mechanism under the links with large fluctuations. By taking advantage of the higher resolution of link quality indicator in the transition region as well as the stronger learning ability and higher prediction accuracy of wavelet neural network, WNN-LQP could reduce the prediction errors under moderate and sudden changed links effectively. Compared with the similar mechanism, its prediction errors are reduced by 26.9% under both moderate and sudden changed links.","PeriodicalId":376290,"journal":{"name":"2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115307050","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 : 2021-04-23DOI: 10.1109/ICCCS52626.2021.9449143
Hao Wang, Wei Zhang, Yanyan Liu
The joint source-channel coding (JSCC) scheme is applicable to the resource-constrained, real-time, time-varying and low-cost communications. A high-efficient JSCC scheme based on Reed-Solomon (RS) codes is proposed, which can reduce the amount of data transmitted and ensure the reliability of communication system. In addition, a low complexity joint source-channel decoder is proposed, which can implement signal reconstruction and channel decoding through one circuit with high efficiency. The results illustrate that our decoder only needs about 41.1k gates and operates at 500 MHz to achieve the throughput of 3.05 Gb/s, which can be applied to the scenarios of Internet of Things (IoT) and Artificial Intelligence (AI).
{"title":"Joint Coding Scheme Based on Reed-Solomon Codes","authors":"Hao Wang, Wei Zhang, Yanyan Liu","doi":"10.1109/ICCCS52626.2021.9449143","DOIUrl":"https://doi.org/10.1109/ICCCS52626.2021.9449143","url":null,"abstract":"The joint source-channel coding (JSCC) scheme is applicable to the resource-constrained, real-time, time-varying and low-cost communications. A high-efficient JSCC scheme based on Reed-Solomon (RS) codes is proposed, which can reduce the amount of data transmitted and ensure the reliability of communication system. In addition, a low complexity joint source-channel decoder is proposed, which can implement signal reconstruction and channel decoding through one circuit with high efficiency. The results illustrate that our decoder only needs about 41.1k gates and operates at 500 MHz to achieve the throughput of 3.05 Gb/s, which can be applied to the scenarios of Internet of Things (IoT) and Artificial Intelligence (AI).","PeriodicalId":376290,"journal":{"name":"2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124946824","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 : 2021-04-23DOI: 10.1109/ICCCS52626.2021.9449191
Wenzhong Zhou, Huiqian Du, Wenbo Mei, Liping Fang
The deep learning-based CS-MRI methods have been demonstrated to be able to reconstruct high-precision MR images. However, it can be observed that most current deep learning-based CS-MRI methods capture long-range dependencies by stacking multiple convolutional layers, which is computationally inefficient. The latent graph neural network has been proposed to efficiently capture long-range dependencies, which can address the above issue. Besides, there are very few works introducing graph neural networks (GNNs) into MRI reconstruction. In this paper, we propose a novel graph reasoning generative adversarial network, termed as GRGAN, by introducing the graph reasoning networks into MRI reconstruction, where the graph reasoning networks are embedded in the generator to capture long-range dependencies more efficiently. In addition, we propose the multi-scale aggregated residual blocks, termed as MARBs, and introduce them into the proposed GRGAN to extract multi-scale feature information effectively. The experimental results demonstrate that the proposed GRGAN surpasses the state-of-the-art deep learning-based CS-MRI methods with fewer model parameters.
{"title":"MRI Reconstruction Using Graph Reasoning Generative Adversarial Network","authors":"Wenzhong Zhou, Huiqian Du, Wenbo Mei, Liping Fang","doi":"10.1109/ICCCS52626.2021.9449191","DOIUrl":"https://doi.org/10.1109/ICCCS52626.2021.9449191","url":null,"abstract":"The deep learning-based CS-MRI methods have been demonstrated to be able to reconstruct high-precision MR images. However, it can be observed that most current deep learning-based CS-MRI methods capture long-range dependencies by stacking multiple convolutional layers, which is computationally inefficient. The latent graph neural network has been proposed to efficiently capture long-range dependencies, which can address the above issue. Besides, there are very few works introducing graph neural networks (GNNs) into MRI reconstruction. In this paper, we propose a novel graph reasoning generative adversarial network, termed as GRGAN, by introducing the graph reasoning networks into MRI reconstruction, where the graph reasoning networks are embedded in the generator to capture long-range dependencies more efficiently. In addition, we propose the multi-scale aggregated residual blocks, termed as MARBs, and introduce them into the proposed GRGAN to extract multi-scale feature information effectively. The experimental results demonstrate that the proposed GRGAN surpasses the state-of-the-art deep learning-based CS-MRI methods with fewer model parameters.","PeriodicalId":376290,"journal":{"name":"2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126105475","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 : 2021-04-23DOI: 10.1109/ICCCS52626.2021.9449308
Zhonghui Pei, Wei Chen, Luyao Du, Hongjiang Zheng
The Medium Access Control (MAC) layer contention protocol is closely related to the performance of network throughput, end-to-end delay, and access fairness on the Internet of Vehicles (IoV) communication. Based on the MAC layer protocol of the Wireless Access in Vehicular Environments (WAVE) standard system, this paper proposes a MAC layer contention window adaptive adjustment policy using Reinforcement Learning. Through the detection of the number of neighbors and the application of the Q-Learning algorithm, the vehicle can adjust the contention window according to the number of nodes competing for the same channel to adapt to the changing environments of the IoV. Three different MAC protocols are simulated and analyzed under the Vehicle in Network Simulation (Veins) platform. The results show that the proposed MAC protocol based on neighbor detection and Q-Learning performs better than WAVE MAC protocol and general MAC protocol based on Q-Learning.
{"title":"MAC Contention Protocol Based on Reinforcement Learning for IoV Communication Environments","authors":"Zhonghui Pei, Wei Chen, Luyao Du, Hongjiang Zheng","doi":"10.1109/ICCCS52626.2021.9449308","DOIUrl":"https://doi.org/10.1109/ICCCS52626.2021.9449308","url":null,"abstract":"The Medium Access Control (MAC) layer contention protocol is closely related to the performance of network throughput, end-to-end delay, and access fairness on the Internet of Vehicles (IoV) communication. Based on the MAC layer protocol of the Wireless Access in Vehicular Environments (WAVE) standard system, this paper proposes a MAC layer contention window adaptive adjustment policy using Reinforcement Learning. Through the detection of the number of neighbors and the application of the Q-Learning algorithm, the vehicle can adjust the contention window according to the number of nodes competing for the same channel to adapt to the changing environments of the IoV. Three different MAC protocols are simulated and analyzed under the Vehicle in Network Simulation (Veins) platform. The results show that the proposed MAC protocol based on neighbor detection and Q-Learning performs better than WAVE MAC protocol and general MAC protocol based on Q-Learning.","PeriodicalId":376290,"journal":{"name":"2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126168130","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 : 2021-04-23DOI: 10.1109/ICCCS52626.2021.9449100
W. Tang, Singha Chaveesuk
In the information and communication technology era, the scales and types of big data are expanding rapidly, playing an increasingly important role in business. Big data technology is undoubtedly a big wave between information technology and manufacturing enterprise. However, some small and medium-sized manufacturing enterprises may not adopt and use big data technology as ICT related companies do. In this paper, we use the TOE model to test the factors (technological context, organizational context and environmental context) affecting BDA system adoption in SMEs of manufacturing.
{"title":"Big Data Analytics System Adoption in SMEs of Manufacturing","authors":"W. Tang, Singha Chaveesuk","doi":"10.1109/ICCCS52626.2021.9449100","DOIUrl":"https://doi.org/10.1109/ICCCS52626.2021.9449100","url":null,"abstract":"In the information and communication technology era, the scales and types of big data are expanding rapidly, playing an increasingly important role in business. Big data technology is undoubtedly a big wave between information technology and manufacturing enterprise. However, some small and medium-sized manufacturing enterprises may not adopt and use big data technology as ICT related companies do. In this paper, we use the TOE model to test the factors (technological context, organizational context and environmental context) affecting BDA system adoption in SMEs of manufacturing.","PeriodicalId":376290,"journal":{"name":"2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125505138","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}
Aiming at the difficulties that the receiver's field of view and random movement seriously affect the robustness of the visible light communication channel, this paper proposes to use the optimal-tilt-angle receiver as one of the solutions. Firstly, we take the channel capacity as the optimization goal and the Kuhn-Tucker conditions as the optimization tool to deduce the mathematical expression of the optimal tilt angle of the receiver in the single LED scene. Secondly, we propose to use the channel capacity difference between the optimal-tilt-angle receiver and the vertical-up receiver to evaluate the improvement of the channel robustness of the optimal-tilt-angle receiver. Finally, the cumulative distribution function and probability density function of the channel capacity difference are deduced when the receiver moves randomly. The simulation results show that the optimal-tilt-angle receiver improves the channel robustness more obviously in the scenes where the LED has a wide half-power semi-angle, the receiver has a narrow field of view, and the receiver has a wide range of movement.
{"title":"Performance Analysis of Mobile Receivers with Optimal Tilt Angle in Visible Light Communication System","authors":"Lingchuan Kang, Chao Wang, Yu Mu, Xiao-ping Du, Jingshu Xue, Yi-jun Zhu","doi":"10.1109/ICCCS52626.2021.9449314","DOIUrl":"https://doi.org/10.1109/ICCCS52626.2021.9449314","url":null,"abstract":"Aiming at the difficulties that the receiver's field of view and random movement seriously affect the robustness of the visible light communication channel, this paper proposes to use the optimal-tilt-angle receiver as one of the solutions. Firstly, we take the channel capacity as the optimization goal and the Kuhn-Tucker conditions as the optimization tool to deduce the mathematical expression of the optimal tilt angle of the receiver in the single LED scene. Secondly, we propose to use the channel capacity difference between the optimal-tilt-angle receiver and the vertical-up receiver to evaluate the improvement of the channel robustness of the optimal-tilt-angle receiver. Finally, the cumulative distribution function and probability density function of the channel capacity difference are deduced when the receiver moves randomly. The simulation results show that the optimal-tilt-angle receiver improves the channel robustness more obviously in the scenes where the LED has a wide half-power semi-angle, the receiver has a narrow field of view, and the receiver has a wide range of movement.","PeriodicalId":376290,"journal":{"name":"2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)","volume":"515 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116210233","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 : 2021-04-23DOI: 10.1109/ICCCS52626.2021.9449310
Jean Paul Twarayisenze, Zhiquan Bai, Abeer Mohamed, K. Pang, Jingjing Wang, Xinghai Yang, K. Kwak
Adaptive spatial modulation (ASM) is a closed loop feedback transmission technique for multiple-input multiple-output (MIMO) systems, where different modulation orders can be assigned to the transmit antennas based on the available channel conditions. However, the conventional optimal modulation order selection (MOS) schemes in ASM have high computational complexity. In this paper, a supervised learning aided feed-forward artificial neural network (ANN) is proposed to design the MOS in ASM and achieve an effective tradeoff between the system computational complexity and the bit error rate (BER) performance. Specifically, the proposed ANN is utilized to transform the MOS problem in ASM to a multiclass classification problem based on a low search classification method and predict the optimal MOS candidate which maximizes the minimum Euclidean distance. Simulation results reveal that, for a given spectral efficiency (SE), the proposed ANN based ASM scheme outperforms the classical SM scheme and retains the advantages of the conventional ASM scheme but with lower system computational complexity.
{"title":"Artificial Neural Network Based Adaptive Spatial Modulation","authors":"Jean Paul Twarayisenze, Zhiquan Bai, Abeer Mohamed, K. Pang, Jingjing Wang, Xinghai Yang, K. Kwak","doi":"10.1109/ICCCS52626.2021.9449310","DOIUrl":"https://doi.org/10.1109/ICCCS52626.2021.9449310","url":null,"abstract":"Adaptive spatial modulation (ASM) is a closed loop feedback transmission technique for multiple-input multiple-output (MIMO) systems, where different modulation orders can be assigned to the transmit antennas based on the available channel conditions. However, the conventional optimal modulation order selection (MOS) schemes in ASM have high computational complexity. In this paper, a supervised learning aided feed-forward artificial neural network (ANN) is proposed to design the MOS in ASM and achieve an effective tradeoff between the system computational complexity and the bit error rate (BER) performance. Specifically, the proposed ANN is utilized to transform the MOS problem in ASM to a multiclass classification problem based on a low search classification method and predict the optimal MOS candidate which maximizes the minimum Euclidean distance. Simulation results reveal that, for a given spectral efficiency (SE), the proposed ANN based ASM scheme outperforms the classical SM scheme and retains the advantages of the conventional ASM scheme but with lower system computational complexity.","PeriodicalId":376290,"journal":{"name":"2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122008360","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 : 2021-04-23DOI: 10.1109/ICCCS52626.2021.9449199
Pan Gao, Meng Yue, Zhi-jun Wu
In recent years, various machine learning algorithms and intelligent optimization algorithms have emerged one after another, and are widely used in intrusion detection. As a highly sparse model, the relevance vector machine (RVM) is very suitable for intrusion detection scenarios with large scale data. The selection of the parameters of the intrusion detection model directly affects the performance of intrusion detection. Therefore, the selection and determination of parameters is a very critical point to obtain better detection performance. At the same time, the classification performance of RVM obviously depends on the kernel function. To ensure the diversity of kernel function, we adopt a hybrid kernel function formed by linear combination. In addition, RVM is easy to fall into the local optimum, and it has large initial value randomness and poor convergence. Aiming at the limitations of the RVM algorithm, we propose a novel WOA-HRVM model, which optimizes the parameters of the hybrid kernel RVM by WOA algorithm to obtain better performance. The proposed WOA-HRVM is evaluated on NSL-KDD and CICIDS2017 dataset. Compared with other algorithms tested, the proposed WOA-HRVM algorithm significantly improves the accuracy and speed of intrusion detection.
{"title":"A Novel Intrusion Detection Method Based on WOA Optimized Hybrid Kernel RVM","authors":"Pan Gao, Meng Yue, Zhi-jun Wu","doi":"10.1109/ICCCS52626.2021.9449199","DOIUrl":"https://doi.org/10.1109/ICCCS52626.2021.9449199","url":null,"abstract":"In recent years, various machine learning algorithms and intelligent optimization algorithms have emerged one after another, and are widely used in intrusion detection. As a highly sparse model, the relevance vector machine (RVM) is very suitable for intrusion detection scenarios with large scale data. The selection of the parameters of the intrusion detection model directly affects the performance of intrusion detection. Therefore, the selection and determination of parameters is a very critical point to obtain better detection performance. At the same time, the classification performance of RVM obviously depends on the kernel function. To ensure the diversity of kernel function, we adopt a hybrid kernel function formed by linear combination. In addition, RVM is easy to fall into the local optimum, and it has large initial value randomness and poor convergence. Aiming at the limitations of the RVM algorithm, we propose a novel WOA-HRVM model, which optimizes the parameters of the hybrid kernel RVM by WOA algorithm to obtain better performance. The proposed WOA-HRVM is evaluated on NSL-KDD and CICIDS2017 dataset. Compared with other algorithms tested, the proposed WOA-HRVM algorithm significantly improves the accuracy and speed of intrusion detection.","PeriodicalId":376290,"journal":{"name":"2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116870864","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}
Network Compression is a challenging task, but it is crucial for using the deeper network in the low-performance device. If the original training datasets could be obtained, the traditional network compression approaches are useful for training a compact deep model. This paper proposes a novel framework for knowledge distillation without original training datasets via Generative Adversarial Network(GAN). We arrange the fixed pre-trained deeper network and the compact network as the discriminators to generate the training dataset. We also use the deeper network and the compact network as the generators, then introduce one simple full connection network as the discriminator to compress the complex network. We propose (i) a series of new images generation loss functions. (ii) a knowledge distillation method via generating adversarial networks. Finally, we show the superiority of our approach by contrasting with SOTA by benchmark datasets.
{"title":"Data-free Knowledge Distillation via Adversarial*","authors":"Yu Jin, Zhaofan Qiu, GengQuan Xie, Jinye Cai, Chao Li, Liwen Shen","doi":"10.1109/ICCCS52626.2021.9449145","DOIUrl":"https://doi.org/10.1109/ICCCS52626.2021.9449145","url":null,"abstract":"Network Compression is a challenging task, but it is crucial for using the deeper network in the low-performance device. If the original training datasets could be obtained, the traditional network compression approaches are useful for training a compact deep model. This paper proposes a novel framework for knowledge distillation without original training datasets via Generative Adversarial Network(GAN). We arrange the fixed pre-trained deeper network and the compact network as the discriminators to generate the training dataset. We also use the deeper network and the compact network as the generators, then introduce one simple full connection network as the discriminator to compress the complex network. We propose (i) a series of new images generation loss functions. (ii) a knowledge distillation method via generating adversarial networks. Finally, we show the superiority of our approach by contrasting with SOTA by benchmark datasets.","PeriodicalId":376290,"journal":{"name":"2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128266610","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 : 2021-04-23DOI: 10.1109/ICCCS52626.2021.9449268
Majid Mumtaz, Ping Luo
RSA public key cryptosystem is the “de-facto” standard, provides confidentiality and privacy security services over the internet. At Eurocrypt 1999, Boneh and Durfee proposed a polynomial time attacks on RSA small decryption key exponent. Their attacks worked by exploiting the lattice and sub lattice structure using lattice based Coppersmith's method to solve a modular polynomials, when $d < N^{0.284}$ and $d < N^{0.292}$ respectively. In this work, we propose a new attack on some special case of Boneh and Durfee's attack method with respect to large decryption exponent (i.e. $d=N^{epsilon} > e=N^{alpha}$, where $alpha$ and $epsilon$ are the encryption and decryption exponents respectively) for some $alphaleqepsilon$. The condition $d > phi(N)-N^{epsilon}$ satisfies our devised attack and the experimental outcome certifies that an RSA cryptosystem with large decryption exponent successfully revealed the weak keys through lattice basis reduction method.
{"title":"Cryptanalysis of a Special Case of RSA Large Decryption Exponent Using Lattice Basis Reduction Method","authors":"Majid Mumtaz, Ping Luo","doi":"10.1109/ICCCS52626.2021.9449268","DOIUrl":"https://doi.org/10.1109/ICCCS52626.2021.9449268","url":null,"abstract":"RSA public key cryptosystem is the “de-facto” standard, provides confidentiality and privacy security services over the internet. At Eurocrypt 1999, Boneh and Durfee proposed a polynomial time attacks on RSA small decryption key exponent. Their attacks worked by exploiting the lattice and sub lattice structure using lattice based Coppersmith's method to solve a modular polynomials, when $d < N^{0.284}$ and $d < N^{0.292}$ respectively. In this work, we propose a new attack on some special case of Boneh and Durfee's attack method with respect to large decryption exponent (i.e. $d=N^{epsilon} > e=N^{alpha}$, where $alpha$ and $epsilon$ are the encryption and decryption exponents respectively) for some $alphaleqepsilon$. The condition $d > phi(N)-N^{epsilon}$ satisfies our devised attack and the experimental outcome certifies that an RSA cryptosystem with large decryption exponent successfully revealed the weak keys through lattice basis reduction method.","PeriodicalId":376290,"journal":{"name":"2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128358226","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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