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SIMAD:Secure Intelligent Method for IoT-Fog Environments Attacks Detection

Computers, Materials & Continua 2022年第2期70卷 2727-2742页

作者： Wided Ben Daoud Sami Mahfoudhi NTS’Com Research Unit SfaxTunisia Department of MIS CBEQassim UniversityBuraidahAl QassimKSA

The Internet of Thing IoT paradigm has emerged in numerous domains and it has achieved an exponential progress.Nevertheless,alongside this advancement,IoT networks are facing an ever-increasing rate of security risks because of the continuous and rapid changes in network environments.In order to overcome these security challenges,the Fog system has delivered a powerful environment that provides additional resources for a more improved data security.However,because of the emerging of various breaches,several attacks are ceaselessly emerging in IoT and Fog environment.Consequently,the new emerging applications in IoT-Fog environment still require novel,distributed,and intelligent security models,controls,and decisions.In addition,the ever-evolving hacking techniques and methods and the expanded risks surfaces have demonstrated the importance of attacks detection systems.This proves that even advanced solutions face difficulties in discovering and recognizing these small variations of attacks.In fact,to address the above problems,Artificial Intelligence(AI)methods could be applied on the millions of terabytes of collected information to enhance and optimize the processes of IoT and Fog systems.In this respect,this research is designed to adopt a new security scheme supported by an advanced machine learning algorithm to ensure an intelligent distributed attacks detection and a monitoring process that detects malicious attacks and updates threats signature databases in IoTFog environments.We evaluated the performance of our distributed approach with the application of certain machine learningmechanisms.The experiments show that the proposed scheme,applied with the Random Forest(RF)is more efficient and provides better accuracy(99.50%),better scalability,and lower false alert rates.In this regard,the distribution character of our method brings about faster detection and better learning.

关键词： Attack detection Fog IoT network machine learning distributed mechanism

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Health Data Deduplication Using Window Chunking-Signature Encryption in Cloud

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Intelligent Automation & Soft Computing 2023年第4期36卷 1079-1093页

作者： G.Neelamegam P.Marikkannu Department of Computer Science and Engineering Anna University Regional Campus CoimbatoreCoimbatore641046India

Due to the development of technology in medicine,millions of health-related data such as scanning the images are generated.It is a great challenge to store the data and handle a massive volume of data.Healthcare data is stored in the cloud-Fog storage environments.This cloud-Fog based health model allows the users to get health-related data from different sources,and duplicated informa-tion is also available in the background.Therefore,it requires an additional sto-rage area,increase in data acquisition time,and insecure data replication in the environment.This paper is proposed to eliminate the de-duplication data using a window size chunking algorithm with a biased sampling-based bloomﬁlter and provide the health data security using the Advanced Signature-Based Encryp-tion(ASE)algorithm in the Fog-Cloud Environment(WCA-BF+ASE).This WCA-BF+ASE eliminates the duplicate copy of the data and minimizes its sto-rage space and maintenance cost.The data is also stored in an efﬁcient and in a highly secured manner.The security level in the cloud storage environment Win-dows Chunking Algorithm(WSCA)has got 86.5%,two thresholds two divisors(TTTD)80%,Ordinal in Python(ORD)84.4%,Boom Filter(BF)82%,and the proposed work has got better security storage of 97%.And also,after applying the de-duplication process,the proposed method WCA-BF+ASE has required only less storage space for variousﬁle sizes of 10 KB for 200,400 MB has taken only 22 KB,and 600 MB has required 35 KB,800 MB has consumed only 38 KB,1000 MB has taken 40 KB of storage spaces.

关键词： Health data encryption chunks cloud Fog deduplication bloomﬁlter Algorithm 3:Generation of Key

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Enhanced fat degradation following the addition of a Pseudomonas species to a bioaugmentation product used in grease traps

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Journal of Environmental Sciences 2019年第3期31卷 174-188页

作者： Markella Tzirita Seraphim Papanikolaou Bríd Quilty School of Biotechnology Dublin City University (DCU) Department of Food Science and Human Nutrition Agricultural University of Athens National Institute for Cellular Biotechnology (NICB) DCU

The presence of fats, oils and greases(Fogs) in wastewater can lead to many problems including blockages. Investigation of a bioaugmentation product, consisting of Bacillus spp., to degrade butter(1%, W/V) and olive oil(1%, V/V) was performed in aerobic batch cultures for 13-day incubation. Gravimetric analysis of the remaining substrates showed slowly degradation of the oil after a 2-day lag, but no degradation of the butter. Addition of a Pseudomonas putida strain CP1 to the Bacillus spp. population promoted rapidly degradation of both fats after7 days of incubation. High fat accumulation revealed the potential use of the new bacterial mixture for production of added-value compounds. Lipase production only by the Bacillus spp.along with the analysis of the remaining lipids with thin layer chromatography and gas chromatography, suggested that the Bacillus spp. mainly only hydrolyzed the fat. The breakdown products were metabolized by the Pseudomonas putida CP1 performing preferential utilization of unsaturated fatty acids. Investigation of population dynamics using selective plating and a labeled Pseudomonas putida CP1::Tn7-gfp showed domination of the latter. The new mixture performed a successful cooperation with good potential for Fog treatment and an aggregative response desirable to fat degradation in grease traps.

关键词： Bacillus Bioaugmentation Biodegradation Fog Lipase Pseudomonas

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A Novel Transparent and Auditable Fog-Assisted Cloud Storage with Compensation Mechanism

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Tsinghua Science and Technology 2020年第1期25卷 28-43页

作者： Donghyun Kim Junggab Son Daehee Seo Yeojin Kim Hyobin Kim Jung Taek Seo Department of Computer Science Kennesaw State UniversityMariettaGA 30060USA Department of Information Security Engineering Soonchunhyang UniversityAsan 31538South Korea

This paper introduces a new Fog-assisted cloud storage which can achieve much higher throughput compared to the traditional cloud-only storage architecture by reducing the traffics toward the cloud storage. The Fog-storage service providers are transparency to end-users and therefore, no modification on the end-user devices is necessary. This new system is featured with(1) a stronger audit scheme which is naturally coupled with the proposed architecture and does not suffer from the replay attack and(2) a transparent and efficient compensation mechanism for the Fog-storage service providers. We provide rigorous theoretical analysis on the correctness and soundness of the proposed system. To the best of our knowledge, this is the first paper to discuss about a storage data audit scheme for Fog-assisted cloud storage as well as the compensation mechanism for the service providers of the Fog-storage service providers.

关键词： Fog computing cloud computing network storage data audit merkle hash tree integer factorization payment transparency

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Urban climate in the Tokyo metropolitan area in Japan

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Journal of Environmental Sciences 2017年第9期29卷 54-62页

作者： Jun Matsumoto Fumiaki Fujibe Hideo Takahashi Research Center for Climatology Department of Geography Tokyo Metropolitan University Department of Coupled Ocean-Atmosphere-Land Processes Research JAMSTEC

Long-term climate changes related with urbanization in Tokyo, Japan, and recent temperature and heavy rainfall distribution in the Tokyo metropolitan area are reviewed. A relatively high temperature increase in annual mean temperature at the rate of 3.0°C/century was detected in Tokyo for the period 1901–2015. Some observational evidence showed the existence of both thermal and mechanical effects of urbanization on recent heavy rainfall occurrences, and modeling studies also support precipitation enhancement. Urban influences were recognized in other climatological elements, such as number of Fog days, relative humidity, and wind circulation.

关键词： Heat island Cool island Heavy rainfall Fog Humidity Sea breeze

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高效可验证的雾辅助私有集合交集计算（英文）

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Frontiers of Information Technology & Electronic Engineering 2018年第12期19卷 1558-1568页

作者： Qiang WANG Fu-cai ZHOU Tie-min MA Zi-feng XU Software College Northeastern University School of Computer Science and Engineering Northeastern University

私有集合交集计算允许两方实体在不泄露除交集结果以外其他信息的前提下计算出两方实体的集合交集。随着雾计算的发展,将集合交集外包至雾的需求应运而生。然而,目前私有集合交集计算都是基于全同态加密和配对操作,所需代价较高且不支... 详细信息

私有集合交集计算允许两方实体在不泄露除交集结果以外其他信息的前提下计算出两方实体的集合交集。随着雾计算的发展,将集合交集外包至雾的需求应运而生。然而,目前私有集合交集计算都是基于全同态加密和配对操作,所需代价较高且不支持移动,难以在雾计算中应用。提出一种高效可验证的雾辅助私有集合交集计算方案。在该方案中,实体将私有集合交集计算外包至雾,雾在没有解密能力的前提下计算集合交集。该方案不依赖全同态加密和配对操作,极大提高了计算效率。此外,构建并证明了该方案的安全性。最后,对比分析本方案与其他方案的通信复杂度和计算复杂度。分析结果表明,该方案更高效,更具现实意义。

关键词： Private set intersection Fog computing Verifiable Data privacy

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SIoTFog:具备拜占庭容错机制的物联网雾计算网络（英文）

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Frontiers of Information Technology & Electronic Engineering 2018年第12期19卷 1546-1557页

作者： Jian-wen XU Kaoru OTA Mian-xiong DONG An-feng LIU Qiang LI Department of Information and Electronic Engineering Muroran Institute of Technology School of Information Science and Engineering Central South University MOE Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University

物联网技术爆炸式发展,从可穿戴设备到车辆互联,再到智慧城市等方面,正在改变人们的日常生活。过去,人们习惯将雾计算看作云计算的延伸;事实上,雾计算逐渐成为传输、处理分布式大数据的理想解决办法。提出一种考虑拜占庭容错的组网方法... 详细信息

物联网技术爆炸式发展,从可穿戴设备到车辆互联,再到智慧城市等方面,正在改变人们的日常生活。过去,人们习惯将雾计算看作云计算的延伸;事实上,雾计算逐渐成为传输、处理分布式大数据的理想解决办法。提出一种考虑拜占庭容错的组网方法,以及两种针对物联网雾计算的资源分配算法。目的是设计一个称作"SIoTFog"的安全雾计算网络,能够抵御拜占庭错误影响并提高传输、处理物联网大数据的效率。考虑两种情况,即面对单一拜占庭错误和多拜占庭错误的不同隐患时,比较算法性能。选择时延、传输时总转发跳数和设备利用率作为性能指标。仿真结果表明,该方法可助力实现高效、可靠的雾计算网络。

关键词： Byzantine fault tolerance Fog computing Resource allocation Intemet of Things (IoT)

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Energy-Efficient Computation Offloading and Resource Allocation in Fog Computing for Internet of Everything

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China Communications 2019年第3期16卷 32-41页

作者： Qiuping Li Junhui Zhao Yi Gong Qingmiao Zhang School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China School of Information Engineering East China Jiaotong UniversityNanchang 330013China Shenzhen Engineering Laboratory of Intelligent Information Processing for IoT Southern University of Science and TechnologyShenzhen 518055China

With the dawning of the Internet of Everything(IoE) era, more and more novel applications are being deployed. However, resource constrained devices cannot fulfill the resource-requirements of these applications. This paper investigates the computation offloading problem of the coexistence and synergy between Fog computing and cloud computing in IoE by jointly optimizing the offloading decisions, the allocation of computation resource and transmit power. Specifically, we propose an energy-efficient computation offloading and resource allocation(ECORA) scheme to minimize the system cost. The simulation results verify the proposed scheme can effectively decrease the system cost by up to 50% compared with the existing schemes, especially for the scenario that the computation resource of Fog computing is relatively small or the number of devices increases.

关键词： Fog computing cloud computing resource allocation computation offloading IoE

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Deep Learning Based Channel Estimation in Fog Radio Access Networks

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China Communications 2019年第11期16卷 16-28页

作者： Zhendong Mao Shi Yan State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

As a promising paradigm of the fifth generation networks,Fog radio access network(F-RAN)has attracted lots of attention nowadays.To fully utilize the promising gain of F-RANs,the acquisition of accurate channel state information is significant.However,conventional channel estimation approaches are not suitable in F-RANs due to the large training and feedback overhead.In this paper,we consider the channel estimation in F-RANs with Fog access point(F-AP)equipped with massive antennas.Thanks to the computing ability of F-AP and the sparsity of channel matrices in angular domain,Gated Recurrent Unit(GRU),a data-driven based channel estimation is proposed at F-AP to reduce the training and feedback overhead.The GRU-based method can capture the hidden sparsity structure automatically through the network training.Moreover,to further improve the channel estimation,a bidirectional GRU based method is proposed,whose target channel structure is decided by previous and subsequent structures.We compare the performance of our proposed channel estimation with traditional methods(Orthogonal Matching Pursuit(OMP)and Simultaneous OMP(SOMP)).Simulation results show that the proposed approaches have better performance compared with the traditional OMP and SOMP methods.

关键词： Fog radio access network(F-RAN) massive MIMO compressive sensing deep learning gated recurrent unit(GRU)

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Joint Design of Coalition Formation and Semi-Blind Channel Estimation in Fog Radio Access Networks

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China Communications 2019年第11期16卷 1-15页

作者： Zhifeng Wang Feifan Yang Shi Yan Saleemullah Memon Zhongyuan Zhao Chunjing Hu State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Coordinated signal processing can obtain a huge transmission gain for Fog Radio Access Networks(F-RANs).However,integrating into large scale,it will lead to high computation complexity in channel estimation and spectral efficiency loss in transmission performance.Thus,a joint cluster formation and channel estimation scheme is proposed in this paper.Considering research remote radio heads(RRHs)centred serving scheme,a coalition game is formulated in order to maximize the spectral efficiency of cooperative RRHs under the conditions of balancing the data rate and the cost of channel estimation.As the cost influences to the necessary consumption of training length and estimation error.Particularly,an iterative semi-blind channel estimation and symbol detection approach is designed by expectation maximization algorithm,where the channel estimation process is initialized by subspace method with lower pilot length.Finally,the simulation results show that a stable cluster formation is established by our proposed coalition game method and it outperforms compared with full coordinated schemes.

关键词： channel estimation cluster formation game theory Fog radio access networks(F-RANs)

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