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International Conference on Internet of Things: Systems, Management and security

作者： Duisen Gulsezim Seiitkaliyeva Zhansaya Abdul Razaque Yestayeva Ramina Fathi Amsaad Muder Almiani Raouf Ganda Ahmed Oun Computer Eng. And Security International Inf. Tech. Univ Almaty Kazakhstan Dept. of Computer Sci NYIT Nanjing China School of Information Security and Applied Computing Eastern Michigan University USA Computer Information Sys Al-Hussein Bin Talal University Jordan Yale School of Management Yale University USA EECS Department University of Toledo OH

Dependence of the individuals on the Internet for performing the several actions require secure data communication. Thus, the reliable data communication improves the confidentiality. As, enhanced security leads to reliable and faster communication. To improve the reliability and confidentiality, there is dire need of fully secured authentication method. There are several methods of password protections were introduced to protect the confidentiality and reliability. Most of the existing methods are based on alphanumeric approaches, but few methods provide the dual authentication process. In this paper, we introduce improved graphical password authentication using Twofish Encryption and Visual Cryptography (TEVC) method. Our proposed TEVC is unpredictably organized as predicting the correct graphical password and arranging its particles in the proper order is harder as compared to traditional alphanumeric password system. TEVC is tested by using JAVA platform. Based on the testing results, we confirm that proposed TEVC provides secure authentication. TEVC encryption algorithm detected as more prudent and possessing lower time complexity as compared to other known existing algorithms message code confirmation and fingerprint scan with password.

关键词： Conferences Internet security

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Accessing Multilingual information Repositories 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Carol Peters Fredric C. Gey Julio Gonzalo Henning Müller Gareth J. F. Jones Michael Kluck Bernardo Magnini Maarten Rijke

ISBN: (数字)9783540457008

ISBN: (纸本)9783540456971

The sixth campaign of the Cross Language Evaluation Forum (CLEF) for European languages was held from January to September 2005. CLEF is by now an established international evaluation initiative and 74 groups from all over the world submitted results for one or more of the different evaluation tracks in 2005, compared with 54 groups in 2004. There were eight distinct evaluation tracks, designed to test the performance of a wide range of systems for multilingual information access. Full details regarding the design of the tracks, the methodologies used for evaluation, and the results obtained by the participants can be found in the different sections of these proceedings. As always the results of the campaign were reported and discussed at the annual workshop held in Vienna, Austria, September 21-23, immediately following the ninth European Conference on Digital Libraries. The workshop was attended by approximately 110 academic and industrial researchers and system developers. In addition to presentations by participants in the campaign, Noriko Kando from the National Institute of Informatics, Tokyo, gave an invited talk on the activities of the NTCIR evaluation initiative for Asian languages. Breakout sessions gave participants a chance to discuss ideas and results in detail. The final session was dedicated to proposals for activities for CLEF 2006. The presentations given at the workshop can be found on the CLEF Web site at: www. clef-campaign. org. We should like to thank the other members of the CLEF Steering Committee for their assistance in the coordination of this event.

关键词： information Storage and Retrieval Artificial Intelligence information Systems Applications (incl. Internet) Natural Language Processing (NLP)

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Impossible meet-in-the-middle fault analysis on the LED lightweight cipher in VANETs

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Science China(information Sciences) 2018年第3期61卷 118-130页

作者： Wei LI Vincent RIJMEN Zhi TAO Qingju WANG Hua CHEN Yunwen LIU Chaoyun LI Ya LIU School of Computer Science and Technology Donghua University imec-COSIC KU Leuven Shanghai Key Laboratory of Integrate Administration Technologies for Information Security Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Applied Mathematics and Computer Science Technical University of Denmark Trusted Computing and Information Assurance Laboratory Institute of SoftwareChinese Academy of Sciences College of Science National University of Defense Technology Department of Computer Science and Engineering University of Shanghai for Science and Technology

With the expansion of wireless technology, vehicular ad-hoc networks(VANETs) are emerging as a promising approach for realizing smart cities and addressing many serious traffic problems, such as road safety, convenience, and efficiency. To avoid any possible rancorous attacks, employing lightweight ciphers is most effective for implementing encryption/decryption, message authentication, and digital signatures for the security of the VANETs. Light encryption device(LED) is a lightweight block cipher with two basic keysize variants: LED-64 and LED-128. Since its inception, many fault analysis techniques have focused on provoking faults in the last four rounds to derive the 64-bit and 128-bit secret keys. It is vital to investigate whether injecting faults into a prior round enables breakage of the LED. This study presents a novel impossible meet-in-the-middle fault analysis on a prior round. A detailed analysis of the expected number of faults is used to uniquely determine the secret key. It is based on the propagation of truncated differentials and is surprisingly reminiscent of the computation of the complexity of a rectangle attack. It shows that the impossible meet-in-the-middle fault analysis could successfully break the LED by fault injections.

关键词： VANETs LED lightweight cipher impossible meet-in-the-middle fault analysis

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$\textsf{LoPub}$ : High-Dimensional Crowdsourced Data Publication With Local Differential Privacy

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IEEE Transactions on information Forensics and security 2018年第9期13卷 2151-2166页

作者： Xuebin Ren Chia-Mu Yu Weiren Yu Shusen Yang Xinyu Yang Julie A. McCann Philip S. Yu School of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China Taiwan Information Security Center Taipei Taiwan Department of Computer Science and Engineering National Chung Hsing University Taichung Taiwan Department of Computing Imperial College London London U.K. School of Engineering and Applied Science Aston University Birmingham U.K. Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China Department of Computer Science University of Illinois at Chicago Chicago IL USA

High-dimensional crowdsourced data collected from numerous users produces rich knowledge about our society; however, it also brings unprecedented privacy threats to the participants. Local differential privacy (LDP), a variant of differential privacy, is recently proposed as a state-of-the-art privacy notion. Unfortunately, achieving LDP on high-dimensional crowdsourced data publication raises great challenges in terms of both computational efficiency and data utility. To this end, based on the expectation maximization (EM) algorithm and Lasso regression, we first propose efficient multi-dimensional joint distribution estimation algorithms with LDP. Then, we develop a local differentially private high-dimensional data publication algorithm (LoPub) by taking advantage of our distribution estimation techniques. In particular, correlations among multiple attributes are identified to reduce the dimensionality of crowdsourced data, thus speeding up the distribution learning process and achieving high data utility. Extensive experiments on real-world datasets demonstrate that our multivariate distribution estimation scheme significantly outperforms existing estimation schemes in terms of both communication overhead and estimation speed. Moreover, LoPub can keep, on average, 80% and 60% accuracy over the released datasets in terms of support vector machine and random forest classification, respectively.

关键词： Privacy Data privacy Servers Correlation Estimation Frequency estimation Sensors

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Detection of Phishing Websites using Machine Learning

Detection of Phishing Websites using Machine Learning

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IEEE Cloud Summit

作者： Abdul Razaque Mohamed Ben Haj Frej Dauren Sabyrov Aidana Shaikhyn Fathi Amsaad Ahmed Oun International IT University Almaty Kazakhstan University of Bridgeport Bridgeport CT USA Computer Engineering and Telecommunication International IT University Almaty Kazakhstan School of Information Security and Applied Computing (SISAC) Eastern Michigan University Ypsilanti MI USA University of Toledo Toledo OH US

ISBN: (数字)9781728182667

ISBN: (纸本)9781728182674

Phishing sends malicious links or attachments through emails that can perform various functions, including capturing the victim's login credentials or account information. These emails harm the victims, cause money loss, and identity theft. In this paper, we contribute to solving the phishing problem by developing an extension for the Google Chrome web browser. In the development of this feature, we used JavaScript PL. To be able to identify and prevent the fishing attack, a combination of Blacklisting and semantic analysis methods was used. Furthermore, a database for phishing sites is generated, and the text, links, images, and other data on-site are analyzed for pattern recognition. Finally, our proposed solution was tested and compared to existing approaches. The results validate that our proposed method is capable of handling the phishing issue substantially.

关键词： Uniform resource locators Social networking (online) Phishing Semantics Browsers Electronic mail Videos

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Privacy-Preserving Electricity Theft Detection based on Blockchain

arXiv

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arXiv 2023年

作者： Zhao, Zhiqiang Liu, Yining Zeng, Zhixin Chen, Zhixiong Zhou, Huiyu School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China The Fujian Key Laboratory of Financial Information Processing The Key Laboratory of Applied Mathematics Fujian Province University Putian University Fujian Putian351100 China The School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom

In most electricity theft detection schemes, consumers’ power consumption data is directly input into the detection center. Although it is valid in detecting the theft of consumers, the privacy of all consumers is at risk unless the detection center is assumed to be trusted. In fact, it is impractical. Moreover, existing schemes may result in some security problems, such as the collusion attack due to the presence of a trusted third party, and malicious data tampering caused by the system operator (SO) being attacked. Aiming at the problems above, we propose a blockchain-based privacy-preserving electricity theft detection scheme without a third party. Specifically, the proposed scheme uses an improved functional encryption scheme to enable electricity theft detection and load monitoring while preserving consumers’ privacy;distributed storage of consumers’ data with blockchain to resolve security problems such as data tampering, etc. Meanwhile, we build a long short-term memory network (LSTM) model to perform higher accuracy for electricity theft detection. The proposed scheme is evaluated in a real environment, and the results show that it is more accurate in electricity theft detection within acceptable communication and computational overhead. Our system analysis demonstrates that the proposed scheme can resist various security attacks and preserve consumers’ privacy. Copyright © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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Deep Generative Network for Image Inpainting with Gradient Semantics and Spatial-Smooth Attention

SSRN

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SSRN 2023年

作者： Sheng, Ziqi Xu, Wenbo Lin, Cong Lu, Wei School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China Applied Laboratory of Dig Data and Education Statistics School of Statistics and Mathematics Guangdong University of Finance and Economics Guangzhou510006 China

As a powerful means of tampering in image content security area, image inpainting based on deep generative networks can yield visually appealing outputs but often produces ambiguous artifacts, particularly in boundary and high semantic areas. To address this issue, a novel end-to-end network with gradient semantics and spatial-smooth attention (GS-SSA) is proposed, which combines a gradient learning network and an image inpainting network. The gradient learning network is meant to properly anticipate the gradient semantics in the hole region and get a complete gradient semantic map. The image inpainting network utilizes the complete gradient semantic map to better repair the missing pixels and obtain the final inpainting results. Moreover, spatial-smooth attention is introduced into the image inpainting network, considering the spatial structural relations and the surrounding information in the hole region respectively. © 2023, The Authors. All rights reserved.

关键词： Semantics

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CNNs in Crop Care: A Comparative Analysis of Tomato Disease Detection Models

CNNs in Crop Care: A Comparative Analysis of Tomato Disease ...

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Cyber Resilience (ICCR), International Conference on

作者： Ali M Ali Shadi Nashwan Ahmad Al-Qerem Amjad Aldweesh Mohammad Alauthman Zenab Elgamal Ammar Almomani Communications and Computer Engineering Department Faculty of Engineering AlAhliyya Amman University Amman Jordan Cybersecurity Department Faculty of Information Technology Middle East University (MEU) Amman Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan College of Computing and IT Shaqra University Shaqra Saudi Arabia Department of Information Security Faculty of Information Technology University of Petra Amman Jordan School of Computing Skyline University College University City of Sharjah Sharjah United Arab Emirates School of Computing Skyline University College Sharjah UAE I.T. department AlHuson University College AlBalqa Applied University Irbid Jordan

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Timely and accurate detection of plant diseases is critical for sustainable crop production. This paper presents a comparative analysis of convolutional neural network (CNN) models for automated tomato leaf disease detection, including VGG16, VGG19, AlexNet and Google Net. A dataset of tomato leaf images with multiple disease types was utilized to train and test the performance of each CNN architecture. Our results demonstrate that the VGG16 model achieved the highest accuracy of 99.38% in classifying tomato leaf diseases. Compared to AlexNet and VGG19 models which obtained 98.67% and 99.29% accuracy respectively, the superior performance of VGG16 highlights its suitability for deploying robust tomato disease detection systems to enable prompt disease control interventions. Our work provides valuable insights on harnessing deep learning for agricultural disease surveillance, allowing farmers and agronomists to identify crop infections early and implement timely management practices to reduce yield losses.

关键词： Deep learning Plant diseases Analytical models Visualization Surveillance Crops Training data

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Empowering Small and Medium Enterprises with Data Analytics for Enhanced Competitiveness

Empowering Small and Medium Enterprises with Data Analytics ...

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IEEE International Conference on Control System, computing and Engineering (ICCSCE)

作者： Aissa Mosbah Musab A. M. Ali Nooritawati Md Tahir College of Commerce and Business Administration Dhofar University Salalah Oman College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computer Engineering and Information Security International Information Technology University Almaty Kazakhstan School of Electrical Engineering College of Engineering Universiti Teknologi MARA Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Malaysia

In today’s competitive landscape, Small and Medium-sized Enterprises (SMEs) are increasingly embracing business data analytics to gain valuable insights and make informed decisions. However, the true essence of big data lies not only in its volume but also in the analysis process and the derived insights that aid managers in making effective business choices. Despite this, limited knowledge exists regarding SMEs’ utilization of data analytics and its potential in facilitating informed decision-making. Therefore, this paper aims to elucidate the concept of business data analytics within the context of SMEs and outline the essential requirements for its effective implementation, enabling SMEs to leverage data analytics for enhanced competitiveness. It is proposed that the foundation of a successful data analytics system for SMEs should encompass four key elements: data, people, technology, and process. The degree to which data analytics contributes to a firm’s competitiveness is largely influenced by four factors: data quality, well-defined objectives, the caliber of analytic tools and techniques, and analytical skills. Data generates information and insights, which in turn foster knowledge that ultimately leads to wisdom. However, due to limited resources, SMEs may be less inclined to engage in advanced predictive and prescriptive analytics compared to larger firms.

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Cyberbullying Detection Using Deep Learning: A Comparative Study

Cyberbullying Detection Using Deep Learning: A Comparative S...

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Cyber Resilience (ICCR), International Conference on

作者： Mouhammd Alkasassbeh Ammar Almomani Amjad Aldweesh Ahmad Al-Qerem Mohammad Alauthman Khalid Nahar Beenu Mago Computer Science Department Princess Sumaya University for Technology Amman Jordan School of Computing Skyline University College Sharjah UAE I.T. department AlHuson University College AlBalqa Applied University Irbid Jordan College of Computing and IT Shaqra University Shaqra Saudi Arabia Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Department of Information Security Faculty of Information Technology University of Petra Amman Jordan Computer Science Department Faculty of I.T Yarmouk University Irbid Jordan School of Computing Skyline University College University City of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Cyberbullying has become a serious issue with the rise in social media usage, especially among adolescents. Victims of cyberbullying often suffer from psychological distress, anxiety, depression and even suicidal tendencies. With the rapid growth of user-generated content online, manual detection of cyberbullying is extremely difficult. Hence, automated cyber-bullying detection systems are needed. In recent years, deep learning models have emerged as a promising technique for cyberbullying detection. This paper provides a comprehensive review of the use of deep learning for cyberbullying detection. The key contributions are: (1) a taxonomy of cyberbullying detection techniques, (2) a review of datasets used for model training and evaluation, (3) a comparative analysis of various deep learning models including CNN, RNN, LSTM, GRU and Transformer- based models, (4) a discussion of key challenges and limitations, and (5) suggestions for future research directions. The paper provides a critical perspective on model performance across multiple datasets. Key findings indicate RNNs and specifically LSTM and GRU models demonstrate state-of-the-art performance for sequential cyberbullying detection. The study concludes that deep ensemble models combining LSTMs and pretrained contextual embeddings show significant promise to advance the field.

关键词： Deep learning Training Analytical models Reviews User-generated content Taxonomy Cyberbullying

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