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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

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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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On developing deep learning models with particle swarm optimization in the presence of poisoning attacks

On developing deep learning models with particle swarm optim...

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information Technology (ACIT)

作者： Majdi Maabreh Omar Darwish Ola Karajeh Yahya Tashtoush Department of Computer Information Systems The Hashemite University Zarqa Jordan Information Security and Applied Computing Department Eastern Michigan University MI USA Department of Computer Science Virginia Polytechnic Institute and State University VA USA Department of Computer Science Jordan University of Science and Technology Irbid Jordan

ISBN: (数字)9798350320244

ISBN: (纸本)9798350320251

Deep learning (DL) has demonstrated several successes in a variety of fields, particularly in the era of big data. The process of training a deep learning model entails selecting the ideal learning parameters such as the number of hidden layers and neurons. Particle Swarm Optimization (PSO) is one useful nature-inspired algorithm to set those two influential learning parameters. In this study, two different datasets are used to study and evaluate the particle swarm optimizer with deep learning on datasets of different concentrations of poisoning attack, where some adversarial samples were crafted by attackers to ruin the learning process. The results showed that particle swarm optimization could find settings for deep learning with the existence of poisoned data that maximizes the model accuracy on the unseen testing dataset, and could also offer better recommendations compared to those recommended on all benign samples. This may introduce a concern that optimizers might conceal the existence of data poisoning, which may lead to unreliable learning in the advanced stages of upgrading the model on updated datasets.

关键词： Deep learning Training Neurons Big Data Data models Safety Particle swarm optimization

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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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Two Factor Authentication using Twofish Encryption and Visual Cryptography Algorithms for Secure Data Communication

Two Factor Authentication using Twofish Encryption and Visua...

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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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Optimizing Phishing URL Detection: A Comparative Analysis of Feature Counts and Training Times

Optimizing Phishing URL Detection: A Comparative Analysis of...

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Intelligent computing, Communication, Networking and Services (ICCNS), International Conference on

作者： Moayyad Alajlouni Yahya Tashtoush Saud Alhajaj Al. Dossari Omar Darwish Department of Computer Science Jordan University of Science and Technology Irbid Jordan Department of Electrical Engineering Prince Sattam bin Abdulaziz University Wadi Addwasir Saudi Arabia Information Security and Applied Computing Department Eastern Michigan University Ypsilanti MI USA

ISBN: (数字)9798350354690

ISBN: (纸本)9798350354706

Phishing attacks are the biggest cybersecurity threats in the digital world. Attackers exploit users by impersonating real, authentic web sites to obtain sensitive information such as passwords and bank statements. One common technique used in these attacks is malicious URLs. These malicious URLs mimic legitimate URLs, misleading users into interacting with malicious websites. This practice, URL phishing, presents a big threat to internet security, emphasizing the need for advanced detection methods. This paper presents a method for detecting malicious URLs using statistical features extracted from n-grams. These n-grams are extracted from the hexadecimal representation of URLs. We used Explainable AI (XAI) to explore the extracted features and evaluate their importance and role in phishing detection. A key advantage of our method is its ability to reduce the required features and the training time by using fewer features after applying XAI techniques. Our technique only uses statistical features extracted from n-grams and the n-gram itself, without requiring any high-level features. We applied XAI techniques, SHapley Additive exPlanations (SHAP), and Local Interpretable Model-agnostic Explanations (LIME). Through the explanation provided by XAI methods, we were able to determine the most important features in our feature set, enabling a reduction in feature count. Using fewer features (4, 7, 10, 13, 15), we got good accuracy compared to the 41 features and 87 features used in the original experiment and reduced the models' training times and complexity. This research aimed to enhance phishing URL detection by optimizing feature selection and analyzing the impact on training times. Our findings show the importance of using minimal statistical features to identify malicious URLs effectively and efficiently.

关键词： Training Uniform resource locators Accuracy Phishing Neural networks Internet security Passwords Feature extraction Computational efficiency Complexity theory

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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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The differential spectrum and boomerang spectrum of a class of locally-APN functions

arXiv

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arXiv 2022年

作者： Hu, Zhao Li, Nian Xu, Linjie Zeng, Xiangyong Tang, Xiaohu The Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan430062 China The WuHan Marine Communication Research Institute Wuhan430000 China The Information Security and National Computing Grid Laboratory Southwest Jiaotong University Chengdu610031 China

In this paper, we study the boomerang spectrum of the power mapping F(x) = xk(q−1) over Fq2, where q = pm, p is a prime, m is a positive integer and gcd(k, q + 1) = 1. We first determine the differential spectrum of F (x) and show that F (x) is locally-APN. This extends a result of [IEEE Trans. Inf. Theory 57(12):8127-8137, 2011] from (p, k) = (2, 1) to general (p, k). We then determine the boomerang spectrum of F (x) by making use of its differential spectrum, which shows that the boomerang uniformity of F (x) is 4 if p = 2 and m is odd and otherwise it is 2. Our results not only generalize the results in [Des. Codes Cryptogr. 89:2627-2636, 2021] and [Adv. Math. Commun., doi: 10.3934/amc.2022046] but also extend the example x45 over F28 in [Des. Codes Cryptogr. 89:2627-2636, 2021] into an infinite class of power mappings with boomerang uniformity *** Codes 12E20, 11T06, 94A60 Copyright © 2022, The Authors. All rights reserved.

关键词： Mapping

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A Classifier to Detect Stream Applications Based on Network Traffic Statistical Metrics

A Classifier to Detect Stream Applications Based on Network ...

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information Technology (ACIT)

作者： Omar Darwish Sarah Herzallah Majdi Maabreh Shouroq Al-Eidi Mahmoud Al-Maani Yahya Tashtoush Information Security and Applied Computing Eastern Michigan University Ypsilanti Michigan Electrical and Computer Engineering Eastern Michigan University Ypsilanti Michigan Department of Information Technology The Hashemite University Zarqa Jordan Computer Science Department Tafila Technical University Tafila Jordan Department of Computer Science Jordan University of Science and Technology Irbid Jordan

ISBN: (数字)9798350320244

ISBN: (纸本)9798350320251

One of the most crucial considerations, when considering security vulnerabilities, is network traffic. There is still potential for more research on the inter-arrival time side, even though some studies concentrate on network traffic from the perspective of the packet fields such as packet length and packet number. Inter-arrival timings are crucial to investigate because there are numerous attacks, such as Covert Timing Channels attacks, that heavily rely on them. In this article, we conduct a statistical analysis of the TCP inter-arrival times of two major key streaming programs (Zoom and Skype), which are frequently used, particularly during and following the COVID-19 pandemic. Using two internet-connected devices and the statistical measures of TCP, a dataset of 18,371 instances is created for this use. Five machine learning algorithms are evaluated on balanced and imbalanced forms of the dataset. The results revealed that the traffic of Zoom and Skype calls can be identified by machine learning algorithms with an accuracy of up to 99% by random forest.

关键词： Measurement COVID-19 Machine learning algorithms Statistical analysis Pandemics Telecommunication traffic Timing

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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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