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3rd IEEE Global Conference for Advancement in technology, GCAT 2022

作者： Borade, Shwetambari Kalbande, Dhananjay Jakaria, Hemangi Patil, Linesh Information Technology Cyber Security Thadomal Shahani Engineering College Shah & Anchor Kutchhi Engineering College Mumbai India Department of Computer Engineering Mumbai India Shah & Anchor Kutchhi Engineering College Department of Information Technology Mumbai India Shah & Anchor Kutchhi Engineering College Department of Computer Engineering Mumbai India

ISBN: (数字)9781665468558

ISBN: (纸本)9781665468534

Dermatological illnesses are the most serious problem in the twenty-first century, owing to a lack of awareness and the high cost of diagnosis. To address the issue, we believe that automated methods-based applications will be extremely beneficial in the early stages of diagnosis. As a result, in this research, we describe an automated approach for identifying the kind of cosmetic skin and cosmetic skin condition from photographs. Our model is created utilizing machine learning methods. The model consists of three phases: data gathering and data augmentation, features extraction, and prediction. © 2022 IEEE.

关键词： Machine learning

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Advancing Android Malware Detection with BioSentinel Neural Network using Hybrid Deep Learning Techniques

Advancing Android Malware Detection with BioSentinel Neural ...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： D Sandhya Rani K Gnaneshwar K Sampurnima Pattem Soma Sekhar G Bala Krishna Shirisha Kakarla Department of Computer Science Engineering CVR College of Engineering Ibrahimpatnam Rangareddy India Department of Computer Science Engineering-Cyber Security Sreenidhi Institute of Science and Technology Hyderabad India Department of Computer Science Engineering Vardhaman College of Engineering Hyderabad India

The paper introduces the BioSentinel Neural Network (BSNN), a novel hybrid deep learning model designed to enhance malware detection, particularly focusing on zero-day threats. The BSNN model integrates diverse neural network architectures, leveraging Graph Neural Networks (GNNs) for feature extraction and Transformers for sequential data analysis. It demonstrates significant improvements in accuracy (93.16%), precision (90.89%), recall (88.19%), and F1-score (90.45%) over traditional methods. The model exhibits a high detection rate of 88% for zero-day malware from 1000 samples tested and shows computational efficiency with an average inference time of 30 milliseconds. This research addresses critical gaps in current malware detection approaches, highlighting the potential of hybrid deep learning techniques in the cybersecurity domain.

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Information-Centric IoT-Based Smart Farming with Dynamic Data Optimization

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computers, Materials & Continua 2023年第2期74卷 3865-3880页

作者： Souvik Pal Hannah VijayKumar D.Akila N.Z.Jhanjhi Omar A.Darwish Fathi Amsaad Department of Computer Science and Engineering Sister Nivedita UniversityKolkataIndia Post Doctoral Researcher Sambalpur UniversitySambalpurIndia Department of Computer Science Anna Adarsh College for WomenChennaiIndia Department of Information Technology Vels Institute of ScienceTechnology and Advanced StudiesChennaiIndia School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya47500SelangorMalaysia FITTaylor’s UniversitySubang Jaya47500SelangorMalaysia Department of Information Security and Applied Computing Eastern Michigan UniversityUSA School of Information Security and Applied Computing Eastern Michigan UniversityUSA

Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies,including big data,the cloud,and the Internet of Things(IoT).Many researchers try to integrate IoT-based smart farming on cloud platforms *** define various frameworks on smart farming and monitoring system and still lacks to define effective data management *** IoT-cloud systems involve massive structured and unstructured data,data optimization comes into the ***,this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization(ICISF-DDO),which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved ***,a conceptual framework of the proposed scheme and statistical design model has beenwell *** information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data *** simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%,reliability ratio of 98.63%,a coverage ratio of 99.67%,least sensor error rate of 8.96%,and efficient energy consumption ratio of 4.84%.

关键词： Smart farming data optimization information management internet of things cloud

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Optimization of Ethanol Production Using State-Space Modeling and Optimal Control technology 13

Optimization of Ethanol Production Using State-Space Modelin...

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13th International Conference on Dependable Systems, Services and Technologies, DESSERT 2023

作者： Markina, Lyudmila Palchevskyi, Bogdan Hrudetskyi, Roman Smoliankin, Oleh Melnychuk, Yuliia Khrystynets, Nataliya Lutsk National Technical University Department of Automation and Computer-Integrated Technologies Lutsk Ukraine Lutsk National Technical University Department of Digital Educational Technologies Lutsk Ukraine Lutsk National Technical University Department of Computer Engineering and Cyber Security Lutsk Ukraine

ISBN: (纸本)9798350396119

in present paper we develop the automatic optimal control of the production process of dough mixture preparation, that can be used in the production of alcohol and fuel ethanol from grain raw materials. Known methods of automatic control of the dough preparation process are based on measuring a consumption of raw materials and a concentration of dough and regulating the water supply. We offer an improved method based on robust stabilization nonlinear process control that will ensure more stable operation of the system, smooth transitions of regimes and economy of used energy, improvement of a quality of the mixture dough, as well as final ethanol product. The optimal operating modes of experimental installation were investigated using state-space dynamical modelling technic and ethanol production process simulation. © 2023 IEEE.

关键词： Water supply

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Text Summarization Generation Based on Improved Transformer Model

Text Summarization Generation Based on Improved Transformer ...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on cyber Science and technology Congress, DASC/PiCom/CBDCom/cyberSciTech 2023

作者： Lin, Jingsong Guo, Xiaodong Dong, Chen Lyu, Chenxi Xu, Li Chen, Zhenyi College of Computer and Data Science Fuzhou University Fuzhou350116 China College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China University of South Florida Department of Computer Science and Engineering TampaFL33620 United States

ISBN: (纸本)9798350304602

In the era of big data, the number of internet users is increasing yearly, and each user receives a massive amount of information every day. The low-value density of massive text data makes it difficult to utilize its information. In the face of this problem, text summarization generation technology that can extract the paper's leading content and condense the paper's main idea is a powerful solution. In text summarization generation, abstractive summarization techniques with high readability, clear expression, and closer proximity to human language habits are often favored. However, mainstream technologies still have issues like summarizations' detachment from focus. In this paper, keyword information is employed to join the text summarization generation task, and a keyword information module is added to the Transformer to better perform the summarization generation task. Finally, the ROUGE evaluation standard is used to compare the performance of the above models on the large-scale Chinese short text summarization (LCSTS) dataset. The experimental results show that the model generates higher-quality summarizations using keyword information, and the related methods have considerable potential. Future work can explore better model structures, more suitable methods for utilizing keyword information, and more accurate keyword extraction techniques to improve the quality of abstractive summarizations. © 2023 IEEE.

关键词： Large dataset

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Intelligent Malware Detection Using Sentence Transformer and Hybrid Deep Learning Architecture 10

Intelligent Malware Detection Using Sentence Transformer and...

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10th International Conference on Signal Processing and Communication, ICSC 2025

作者： Kumar, Sanjeev Sapru, Yudhishthira Sapru, Sugandha Janet, B. Centre for Development of Advanced Computing Cyber Security Technology Division Mohali India Indian Computer Emergency Response Team CERT-In Delhi India Pggc Panjab University Department of Mathematics Chandigarh India National Institute of Technology Department of Computer Application Tiruchirappalli India

ISBN: (纸本)9798331511890

The complex evasion techniques adopted by the malware creator make signature-based malware detection approaches unable to detect unknown malware attacks. This study proposes a new malware detection and classification approach with significantly less dependence on manual feature engineering. The bidirectional encoder representations from transformers (BERT) are used to model the opcode sequences of the malware file as the semantic behavior of malware. After that, the vectorized feature vector is fed to the hybrid one-dimensional convolution neural network (1D-CNN) and the gated recurrent unit (GRU) model for feature reduction and normalization before training a Softmax classifier. A hybrid learning architecture is designed with a custom 1D-CNN and a GRU model to take the strengths of both types of networks. The proposed scheme is evaluated using the Microsoft BIG dataset, consisting of 1625 assembly files belonging to 9 distinct malware families. The proposed method produced 95.77% accuracy even with the smaller dataset, which we believe will boost performance by adding a more diverse dataset. It uses the next-generation BERT language model, which gives a promising future area for malware defenders. This research presents malware detection without much reliance on manual feature engineering or signature-based detection, making it suitable for detecting unknown malware attacks. © 2025 IEEE.

关键词： cybersecurity Deep learning Malware detection Natural language processing Sentence transformer

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DarkSAM: Fooling Segment Anything Model to Segment Nothing 38

DarkSAM: Fooling Segment Anything Model to Segment Nothing

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Ziqi Song, Yufei Li, Minghui Hu, Shengshan Wang, Xianlong Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM. © 2024 Neural information processing systems foundation. All rights reserved.

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Optical Ciphering Scheme for Cancellable Speaker Identification System

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computer Systems Science & engineering 2023年第4期45卷 563-578页

作者： Walid El-Shafai Marwa A.Elsayed Mohsen A.Rashwan Moawad I.Dessouky Adel S.El-Fishawy Naglaa F.Soliman Amel A.Alhussan Fathi EAbd El-Samie Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Communications Faculty of EngineeringCairo UniversityCairoEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Most current security and authentication systems are based on personal *** security problem is a major issue in the field of biometric *** is due to the use in databases of the original *** biometrics will forever be lost if these databases are *** privacy is the most important goal of cancelable *** order to protect privacy,therefore,cancelable biometrics should be non-invertible in such a way that no information can be inverted from the cancelable biometric templates stored in personal identification/verification *** methodology to achieve non-invertibility is the employment of non-invertible *** work suggests an encryption process for cancellable speaker identification using a hybrid encryption *** system includes the 3D Jigsaw transforms and Fractional Fourier Transform(FrFT).The proposed scheme is compared with the optical Double Random Phase Encoding(DRPE)encryption *** evaluation of simulation results of cancellable biometrics shows that the algorithm proposed is secure,authoritative,and *** encryption and cancelability effects are good and reveal good ***,it introduces recommended security and robustness levels for its utilization for achieving efficient cancellable biometrics systems.

关键词： Cancellable biometrics jigsaw transform FrFT DRPE speaker identification

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An Autoencoder-Like Nonnegative Matrix Co-Factorization for Improved Student Cognitive Modeling 38

An Autoencoder-Like Nonnegative Matrix Co-Factorization for ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yu, Shenbao Pan, Yinghui Zeng, Yifeng Doshi, Prashant Liu, Guoquan Poh, Kim-Leng Lin, Mingwei College of Computer and Cyber Security Fujian Normal University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer and Information Sciences Northumbria University United Kingdom Intelligent Thought and Action Lab School of Computing University of Georgia United States Financial Technology Research Institute Fudan University China College of Design and Engineering National University of Singapore Singapore

Student cognitive modeling (SCM) is a fundamental task in intelligent education, with applications ranging from personalized learning to educational resource allocation. By exploiting students' response logs, SCM aims to predict their exercise performance as well as estimate knowledge proficiency in a subject. Data mining approaches such as matrix factorization can obtain high accuracy in predicting student performance on exercises, but the knowledge proficiency is unknown or poorly estimated. The situation is further exacerbated if only sparse interactions exist between exercises and students (or knowledge concepts). To solve this dilemma, we root monotonicity (a fundamental psychometric theory on educational assessments) in a co-factorization framework and present an autoencoder-like nonnegative matrix co-factorization (AE-NMCF), which improves the accuracy of estimating the student's knowledge proficiency via an encoder-decoder learning pipeline. The resulting estimation problem is nonconvex with nonnegative constraints. We introduce a projected gradient method based on block coordinate descent with Lipschitz constants and guarantee the method's theoretical convergence. Experiments on several real-world data sets demonstrate the efficacy of our approach in terms of both performance prediction accuracy and knowledge estimation ability, when compared with existing student cognitive models. © 2024 Neural information processing systems foundation. All rights reserved.

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GAN-Loc: Empowering Indoor Localization for Unknown Areas via Generative Fingerprint Map 21

GAN-Loc: Empowering Indoor Localization for Unknown Areas vi...

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21st Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2024

作者： Yoon, JinYi You, Yeawon Kang, Dayeon Kim, Jeewoon Lee, HyungJune Department of Computer Science Virginia Tech BlacksburgVA United States Department of Cyber Security Ewha Womans University Seoul Korea Republic of Manning College of Information & Computer Sciences University of Massachusetts AmherstMA United States Department of Computer Science University of California IrvineCA United States Department of Computer Science and Engineering Ewha Womans University Seoul Korea Republic of

ISBN: (纸本)9798331519186

As indoor localization becomes a necessity to provide intelligent location-based services for in-building users, fingerprint-based positioning has been widely adopted in numerous Wi-Fi-equipped devices. However, its reliance on extensive offline site surveys limits its application in unexplored environments without prior fingerprint sampling. To address the challenge, we propose GAN-Loc, a novel generative fingerprint-based framework. We extract the underlying correlation between location and radio signal features, empowering indoor localization in un/under-explored areas, including unknown data points, newly deployed APs, or unexplored sites. It involves: 1) decomposing into a signal feature map for each AP perspective;2) learning with a set of points and their associated signal strength data;3) generating and integrating synthetic radio fingerprints;and 4) employing them into some existing localization algorithms. We evaluated GAN-Loc with extensive real-world RSSI measurements in seven different indoor places. GAN-Loc achieves the localization accuracy of up to 1.21m, 1.29m, and 1.28m for Wi-Fi, Zigbee, and BLE, respectively, compared to the accuracy of 1.47m, 1.58m, and 1.22m using an ideal ground-truth map, which is unachievable without site survey in unknown sites. © 2024 IEEE.

关键词： Gallium nitride

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